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Merge branch 'main' into pr-1810

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# Conflicts:
#	nanobot/agent/memory.py
#	tests/test_memory_consolidation_types.py
This commit is contained in:
Xubin Ren
2026-03-13 03:29:16 +00:00
parent b24d6ffc94 62a2e71748
commit 60c29702cc
56 changed files with 4477 additions and 958 deletions
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4
.gitignore vendored
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@@ -1,5 +1,6 @@
.worktrees/
.assets
.docs
.env
*.pyc
dist/
@@ -7,7 +8,7 @@ build/
docs/
*.egg-info/
*.egg
*.pyc
*.pycs
*.pyo
*.pyd
*.pyw
@@ -20,4 +21,5 @@ __pycache__/
poetry.lock
.pytest_cache/
botpy.log
nano.*.save

103
README.md
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@@ -64,7 +64,7 @@
## Key Features of nanobot:
🪶 **Ultra-Lightweight**: Just ~4,000 lines of core agent code — 99% smaller than Clawdbot.
🪶 **Ultra-Lightweight**: A super lightweight implementation of OpenClaw — 99% smaller, significantly faster.
🔬 **Research-Ready**: Clean, readable code that's easy to understand, modify, and extend for research.
@@ -78,6 +78,25 @@
<img src="nanobot_arch.png" alt="nanobot architecture" width="800">
</p>
## Table of Contents
- [News](#-news)
- [Key Features](#key-features-of-nanobot)
- [Architecture](#️-architecture)
- [Features](#-features)
- [Install](#-install)
- [Quick Start](#-quick-start)
- [Chat Apps](#-chat-apps)
- [Agent Social Network](#-agent-social-network)
- [Configuration](#️-configuration)
- [Multiple Instances](#-multiple-instances)
- [CLI Reference](#-cli-reference)
- [Docker](#-docker)
- [Linux Service](#-linux-service)
- [Project Structure](#-project-structure)
- [Contribute & Roadmap](#-contribute--roadmap)
- [Star History](#-star-history)
## ✨ Features
<table align="center">
@@ -208,6 +227,7 @@ Connect nanobot to your favorite chat platform.
| **Slack** | Bot token + App-Level token |
| **Email** | IMAP/SMTP credentials |
| **QQ** | App ID + App Secret |
| **Wecom** | Bot ID + Bot Secret |
<details>
<summary><b>Telegram</b> (Recommended)</summary>
@@ -482,7 +502,8 @@ Uses **WebSocket** long connection — no public IP required.
"appSecret": "xxx",
"encryptKey": "",
"verificationToken": "",
"allowFrom": ["ou_YOUR_OPEN_ID"]
"allowFrom": ["ou_YOUR_OPEN_ID"],
"groupPolicy": "mention"
}
}
}
@@ -490,6 +511,7 @@ Uses **WebSocket** long connection — no public IP required.
> `encryptKey` and `verificationToken` are optional for Long Connection mode.
> `allowFrom`: Add your open_id (find it in nanobot logs when you message the bot). Use `["*"]` to allow all users.
> `groupPolicy`: `"mention"` (default — respond only when @mentioned), `"open"` (respond to all group messages). Private chats always respond.
**3. Run**
@@ -677,6 +699,46 @@ nanobot gateway
</details>
<details>
<summary><b>Wecom (企业微信)</b></summary>
> Here we use [wecom-aibot-sdk-python](https://github.com/chengyongru/wecom_aibot_sdk) (community Python version of the official [@wecom/aibot-node-sdk](https://www.npmjs.com/package/@wecom/aibot-node-sdk)).
>
> Uses **WebSocket** long connection — no public IP required.
**1. Install the optional dependency**
```bash
pip install nanobot-ai[wecom]
```
**2. Create a WeCom AI Bot**
Go to the WeCom admin console → Intelligent Robot → Create Robot → select **API mode** with **long connection**. Copy the Bot ID and Secret.
**3. Configure**
```json
{
"channels": {
"wecom": {
"enabled": true,
"botId": "your_bot_id",
"secret": "your_bot_secret",
"allowFrom": ["your_id"]
}
}
}
```
**4. Run**
```bash
nanobot gateway
```
</details>
## 🌐 Agent Social Network
🐈 nanobot is capable of linking to the agent social network (agent community). **Just send one message and your nanobot joins automatically!**
@@ -696,15 +758,17 @@ Config file: `~/.nanobot/config.json`
> [!TIP]
> - **Groq** provides free voice transcription via Whisper. If configured, Telegram voice messages will be automatically transcribed.
> - **VolcEngine / BytePlus Coding Plan**: Use dedicated providers `volcengineCodingPlan` or `byteplusCodingPlan` instead of the pay-per-use `volcengine` / `byteplus` providers.
> - **Zhipu Coding Plan**: If you're on Zhipu's coding plan, set `"apiBase": "https://open.bigmodel.cn/api/coding/paas/v4"` in your zhipu provider config.
> - **MiniMax (Mainland China)**: If your API key is from MiniMax's mainland China platform (minimaxi.com), set `"apiBase": "https://api.minimaxi.com/v1"` in your minimax provider config.
> - **VolcEngine Coding Plan**: If you're on VolcEngine's coding plan, set `"apiBase": "https://ark.cn-beijing.volces.com/api/coding/v3"` in your volcengine provider config.
> - **Alibaba Cloud Coding Plan**: If you're on the Alibaba Cloud Coding Plan (BaiLian), set `"apiBase": "https://coding.dashscope.aliyuncs.com/v1"` in your dashscope provider config.
| Provider | Purpose | Get API Key |
|----------|---------|-------------|
| `custom` | Any OpenAI-compatible endpoint (direct, no LiteLLM) | — |
| `openrouter` | LLM (recommended, access to all models) | [openrouter.ai](https://openrouter.ai) |
| `volcengine` | LLM (VolcEngine, pay-per-use) | [Coding Plan](https://www.volcengine.com/activity/codingplan?utm_campaign=nanobot&utm_content=nanobot&utm_medium=devrel&utm_source=OWO&utm_term=nanobot) · [volcengine.com](https://www.volcengine.com) |
| `byteplus` | LLM (VolcEngine international, pay-per-use) | [Coding Plan](https://www.byteplus.com/en/activity/codingplan?utm_campaign=nanobot&utm_content=nanobot&utm_medium=devrel&utm_source=OWO&utm_term=nanobot) · [byteplus.com](https://www.byteplus.com) |
| `anthropic` | LLM (Claude direct) | [console.anthropic.com](https://console.anthropic.com) |
| `azure_openai` | LLM (Azure OpenAI) | [portal.azure.com](https://portal.azure.com) |
| `openai` | LLM (GPT direct) | [platform.openai.com](https://platform.openai.com) |
@@ -714,10 +778,10 @@ Config file: `~/.nanobot/config.json`
| `minimax` | LLM (MiniMax direct) | [platform.minimaxi.com](https://platform.minimaxi.com) |
| `aihubmix` | LLM (API gateway, access to all models) | [aihubmix.com](https://aihubmix.com) |
| `siliconflow` | LLM (SiliconFlow/硅基流动) | [siliconflow.cn](https://siliconflow.cn) |
| `volcengine` | LLM (VolcEngine/火山引擎) | [volcengine.com](https://www.volcengine.com) |
| `dashscope` | LLM (Qwen) | [dashscope.console.aliyun.com](https://dashscope.console.aliyun.com) |
| `moonshot` | LLM (Moonshot/Kimi) | [platform.moonshot.cn](https://platform.moonshot.cn) |
| `zhipu` | LLM (Zhipu GLM) | [open.bigmodel.cn](https://open.bigmodel.cn) |
| `ollama` | LLM (local, Ollama) | — |
| `vllm` | LLM (local, any OpenAI-compatible server) | — |
| `openai_codex` | LLM (Codex, OAuth) | `nanobot provider login openai-codex` |
| `github_copilot` | LLM (GitHub Copilot, OAuth) | `nanobot provider login github-copilot` |
@@ -783,6 +847,37 @@ Connects directly to any OpenAI-compatible endpoint — LM Studio, llama.cpp, To
</details>
<details>
<summary><b>Ollama (local)</b></summary>
Run a local model with Ollama, then add to config:
**1. Start Ollama** (example):
```bash
ollama run llama3.2
```
**2. Add to config** (partial — merge into `~/.nanobot/config.json`):
```json
{
"providers": {
"ollama": {
"apiBase": "http://localhost:11434"
}
},
"agents": {
"defaults": {
"provider": "ollama",
"model": "llama3.2"
}
}
}
```
> `provider: "auto"` also works when `providers.ollama.apiBase` is configured, but setting `"provider": "ollama"` is the clearest option.
</details>
<details>
<summary><b>vLLM (local / OpenAI-compatible)</b></summary>

4
core_agent_lines.sh
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@@ -15,7 +15,7 @@ root=$(cat nanobot/__init__.py nanobot/__main__.py | wc -l)
printf " %-16s %5s lines\n" "(root)" "$root"
echo ""
total=$(find nanobot -name "*.py" ! -path "*/channels/*" ! -path "*/cli/*" ! -path "*/providers/*" | xargs cat | wc -l)
total=$(find nanobot -name "*.py" ! -path "*/channels/*" ! -path "*/cli/*" ! -path "*/providers/*" ! -path "*/skills/*" | xargs cat | wc -l)
echo " Core total: $total lines"
echo ""
echo " (excludes: channels/, cli/, providers/)"
echo " (excludes: channels/, cli/, providers/, skills/)"

16
nanobot/agent/context.py
View File

@@ -10,7 +10,7 @@ from typing import Any
from nanobot.agent.memory import MemoryStore
from nanobot.agent.skills import SkillsLoader
from nanobot.utils.helpers import detect_image_mime
from nanobot.utils.helpers import build_assistant_message, detect_image_mime
class ContextBuilder:
@@ -182,12 +182,10 @@ Reply directly with text for conversations. Only use the 'message' tool to send
thinking_blocks: list[dict] | None = None,
) -> list[dict[str, Any]]:
"""Add an assistant message to the message list."""
msg: dict[str, Any] = {"role": "assistant", "content": content}
if tool_calls:
msg["tool_calls"] = tool_calls
if reasoning_content is not None:
msg["reasoning_content"] = reasoning_content
if thinking_blocks:
msg["thinking_blocks"] = thinking_blocks
messages.append(msg)
messages.append(build_assistant_message(
content,
tool_calls=tool_calls,
reasoning_content=reasoning_content,
thinking_blocks=thinking_blocks,
))
return messages

137
nanobot/agent/loop.py
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@@ -4,8 +4,9 @@ from __future__ import annotations
import asyncio
import json
import os
import re
import weakref
import sys
from contextlib import AsyncExitStack
from pathlib import Path
from typing import TYPE_CHECKING, Any, Awaitable, Callable
@@ -13,7 +14,7 @@ from typing import TYPE_CHECKING, Any, Awaitable, Callable
from loguru import logger
from nanobot.agent.context import ContextBuilder
from nanobot.agent.memory import MemoryStore
from nanobot.agent.memory import MemoryConsolidator
from nanobot.agent.subagent import SubagentManager
from nanobot.agent.tools.cron import CronTool
from nanobot.agent.tools.filesystem import EditFileTool, ListDirTool, ReadFileTool, WriteFileTool
@@ -44,7 +45,7 @@ class AgentLoop:
5. Sends responses back
"""
_TOOL_RESULT_MAX_CHARS = 500
_TOOL_RESULT_MAX_CHARS = 16_000
def __init__(
self,
@@ -53,10 +54,7 @@ class AgentLoop:
workspace: Path,
model: str | None = None,
max_iterations: int = 40,
temperature: float = 0.1,
max_tokens: int = 4096,
memory_window: int = 100,
reasoning_effort: str | None = None,
context_window_tokens: int = 65_536,
brave_api_key: str | None = None,
web_proxy: str | None = None,
exec_config: ExecToolConfig | None = None,
@@ -73,10 +71,7 @@ class AgentLoop:
self.workspace = workspace
self.model = model or provider.get_default_model()
self.max_iterations = max_iterations
self.temperature = temperature
self.max_tokens = max_tokens
self.memory_window = memory_window
self.reasoning_effort = reasoning_effort
self.context_window_tokens = context_window_tokens
self.brave_api_key = brave_api_key
self.web_proxy = web_proxy
self.exec_config = exec_config or ExecToolConfig()
@@ -91,9 +86,6 @@ class AgentLoop:
workspace=workspace,
bus=bus,
model=self.model,
temperature=self.temperature,
max_tokens=self.max_tokens,
reasoning_effort=reasoning_effort,
brave_api_key=brave_api_key,
web_proxy=web_proxy,
exec_config=self.exec_config,
@@ -105,11 +97,17 @@ class AgentLoop:
self._mcp_stack: AsyncExitStack | None = None
self._mcp_connected = False
self._mcp_connecting = False
self._consolidating: set[str] = set() # Session keys with consolidation in progress
self._consolidation_tasks: set[asyncio.Task] = set() # Strong refs to in-flight tasks
self._consolidation_locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
self._active_tasks: dict[str, list[asyncio.Task]] = {} # session_key -> tasks
self._processing_lock = asyncio.Lock()
self.memory_consolidator = MemoryConsolidator(
workspace=workspace,
provider=provider,
model=self.model,
sessions=self.sessions,
context_window_tokens=context_window_tokens,
build_messages=self.context.build_messages,
get_tool_definitions=self.tools.get_definitions,
)
self._register_default_tools()
def _register_default_tools(self) -> None:
@@ -141,7 +139,7 @@ class AgentLoop:
await self._mcp_stack.__aenter__()
await connect_mcp_servers(self._mcp_servers, self.tools, self._mcp_stack)
self._mcp_connected = True
except Exception as e:
except BaseException as e:
logger.error("Failed to connect MCP servers (will retry next message): {}", e)
if self._mcp_stack:
try:
@@ -182,7 +180,7 @@ class AgentLoop:
initial_messages: list[dict],
on_progress: Callable[..., Awaitable[None]] | None = None,
) -> tuple[str | None, list[str], list[dict]]:
"""Run the agent iteration loop. Returns (final_content, tools_used, messages)."""
"""Run the agent iteration loop."""
messages = initial_messages
iteration = 0
final_content = None
@@ -191,13 +189,12 @@ class AgentLoop:
while iteration < self.max_iterations:
iteration += 1
response = await self.provider.chat(
tool_defs = self.tools.get_definitions()
response = await self.provider.chat_with_retry(
messages=messages,
tools=self.tools.get_definitions(),
tools=tool_defs,
model=self.model,
temperature=self.temperature,
max_tokens=self.max_tokens,
reasoning_effort=self.reasoning_effort,
)
if response.has_tool_calls:
@@ -208,14 +205,7 @@ class AgentLoop:
await on_progress(self._tool_hint(response.tool_calls), tool_hint=True)
tool_call_dicts = [
{
"id": tc.id,
"type": "function",
"function": {
"name": tc.name,
"arguments": json.dumps(tc.arguments, ensure_ascii=False)
}
}
tc.to_openai_tool_call()
for tc in response.tool_calls
]
messages = self.context.add_assistant_message(
@@ -268,8 +258,11 @@ class AgentLoop:
except asyncio.TimeoutError:
continue
if msg.content.strip().lower() == "/stop":
cmd = msg.content.strip().lower()
if cmd == "/stop":
await self._handle_stop(msg)
elif cmd == "/restart":
await self._handle_restart(msg)
else:
task = asyncio.create_task(self._dispatch(msg))
self._active_tasks.setdefault(msg.session_key, []).append(task)
@@ -286,11 +279,25 @@ class AgentLoop:
pass
sub_cancelled = await self.subagents.cancel_by_session(msg.session_key)
total = cancelled + sub_cancelled
content = f"⏹ Stopped {total} task(s)." if total else "No active task to stop."
content = f"Stopped {total} task(s)." if total else "No active task to stop."
await self.bus.publish_outbound(OutboundMessage(
channel=msg.channel, chat_id=msg.chat_id, content=content,
))
async def _handle_restart(self, msg: InboundMessage) -> None:
"""Restart the process in-place via os.execv."""
await self.bus.publish_outbound(OutboundMessage(
channel=msg.channel, chat_id=msg.chat_id, content="Restarting...",
))
async def _do_restart():
await asyncio.sleep(1)
# Use -m nanobot instead of sys.argv[0] for Windows compatibility
# (sys.argv[0] may be just "nanobot" without full path on Windows)
os.execv(sys.executable, [sys.executable, "-m", "nanobot"] + sys.argv[1:])
asyncio.create_task(_do_restart())
async def _dispatch(self, msg: InboundMessage) -> None:
"""Process a message under the global lock."""
async with self._processing_lock:
@@ -341,8 +348,9 @@ class AgentLoop:
logger.info("Processing system message from {}", msg.sender_id)
key = f"{channel}:{chat_id}"
session = self.sessions.get_or_create(key)
await self.memory_consolidator.maybe_consolidate_by_tokens(session)
self._set_tool_context(channel, chat_id, msg.metadata.get("message_id"))
history = session.get_history(max_messages=self.memory_window)
history = session.get_history(max_messages=0)
messages = self.context.build_messages(
history=history,
current_message=msg.content, channel=channel, chat_id=chat_id,
@@ -350,6 +358,7 @@ class AgentLoop:
final_content, _, all_msgs = await self._run_agent_loop(messages)
self._save_turn(session, all_msgs, 1 + len(history))
self.sessions.save(session)
await self.memory_consolidator.maybe_consolidate_by_tokens(session)
return OutboundMessage(channel=channel, chat_id=chat_id,
content=final_content or "Background task completed.")
@@ -362,27 +371,20 @@ class AgentLoop:
# Slash commands
cmd = msg.content.strip().lower()
if cmd == "/new":
lock = self._consolidation_locks.setdefault(session.key, asyncio.Lock())
self._consolidating.add(session.key)
try:
async with lock:
snapshot = session.messages[session.last_consolidated:]
if snapshot:
temp = Session(key=session.key)
temp.messages = list(snapshot)
if not await self._consolidate_memory(temp, archive_all=True):
if not await self.memory_consolidator.archive_unconsolidated(session):
return OutboundMessage(
channel=msg.channel, chat_id=msg.chat_id,
channel=msg.channel,
chat_id=msg.chat_id,
content="Memory archival failed, session not cleared. Please try again.",
)
except Exception:
logger.exception("/new archival failed for {}", session.key)
return OutboundMessage(
channel=msg.channel, chat_id=msg.chat_id,
channel=msg.channel,
chat_id=msg.chat_id,
content="Memory archival failed, session not cleared. Please try again.",
)
finally:
self._consolidating.discard(session.key)
session.clear()
self.sessions.save(session)
@@ -390,33 +392,24 @@ class AgentLoop:
return OutboundMessage(channel=msg.channel, chat_id=msg.chat_id,
content="New session started.")
if cmd == "/help":
return OutboundMessage(channel=msg.channel, chat_id=msg.chat_id,
content="🐈 nanobot commands:\n/new — Start a new conversation\n/stop — Stop the current task\n/help — Show available commands")
unconsolidated = len(session.messages) - session.last_consolidated
if (unconsolidated >= self.memory_window and session.key not in self._consolidating):
self._consolidating.add(session.key)
lock = self._consolidation_locks.setdefault(session.key, asyncio.Lock())
async def _consolidate_and_unlock():
try:
async with lock:
await self._consolidate_memory(session)
finally:
self._consolidating.discard(session.key)
_task = asyncio.current_task()
if _task is not None:
self._consolidation_tasks.discard(_task)
_task = asyncio.create_task(_consolidate_and_unlock())
self._consolidation_tasks.add(_task)
lines = [
"🐈 nanobot commands:",
"/new — Start a new conversation",
"/stop — Stop the current task",
"/restart — Restart the bot",
"/help — Show available commands",
]
return OutboundMessage(
channel=msg.channel, chat_id=msg.chat_id, content="\n".join(lines),
)
await self.memory_consolidator.maybe_consolidate_by_tokens(session)
self._set_tool_context(msg.channel, msg.chat_id, msg.metadata.get("message_id"))
if message_tool := self.tools.get("message"):
if isinstance(message_tool, MessageTool):
message_tool.start_turn()
history = session.get_history(max_messages=self.memory_window)
history = session.get_history(max_messages=0)
initial_messages = self.context.build_messages(
history=history,
current_message=msg.content,
@@ -441,6 +434,7 @@ class AgentLoop:
self._save_turn(session, all_msgs, 1 + len(history))
self.sessions.save(session)
await self.memory_consolidator.maybe_consolidate_by_tokens(session)
if (mt := self.tools.get("message")) and isinstance(mt, MessageTool) and mt._sent_in_turn:
return None
@@ -487,13 +481,6 @@ class AgentLoop:
session.messages.append(entry)
session.updated_at = datetime.now()
async def _consolidate_memory(self, session, archive_all: bool = False) -> bool:
"""Delegate to MemoryStore.consolidate(). Returns True on success."""
return await MemoryStore(self.workspace).consolidate(
session, self.provider, self.model,
archive_all=archive_all, memory_window=self.memory_window,
)
async def process_direct(
self,
content: str,

278
nanobot/agent/memory.py
View File

@@ -2,17 +2,19 @@
from __future__ import annotations
import asyncio
import json
import weakref
from pathlib import Path
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, Any, Callable
from loguru import logger
from nanobot.utils.helpers import ensure_dir
from nanobot.utils.helpers import ensure_dir, estimate_message_tokens, estimate_prompt_tokens_chain
if TYPE_CHECKING:
from nanobot.providers.base import LLMProvider
from nanobot.session.manager import Session
from nanobot.session.manager import Session, SessionManager
_SAVE_MEMORY_TOOL = [
@@ -26,7 +28,7 @@ _SAVE_MEMORY_TOOL = [
"properties": {
"history_entry": {
"type": "string",
"description": "A paragraph (2-5 sentences) summarizing key events/decisions/topics. "
"description": "A paragraph summarizing key events/decisions/topics. "
"Start with [YYYY-MM-DD HH:MM]. Include detail useful for grep search.",
},
"memory_update": {
@@ -42,6 +44,33 @@ _SAVE_MEMORY_TOOL = [
]
def _ensure_text(value: Any) -> str:
"""Normalize tool-call payload values to text for file storage."""
return value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)
def _normalize_save_memory_args(args: Any) -> dict[str, Any] | None:
"""Normalize provider tool-call arguments to the expected dict shape."""
if isinstance(args, str):
args = json.loads(args)
if isinstance(args, list):
return args[0] if args and isinstance(args[0], dict) else None
return args if isinstance(args, dict) else None
_TOOL_CHOICE_ERROR_MARKERS = (
"tool_choice",
"toolchoice",
"does not support",
'should be ["none", "auto"]',
)
def _is_tool_choice_unsupported(content: str | None) -> bool:
"""Detect provider errors caused by forced tool_choice being unsupported."""
text = (content or "").lower()
return any(m in text for m in _TOOL_CHOICE_ERROR_MARKERS)
class MemoryStore:
"""Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
@@ -66,40 +95,27 @@ class MemoryStore:
long_term = self.read_long_term()
return f"## Long-term Memory\n{long_term}" if long_term else ""
@staticmethod
def _format_messages(messages: list[dict]) -> str:
lines = []
for message in messages:
if not message.get("content"):
continue
tools = f" [tools: {', '.join(message['tools_used'])}]" if message.get("tools_used") else ""
lines.append(
f"[{message.get('timestamp', '?')[:16]}] {message['role'].upper()}{tools}: {message['content']}"
)
return "\n".join(lines)
async def consolidate(
self,
session: Session,
messages: list[dict],
provider: LLMProvider,
model: str,
*,
archive_all: bool = False,
memory_window: int = 50,
) -> bool:
"""Consolidate old messages into MEMORY.md + HISTORY.md via LLM tool call.
Returns True on success (including no-op), False on failure.
"""
if archive_all:
old_messages = session.messages
keep_count = 0
logger.info("Memory consolidation (archive_all): {} messages", len(session.messages))
else:
keep_count = memory_window // 2
if len(session.messages) <= keep_count:
"""Consolidate the provided message chunk into MEMORY.md + HISTORY.md."""
if not messages:
return True
if len(session.messages) - session.last_consolidated <= 0:
return True
old_messages = session.messages[session.last_consolidated:-keep_count]
if not old_messages:
return True
logger.info("Memory consolidation: {} to consolidate, {} keep", len(old_messages), keep_count)
lines = []
for m in old_messages:
if not m.get("content"):
continue
tools = f" [tools: {', '.join(m['tools_used'])}]" if m.get("tools_used") else ""
lines.append(f"[{m.get('timestamp', '?')[:16]}] {m['role'].upper()}{tools}: {m['content']}")
current_memory = self.read_long_term()
prompt = f"""Process this conversation and call the save_memory tool with your consolidation.
@@ -108,35 +124,46 @@ class MemoryStore:
{current_memory or "(empty)"}
## Conversation to Process
{chr(10).join(lines)}"""
{self._format_messages(messages)}"""
try:
response = await provider.chat(
messages=[
chat_messages = [
{"role": "system", "content": "You are a memory consolidation agent. Call the save_memory tool with your consolidation of the conversation."},
{"role": "user", "content": prompt},
],
]
try:
forced = {"type": "function", "function": {"name": "save_memory"}}
response = await provider.chat_with_retry(
messages=chat_messages,
tools=_SAVE_MEMORY_TOOL,
model=model,
tool_choice=forced,
)
if response.finish_reason == "error" and _is_tool_choice_unsupported(
response.content
):
logger.warning("Forced tool_choice unsupported, retrying with auto")
response = await provider.chat_with_retry(
messages=chat_messages,
tools=_SAVE_MEMORY_TOOL,
model=model,
tool_choice="auto",
)
if not response.has_tool_calls:
logger.warning("Memory consolidation: LLM did not call save_memory, skipping")
logger.warning(
"Memory consolidation: LLM did not call save_memory "
"(finish_reason={}, content_len={}, content_preview={})",
response.finish_reason,
len(response.content or ""),
(response.content or "")[:200],
)
return False
args = response.tool_calls[0].arguments
# Some providers return arguments as a JSON string instead of dict
if isinstance(args, str):
args = json.loads(args)
# Some providers return arguments as a list (handle edge case)
if isinstance(args, list):
if args and isinstance(args[0], dict):
args = args[0]
else:
logger.warning("Memory consolidation: unexpected arguments as empty or non-dict list")
return False
if not isinstance(args, dict):
logger.warning("Memory consolidation: unexpected arguments type {}", type(args).__name__)
args = _normalize_save_memory_args(response.tool_calls[0].arguments)
if args is None:
logger.warning("Memory consolidation: unexpected save_memory arguments")
return False
if "history_entry" not in args or "memory_update" not in args:
@@ -150,23 +177,156 @@ class MemoryStore:
logger.warning("Memory consolidation: save_memory payload contains null required fields")
return False
if not isinstance(entry, str):
entry = json.dumps(entry, ensure_ascii=False)
if not isinstance(update, str):
update = json.dumps(update, ensure_ascii=False)
entry = entry.strip()
entry = _ensure_text(entry).strip()
if not entry:
logger.warning("Memory consolidation: history_entry is empty after normalization")
return False
self.append_history(entry)
update = _ensure_text(update)
if update != current_memory:
self.write_long_term(update)
session.last_consolidated = 0 if archive_all else len(session.messages) - keep_count
logger.info("Memory consolidation done: {} messages, last_consolidated={}", len(session.messages), session.last_consolidated)
logger.info("Memory consolidation done for {} messages", len(messages))
return True
except Exception:
logger.exception("Memory consolidation failed")
return False
class MemoryConsolidator:
"""Owns consolidation policy, locking, and session offset updates."""
_MAX_CONSOLIDATION_ROUNDS = 5
def __init__(
self,
workspace: Path,
provider: LLMProvider,
model: str,
sessions: SessionManager,
context_window_tokens: int,
build_messages: Callable[..., list[dict[str, Any]]],
get_tool_definitions: Callable[[], list[dict[str, Any]]],
):
self.store = MemoryStore(workspace)
self.provider = provider
self.model = model
self.sessions = sessions
self.context_window_tokens = context_window_tokens
self._build_messages = build_messages
self._get_tool_definitions = get_tool_definitions
self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
def get_lock(self, session_key: str) -> asyncio.Lock:
"""Return the shared consolidation lock for one session."""
return self._locks.setdefault(session_key, asyncio.Lock())
async def consolidate_messages(self, messages: list[dict[str, object]]) -> bool:
"""Archive a selected message chunk into persistent memory."""
return await self.store.consolidate(messages, self.provider, self.model)
def pick_consolidation_boundary(
self,
session: Session,
tokens_to_remove: int,
) -> tuple[int, int] | None:
"""Pick a user-turn boundary that removes enough old prompt tokens."""
start = session.last_consolidated
if start >= len(session.messages) or tokens_to_remove <= 0:
return None
removed_tokens = 0
last_boundary: tuple[int, int] | None = None
for idx in range(start, len(session.messages)):
message = session.messages[idx]
if idx > start and message.get("role") == "user":
last_boundary = (idx, removed_tokens)
if removed_tokens >= tokens_to_remove:
return last_boundary
removed_tokens += estimate_message_tokens(message)
return last_boundary
def estimate_session_prompt_tokens(self, session: Session) -> tuple[int, str]:
"""Estimate current prompt size for the normal session history view."""
history = session.get_history(max_messages=0)
channel, chat_id = (session.key.split(":", 1) if ":" in session.key else (None, None))
probe_messages = self._build_messages(
history=history,
current_message="[token-probe]",
channel=channel,
chat_id=chat_id,
)
return estimate_prompt_tokens_chain(
self.provider,
self.model,
probe_messages,
self._get_tool_definitions(),
)
async def archive_unconsolidated(self, session: Session) -> bool:
"""Archive the full unconsolidated tail for /new-style session rollover."""
lock = self.get_lock(session.key)
async with lock:
snapshot = session.messages[session.last_consolidated:]
if not snapshot:
return True
return await self.consolidate_messages(snapshot)
async def maybe_consolidate_by_tokens(self, session: Session) -> None:
"""Loop: archive old messages until prompt fits within half the context window."""
if not session.messages or self.context_window_tokens <= 0:
return
lock = self.get_lock(session.key)
async with lock:
target = self.context_window_tokens // 2
estimated, source = self.estimate_session_prompt_tokens(session)
if estimated <= 0:
return
if estimated < self.context_window_tokens:
logger.debug(
"Token consolidation idle {}: {}/{} via {}",
session.key,
estimated,
self.context_window_tokens,
source,
)
return
for round_num in range(self._MAX_CONSOLIDATION_ROUNDS):
if estimated <= target:
return
boundary = self.pick_consolidation_boundary(session, max(1, estimated - target))
if boundary is None:
logger.debug(
"Token consolidation: no safe boundary for {} (round {})",
session.key,
round_num,
)
return
end_idx = boundary[0]
chunk = session.messages[session.last_consolidated:end_idx]
if not chunk:
return
logger.info(
"Token consolidation round {} for {}: {}/{} via {}, chunk={} msgs",
round_num,
session.key,
estimated,
self.context_window_tokens,
source,
len(chunk),
)
if not await self.consolidate_messages(chunk):
return
session.last_consolidated = end_idx
self.sessions.save(session)
estimated, source = self.estimate_session_prompt_tokens(session)
if estimated <= 0:
return

33
nanobot/agent/subagent.py
View File

@@ -16,6 +16,7 @@ from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.config.schema import ExecToolConfig
from nanobot.providers.base import LLMProvider
from nanobot.utils.helpers import build_assistant_message
class SubagentManager:
@@ -27,9 +28,6 @@ class SubagentManager:
workspace: Path,
bus: MessageBus,
model: str | None = None,
temperature: float = 0.7,
max_tokens: int = 4096,
reasoning_effort: str | None = None,
brave_api_key: str | None = None,
web_proxy: str | None = None,
exec_config: "ExecToolConfig | None" = None,
@@ -40,9 +38,6 @@ class SubagentManager:
self.workspace = workspace
self.bus = bus
self.model = model or provider.get_default_model()
self.temperature = temperature
self.max_tokens = max_tokens
self.reasoning_effort = reasoning_effort
self.brave_api_key = brave_api_key
self.web_proxy = web_proxy
self.exec_config = exec_config or ExecToolConfig()
@@ -123,33 +118,23 @@ class SubagentManager:
while iteration < max_iterations:
iteration += 1
response = await self.provider.chat(
response = await self.provider.chat_with_retry(
messages=messages,
tools=tools.get_definitions(),
model=self.model,
temperature=self.temperature,
max_tokens=self.max_tokens,
reasoning_effort=self.reasoning_effort,
)
if response.has_tool_calls:
# Add assistant message with tool calls
tool_call_dicts = [
{
"id": tc.id,
"type": "function",
"function": {
"name": tc.name,
"arguments": json.dumps(tc.arguments, ensure_ascii=False),
},
}
tc.to_openai_tool_call()
for tc in response.tool_calls
]
messages.append({
"role": "assistant",
"content": response.content or "",
"tool_calls": tool_call_dicts,
})
messages.append(build_assistant_message(
response.content or "",
tool_calls=tool_call_dicts,
reasoning_content=response.reasoning_content,
thinking_blocks=response.thinking_blocks,
))
# Execute tools
for tool_call in response.tool_calls:

293
nanobot/agent/tools/filesystem.py
View File

@@ -1,4 +1,4 @@
"""File system tools: read, write, edit."""
"""File system tools: read, write, edit, list."""
import difflib
from pathlib import Path
@@ -23,62 +23,108 @@ def _resolve_path(
return resolved
class ReadFileTool(Tool):
"""Tool to read file contents."""
_MAX_CHARS = 128_000 # ~128 KB — prevents OOM from reading huge files into LLM context
class _FsTool(Tool):
"""Shared base for filesystem tools — common init and path resolution."""
def __init__(self, workspace: Path | None = None, allowed_dir: Path | None = None):
self._workspace = workspace
self._allowed_dir = allowed_dir
def _resolve(self, path: str) -> Path:
return _resolve_path(path, self._workspace, self._allowed_dir)
# ---------------------------------------------------------------------------
# read_file
# ---------------------------------------------------------------------------
class ReadFileTool(_FsTool):
"""Read file contents with optional line-based pagination."""
_MAX_CHARS = 128_000
_DEFAULT_LIMIT = 2000
@property
def name(self) -> str:
return "read_file"
@property
def description(self) -> str:
return "Read the contents of a file at the given path."
return (
"Read the contents of a file. Returns numbered lines. "
"Use offset and limit to paginate through large files."
)
@property
def parameters(self) -> dict[str, Any]:
return {
"type": "object",
"properties": {"path": {"type": "string", "description": "The file path to read"}},
"properties": {
"path": {"type": "string", "description": "The file path to read"},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default 1)",
"minimum": 1,
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default 2000)",
"minimum": 1,
},
},
"required": ["path"],
}
async def execute(self, path: str, **kwargs: Any) -> str:
async def execute(self, path: str, offset: int = 1, limit: int | None = None, **kwargs: Any) -> str:
try:
file_path = _resolve_path(path, self._workspace, self._allowed_dir)
if not file_path.exists():
fp = self._resolve(path)
if not fp.exists():
return f"Error: File not found: {path}"
if not file_path.is_file():
if not fp.is_file():
return f"Error: Not a file: {path}"
size = file_path.stat().st_size
if size > self._MAX_CHARS * 4: # rough upper bound (UTF-8 chars ≤ 4 bytes)
return (
f"Error: File too large ({size:,} bytes). "
f"Use exec tool with head/tail/grep to read portions."
)
all_lines = fp.read_text(encoding="utf-8").splitlines()
total = len(all_lines)
content = file_path.read_text(encoding="utf-8")
if len(content) > self._MAX_CHARS:
return content[: self._MAX_CHARS] + f"\n\n... (truncated — file is {len(content):,} chars, limit {self._MAX_CHARS:,})"
return content
if offset < 1:
offset = 1
if total == 0:
return f"(Empty file: {path})"
if offset > total:
return f"Error: offset {offset} is beyond end of file ({total} lines)"
start = offset - 1
end = min(start + (limit or self._DEFAULT_LIMIT), total)
numbered = [f"{start + i + 1}| {line}" for i, line in enumerate(all_lines[start:end])]
result = "\n".join(numbered)
if len(result) > self._MAX_CHARS:
trimmed, chars = [], 0
for line in numbered:
chars += len(line) + 1
if chars > self._MAX_CHARS:
break
trimmed.append(line)
end = start + len(trimmed)
result = "\n".join(trimmed)
if end < total:
result += f"\n\n(Showing lines {offset}-{end} of {total}. Use offset={end + 1} to continue.)"
else:
result += f"\n\n(End of file — {total} lines total)"
return result
except PermissionError as e:
return f"Error: {e}"
except Exception as e:
return f"Error reading file: {str(e)}"
return f"Error reading file: {e}"
class WriteFileTool(Tool):
"""Tool to write content to a file."""
# ---------------------------------------------------------------------------
# write_file
# ---------------------------------------------------------------------------
def __init__(self, workspace: Path | None = None, allowed_dir: Path | None = None):
self._workspace = workspace
self._allowed_dir = allowed_dir
class WriteFileTool(_FsTool):
"""Write content to a file."""
@property
def name(self) -> str:
@@ -101,22 +147,48 @@ class WriteFileTool(Tool):
async def execute(self, path: str, content: str, **kwargs: Any) -> str:
try:
file_path = _resolve_path(path, self._workspace, self._allowed_dir)
file_path.parent.mkdir(parents=True, exist_ok=True)
file_path.write_text(content, encoding="utf-8")
return f"Successfully wrote {len(content)} bytes to {file_path}"
fp = self._resolve(path)
fp.parent.mkdir(parents=True, exist_ok=True)
fp.write_text(content, encoding="utf-8")
return f"Successfully wrote {len(content)} bytes to {fp}"
except PermissionError as e:
return f"Error: {e}"
except Exception as e:
return f"Error writing file: {str(e)}"
return f"Error writing file: {e}"
class EditFileTool(Tool):
"""Tool to edit a file by replacing text."""
# ---------------------------------------------------------------------------
# edit_file
# ---------------------------------------------------------------------------
def __init__(self, workspace: Path | None = None, allowed_dir: Path | None = None):
self._workspace = workspace
self._allowed_dir = allowed_dir
def _find_match(content: str, old_text: str) -> tuple[str | None, int]:
"""Locate old_text in content: exact first, then line-trimmed sliding window.
Both inputs should use LF line endings (caller normalises CRLF).
Returns (matched_fragment, count) or (None, 0).
"""
if old_text in content:
return old_text, content.count(old_text)
old_lines = old_text.splitlines()
if not old_lines:
return None, 0
stripped_old = [l.strip() for l in old_lines]
content_lines = content.splitlines()
candidates = []
for i in range(len(content_lines) - len(stripped_old) + 1):
window = content_lines[i : i + len(stripped_old)]
if [l.strip() for l in window] == stripped_old:
candidates.append("\n".join(window))
if candidates:
return candidates[0], len(candidates)
return None, 0
class EditFileTool(_FsTool):
"""Edit a file by replacing text with fallback matching."""
@property
def name(self) -> str:
@@ -124,7 +196,11 @@ class EditFileTool(Tool):
@property
def description(self) -> str:
return "Edit a file by replacing old_text with new_text. The old_text must exist exactly in the file."
return (
"Edit a file by replacing old_text with new_text. "
"Supports minor whitespace/line-ending differences. "
"Set replace_all=true to replace every occurrence."
)
@property
def parameters(self) -> dict[str, Any]:
@@ -132,40 +208,52 @@ class EditFileTool(Tool):
"type": "object",
"properties": {
"path": {"type": "string", "description": "The file path to edit"},
"old_text": {"type": "string", "description": "The exact text to find and replace"},
"old_text": {"type": "string", "description": "The text to find and replace"},
"new_text": {"type": "string", "description": "The text to replace with"},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences (default false)",
},
},
"required": ["path", "old_text", "new_text"],
}
async def execute(self, path: str, old_text: str, new_text: str, **kwargs: Any) -> str:
async def execute(
self, path: str, old_text: str, new_text: str,
replace_all: bool = False, **kwargs: Any,
) -> str:
try:
file_path = _resolve_path(path, self._workspace, self._allowed_dir)
if not file_path.exists():
fp = self._resolve(path)
if not fp.exists():
return f"Error: File not found: {path}"
content = file_path.read_text(encoding="utf-8")
raw = fp.read_bytes()
uses_crlf = b"\r\n" in raw
content = raw.decode("utf-8").replace("\r\n", "\n")
match, count = _find_match(content, old_text.replace("\r\n", "\n"))
if old_text not in content:
return self._not_found_message(old_text, content, path)
if match is None:
return self._not_found_msg(old_text, content, path)
if count > 1 and not replace_all:
return (
f"Warning: old_text appears {count} times. "
"Provide more context to make it unique, or set replace_all=true."
)
# Count occurrences
count = content.count(old_text)
if count > 1:
return f"Warning: old_text appears {count} times. Please provide more context to make it unique."
norm_new = new_text.replace("\r\n", "\n")
new_content = content.replace(match, norm_new) if replace_all else content.replace(match, norm_new, 1)
if uses_crlf:
new_content = new_content.replace("\n", "\r\n")
new_content = content.replace(old_text, new_text, 1)
file_path.write_text(new_content, encoding="utf-8")
return f"Successfully edited {file_path}"
fp.write_bytes(new_content.encode("utf-8"))
return f"Successfully edited {fp}"
except PermissionError as e:
return f"Error: {e}"
except Exception as e:
return f"Error editing file: {str(e)}"
return f"Error editing file: {e}"
@staticmethod
def _not_found_message(old_text: str, content: str, path: str) -> str:
"""Build a helpful error when old_text is not found."""
def _not_found_msg(old_text: str, content: str, path: str) -> str:
lines = content.splitlines(keepends=True)
old_lines = old_text.splitlines(keepends=True)
window = len(old_lines)
@@ -177,27 +265,29 @@ class EditFileTool(Tool):
best_ratio, best_start = ratio, i
if best_ratio > 0.5:
diff = "\n".join(
difflib.unified_diff(
old_lines,
lines[best_start : best_start + window],
diff = "\n".join(difflib.unified_diff(
old_lines, lines[best_start : best_start + window],
fromfile="old_text (provided)",
tofile=f"{path} (actual, line {best_start + 1})",
lineterm="",
)
)
))
return f"Error: old_text not found in {path}.\nBest match ({best_ratio:.0%} similar) at line {best_start + 1}:\n{diff}"
return (
f"Error: old_text not found in {path}. No similar text found. Verify the file content."
)
return f"Error: old_text not found in {path}. No similar text found. Verify the file content."
class ListDirTool(Tool):
"""Tool to list directory contents."""
# ---------------------------------------------------------------------------
# list_dir
# ---------------------------------------------------------------------------
def __init__(self, workspace: Path | None = None, allowed_dir: Path | None = None):
self._workspace = workspace
self._allowed_dir = allowed_dir
class ListDirTool(_FsTool):
"""List directory contents with optional recursion."""
_DEFAULT_MAX = 200
_IGNORE_DIRS = {
".git", "node_modules", "__pycache__", ".venv", "venv",
"dist", "build", ".tox", ".mypy_cache", ".pytest_cache",
".ruff_cache", ".coverage", "htmlcov",
}
@property
def name(self) -> str:
@@ -205,34 +295,71 @@ class ListDirTool(Tool):
@property
def description(self) -> str:
return "List the contents of a directory."
return (
"List the contents of a directory. "
"Set recursive=true to explore nested structure. "
"Common noise directories (.git, node_modules, __pycache__, etc.) are auto-ignored."
)
@property
def parameters(self) -> dict[str, Any]:
return {
"type": "object",
"properties": {"path": {"type": "string", "description": "The directory path to list"}},
"properties": {
"path": {"type": "string", "description": "The directory path to list"},
"recursive": {
"type": "boolean",
"description": "Recursively list all files (default false)",
},
"max_entries": {
"type": "integer",
"description": "Maximum entries to return (default 200)",
"minimum": 1,
},
},
"required": ["path"],
}
async def execute(self, path: str, **kwargs: Any) -> str:
async def execute(
self, path: str, recursive: bool = False,
max_entries: int | None = None, **kwargs: Any,
) -> str:
try:
dir_path = _resolve_path(path, self._workspace, self._allowed_dir)
if not dir_path.exists():
dp = self._resolve(path)
if not dp.exists():
return f"Error: Directory not found: {path}"
if not dir_path.is_dir():
if not dp.is_dir():
return f"Error: Not a directory: {path}"
items = []
for item in sorted(dir_path.iterdir()):
prefix = "📁 " if item.is_dir() else "📄 "
items.append(f"{prefix}{item.name}")
cap = max_entries or self._DEFAULT_MAX
items: list[str] = []
total = 0
if not items:
if recursive:
for item in sorted(dp.rglob("*")):
if any(p in self._IGNORE_DIRS for p in item.parts):
continue
total += 1
if len(items) < cap:
rel = item.relative_to(dp)
items.append(f"{rel}/" if item.is_dir() else str(rel))
else:
for item in sorted(dp.iterdir()):
if item.name in self._IGNORE_DIRS:
continue
total += 1
if len(items) < cap:
pfx = "📁 " if item.is_dir() else "📄 "
items.append(f"{pfx}{item.name}")
if not items and total == 0:
return f"Directory {path} is empty"
return "\n".join(items)
result = "\n".join(items)
if total > cap:
result += f"\n\n(truncated, showing first {cap} of {total} entries)"
return result
except PermissionError as e:
return f"Error: {e}"
except Exception as e:
return f"Error listing directory: {str(e)}"
return f"Error listing directory: {e}"

53
nanobot/agent/tools/shell.py
View File

@@ -42,6 +42,9 @@ class ExecTool(Tool):
def name(self) -> str:
return "exec"
_MAX_TIMEOUT = 600
_MAX_OUTPUT = 10_000
@property
def description(self) -> str:
return "Execute a shell command and return its output. Use with caution."
@@ -53,22 +56,36 @@ class ExecTool(Tool):
"properties": {
"command": {
"type": "string",
"description": "The shell command to execute"
"description": "The shell command to execute",
},
"working_dir": {
"type": "string",
"description": "Optional working directory for the command"
}
"description": "Optional working directory for the command",
},
"required": ["command"]
"timeout": {
"type": "integer",
"description": (
"Timeout in seconds. Increase for long-running commands "
"like compilation or installation (default 60, max 600)."
),
"minimum": 1,
"maximum": 600,
},
},
"required": ["command"],
}
async def execute(self, command: str, working_dir: str | None = None, **kwargs: Any) -> str:
async def execute(
self, command: str, working_dir: str | None = None,
timeout: int | None = None, **kwargs: Any,
) -> str:
cwd = working_dir or self.working_dir or os.getcwd()
guard_error = self._guard_command(command, cwd)
if guard_error:
return guard_error
effective_timeout = min(timeout or self.timeout, self._MAX_TIMEOUT)
env = os.environ.copy()
if self.path_append:
env["PATH"] = env.get("PATH", "") + os.pathsep + self.path_append
@@ -85,17 +102,15 @@ class ExecTool(Tool):
try:
stdout, stderr = await asyncio.wait_for(
process.communicate(),
timeout=self.timeout
timeout=effective_timeout,
)
except asyncio.TimeoutError:
process.kill()
# Wait for the process to fully terminate so pipes are
# drained and file descriptors are released.
try:
await asyncio.wait_for(process.wait(), timeout=5.0)
except asyncio.TimeoutError:
pass
return f"Error: Command timed out after {self.timeout} seconds"
return f"Error: Command timed out after {effective_timeout} seconds"
output_parts = []
@@ -107,15 +122,19 @@ class ExecTool(Tool):
if stderr_text.strip():
output_parts.append(f"STDERR:\n{stderr_text}")
if process.returncode != 0:
output_parts.append(f"\nExit code: {process.returncode}")
result = "\n".join(output_parts) if output_parts else "(no output)"
# Truncate very long output
max_len = 10000
# Head + tail truncation to preserve both start and end of output
max_len = self._MAX_OUTPUT
if len(result) > max_len:
result = result[:max_len] + f"\n... (truncated, {len(result) - max_len} more chars)"
half = max_len // 2
result = (
result[:half]
+ f"\n\n... ({len(result) - max_len:,} chars truncated) ...\n\n"
+ result[-half:]
)
return result
@@ -143,7 +162,8 @@ class ExecTool(Tool):
for raw in self._extract_absolute_paths(cmd):
try:
p = Path(raw.strip()).resolve()
expanded = os.path.expandvars(raw.strip())
p = Path(expanded).expanduser().resolve()
except Exception:
continue
if p.is_absolute() and cwd_path not in p.parents and p != cwd_path:
@@ -154,5 +174,6 @@ class ExecTool(Tool):
@staticmethod
def _extract_absolute_paths(command: str) -> list[str]:
win_paths = re.findall(r"[A-Za-z]:\\[^\s\"'|><;]+", command) # Windows: C:\...
posix_paths = re.findall(r"(?:^|[\s|>])(/[^\s\"'>]+)", command) # POSIX: /absolute only
return win_paths + posix_paths
posix_paths = re.findall(r"(?:^|[\s|>'\"])(/[^\s\"'>;|<]+)", command) # POSIX: /absolute only
home_paths = re.findall(r"(?:^|[\s|>'\"])(~[^\s\"'>;|<]*)", command) # POSIX/Windows home shortcut: ~
return win_paths + posix_paths + home_paths

18
nanobot/channels/base.py
View File

@@ -1,6 +1,9 @@
"""Base channel interface for chat platforms."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any
from loguru import logger
@@ -18,6 +21,8 @@ class BaseChannel(ABC):
"""
name: str = "base"
display_name: str = "Base"
transcription_api_key: str = ""
def __init__(self, config: Any, bus: MessageBus):
"""
@@ -31,6 +36,19 @@ class BaseChannel(ABC):
self.bus = bus
self._running = False
async def transcribe_audio(self, file_path: str | Path) -> str:
"""Transcribe an audio file via Groq Whisper. Returns empty string on failure."""
if not self.transcription_api_key:
return ""
try:
from nanobot.providers.transcription import GroqTranscriptionProvider
provider = GroqTranscriptionProvider(api_key=self.transcription_api_key)
return await provider.transcribe(file_path)
except Exception as e:
logger.warning("{}: audio transcription failed: {}", self.name, e)
return ""
@abstractmethod
async def start(self) -> None:
"""

3
nanobot/channels/dingtalk.py
View File

@@ -57,6 +57,8 @@ class NanobotDingTalkHandler(CallbackHandler):
content = ""
if chatbot_msg.text:
content = chatbot_msg.text.content.strip()
elif chatbot_msg.extensions.get("content", {}).get("recognition"):
content = chatbot_msg.extensions["content"]["recognition"].strip()
if not content:
content = message.data.get("text", {}).get("content", "").strip()
@@ -112,6 +114,7 @@ class DingTalkChannel(BaseChannel):
"""
name = "dingtalk"
display_name = "DingTalk"
_IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"}
_AUDIO_EXTS = {".amr", ".mp3", ".wav", ".ogg", ".m4a", ".aac"}
_VIDEO_EXTS = {".mp4", ".mov", ".avi", ".mkv", ".webm"}

1
nanobot/channels/discord.py
View File

@@ -25,6 +25,7 @@ class DiscordChannel(BaseChannel):
"""Discord channel using Gateway websocket."""
name = "discord"
display_name = "Discord"
def __init__(self, config: DiscordConfig, bus: MessageBus):
super().__init__(config, bus)

1
nanobot/channels/email.py
View File

@@ -35,6 +35,7 @@ class EmailChannel(BaseChannel):
"""
name = "email"
display_name = "Email"
_IMAP_MONTHS = (
"Jan",
"Feb",

39
nanobot/channels/feishu.py
View File

@@ -244,11 +244,11 @@ class FeishuChannel(BaseChannel):
"""
name = "feishu"
display_name = "Feishu"
def __init__(self, config: FeishuConfig, bus: MessageBus, groq_api_key: str = ""):
def __init__(self, config: FeishuConfig, bus: MessageBus):
super().__init__(config, bus)
self.config: FeishuConfig = config
self.groq_api_key = groq_api_key
self._client: Any = None
self._ws_client: Any = None
self._ws_thread: threading.Thread | None = None
@@ -352,6 +352,27 @@ class FeishuChannel(BaseChannel):
self._running = False
logger.info("Feishu bot stopped")
def _is_bot_mentioned(self, message: Any) -> bool:
"""Check if the bot is @mentioned in the message."""
raw_content = message.content or ""
if "@_all" in raw_content:
return True
for mention in getattr(message, "mentions", None) or []:
mid = getattr(mention, "id", None)
if not mid:
continue
# Bot mentions have no user_id (None or "") but a valid open_id
if not getattr(mid, "user_id", None) and (getattr(mid, "open_id", None) or "").startswith("ou_"):
return True
return False
def _is_group_message_for_bot(self, message: Any) -> bool:
"""Allow group messages when policy is open or bot is @mentioned."""
if self.config.group_policy == "open":
return True
return self._is_bot_mentioned(message)
def _add_reaction_sync(self, message_id: str, emoji_type: str) -> None:
"""Sync helper for adding reaction (runs in thread pool)."""
from lark_oapi.api.im.v1 import CreateMessageReactionRequest, CreateMessageReactionRequestBody, Emoji
@@ -893,6 +914,10 @@ class FeishuChannel(BaseChannel):
chat_type = message.chat_type
msg_type = message.message_type
if chat_type == "group" and not self._is_group_message_for_bot(message):
logger.debug("Feishu: skipping group message (not mentioned)")
return
# Add reaction
await self._add_reaction(message_id, self.config.react_emoji)
@@ -928,16 +953,10 @@ class FeishuChannel(BaseChannel):
if file_path:
media_paths.append(file_path)
# Transcribe audio using Groq Whisper
if msg_type == "audio" and file_path and self.groq_api_key:
try:
from nanobot.providers.transcription import GroqTranscriptionProvider
transcriber = GroqTranscriptionProvider(api_key=self.groq_api_key)
transcription = await transcriber.transcribe(file_path)
if msg_type == "audio" and file_path:
transcription = await self.transcribe_audio(file_path)
if transcription:
content_text = f"[transcription: {transcription}]"
except Exception as e:
logger.warning("Failed to transcribe audio: {}", e)
content_parts.append(content_text)

129
nanobot/channels/manager.py
View File

@@ -7,7 +7,6 @@ from typing import Any
from loguru import logger
from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.base import BaseChannel
from nanobot.config.schema import Config
@@ -32,123 +31,23 @@ class ChannelManager:
self._init_channels()
def _init_channels(self) -> None:
"""Initialize channels based on config."""
"""Initialize channels discovered via pkgutil scan."""
from nanobot.channels.registry import discover_channel_names, load_channel_class
# Telegram channel
if self.config.channels.telegram.enabled:
groq_key = self.config.providers.groq.api_key
for modname in discover_channel_names():
section = getattr(self.config.channels, modname, None)
if not section or not getattr(section, "enabled", False):
continue
try:
from nanobot.channels.telegram import TelegramChannel
self.channels["telegram"] = TelegramChannel(
self.config.channels.telegram,
self.bus,
groq_api_key=self.config.providers.groq.api_key,
)
logger.info("Telegram channel enabled")
cls = load_channel_class(modname)
channel = cls(section, self.bus)
channel.transcription_api_key = groq_key
self.channels[modname] = channel
logger.info("{} channel enabled", cls.display_name)
except ImportError as e:
logger.warning("Telegram channel not available: {}", e)
# WhatsApp channel
if self.config.channels.whatsapp.enabled:
try:
from nanobot.channels.whatsapp import WhatsAppChannel
self.channels["whatsapp"] = WhatsAppChannel(
self.config.channels.whatsapp, self.bus
)
logger.info("WhatsApp channel enabled")
except ImportError as e:
logger.warning("WhatsApp channel not available: {}", e)
# Discord channel
if self.config.channels.discord.enabled:
try:
from nanobot.channels.discord import DiscordChannel
self.channels["discord"] = DiscordChannel(
self.config.channels.discord, self.bus
)
logger.info("Discord channel enabled")
except ImportError as e:
logger.warning("Discord channel not available: {}", e)
# Feishu channel
if self.config.channels.feishu.enabled:
try:
from nanobot.channels.feishu import FeishuChannel
self.channels["feishu"] = FeishuChannel(
self.config.channels.feishu, self.bus,
groq_api_key=self.config.providers.groq.api_key,
)
logger.info("Feishu channel enabled")
except ImportError as e:
logger.warning("Feishu channel not available: {}", e)
# Mochat channel
if self.config.channels.mochat.enabled:
try:
from nanobot.channels.mochat import MochatChannel
self.channels["mochat"] = MochatChannel(
self.config.channels.mochat, self.bus
)
logger.info("Mochat channel enabled")
except ImportError as e:
logger.warning("Mochat channel not available: {}", e)
# DingTalk channel
if self.config.channels.dingtalk.enabled:
try:
from nanobot.channels.dingtalk import DingTalkChannel
self.channels["dingtalk"] = DingTalkChannel(
self.config.channels.dingtalk, self.bus
)
logger.info("DingTalk channel enabled")
except ImportError as e:
logger.warning("DingTalk channel not available: {}", e)
# Email channel
if self.config.channels.email.enabled:
try:
from nanobot.channels.email import EmailChannel
self.channels["email"] = EmailChannel(
self.config.channels.email, self.bus
)
logger.info("Email channel enabled")
except ImportError as e:
logger.warning("Email channel not available: {}", e)
# Slack channel
if self.config.channels.slack.enabled:
try:
from nanobot.channels.slack import SlackChannel
self.channels["slack"] = SlackChannel(
self.config.channels.slack, self.bus
)
logger.info("Slack channel enabled")
except ImportError as e:
logger.warning("Slack channel not available: {}", e)
# QQ channel
if self.config.channels.qq.enabled:
try:
from nanobot.channels.qq import QQChannel
self.channels["qq"] = QQChannel(
self.config.channels.qq,
self.bus,
)
logger.info("QQ channel enabled")
except ImportError as e:
logger.warning("QQ channel not available: {}", e)
# Matrix channel
if self.config.channels.matrix.enabled:
try:
from nanobot.channels.matrix import MatrixChannel
self.channels["matrix"] = MatrixChannel(
self.config.channels.matrix,
self.bus,
)
logger.info("Matrix channel enabled")
except ImportError as e:
logger.warning("Matrix channel not available: {}", e)
logger.warning("{} channel not available: {}", modname, e)
self._validate_allow_from()

18
nanobot/channels/matrix.py
View File

@@ -37,6 +37,7 @@ except ImportError as e:
) from e
from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.base import BaseChannel
from nanobot.config.paths import get_data_dir, get_media_dir
from nanobot.utils.helpers import safe_filename
@@ -146,15 +147,15 @@ class MatrixChannel(BaseChannel):
"""Matrix (Element) channel using long-polling sync."""
name = "matrix"
display_name = "Matrix"
def __init__(self, config: Any, bus, *, restrict_to_workspace: bool = False,
workspace: Path | None = None):
def __init__(self, config: Any, bus: MessageBus):
super().__init__(config, bus)
self.client: AsyncClient | None = None
self._sync_task: asyncio.Task | None = None
self._typing_tasks: dict[str, asyncio.Task] = {}
self._restrict_to_workspace = restrict_to_workspace
self._workspace = workspace.expanduser().resolve() if workspace else None
self._restrict_to_workspace = False
self._workspace: Path | None = None
self._server_upload_limit_bytes: int | None = None
self._server_upload_limit_checked = False
@@ -677,7 +678,14 @@ class MatrixChannel(BaseChannel):
parts: list[str] = []
if isinstance(body := getattr(event, "body", None), str) and body.strip():
parts.append(body.strip())
if marker:
if attachment and attachment.get("type") == "audio":
transcription = await self.transcribe_audio(attachment["path"])
if transcription:
parts.append(f"[transcription: {transcription}]")
else:
parts.append(marker)
elif marker:
parts.append(marker)
await self._start_typing_keepalive(room.room_id)

1
nanobot/channels/mochat.py
View File

@@ -216,6 +216,7 @@ class MochatChannel(BaseChannel):
"""Mochat channel using socket.io with fallback polling workers."""
name = "mochat"
display_name = "Mochat"
def __init__(self, config: MochatConfig, bus: MessageBus):
super().__init__(config, bus)

9
nanobot/channels/qq.py
View File

@@ -54,6 +54,7 @@ class QQChannel(BaseChannel):
"""QQ channel using botpy SDK with WebSocket connection."""
name = "qq"
display_name = "QQ"
def __init__(self, config: QQConfig, bus: MessageBus):
super().__init__(config, bus)
@@ -113,16 +114,16 @@ class QQChannel(BaseChannel):
if msg_type == "group":
await self._client.api.post_group_message(
group_openid=msg.chat_id,
msg_type=2,
markdown={"content": msg.content},
msg_type=0,
content=msg.content,
msg_id=msg_id,
msg_seq=self._msg_seq,
)
else:
await self._client.api.post_c2c_message(
openid=msg.chat_id,
msg_type=2,
markdown={"content": msg.content},
msg_type=0,
content=msg.content,
msg_id=msg_id,
msg_seq=self._msg_seq,
)

35
nanobot/channels/registry.py Normal file
View File

@@ -0,0 +1,35 @@
"""Auto-discovery for channel modules — no hardcoded registry."""
from __future__ import annotations
import importlib
import pkgutil
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from nanobot.channels.base import BaseChannel
_INTERNAL = frozenset({"base", "manager", "registry"})
def discover_channel_names() -> list[str]:
"""Return all channel module names by scanning the package (zero imports)."""
import nanobot.channels as pkg
return [
name
for _, name, ispkg in pkgutil.iter_modules(pkg.__path__)
if name not in _INTERNAL and not ispkg
]
def load_channel_class(module_name: str) -> type[BaseChannel]:
"""Import *module_name* and return the first BaseChannel subclass found."""
from nanobot.channels.base import BaseChannel as _Base
mod = importlib.import_module(f"nanobot.channels.{module_name}")
for attr in dir(mod):
obj = getattr(mod, attr)
if isinstance(obj, type) and issubclass(obj, _Base) and obj is not _Base:
return obj
raise ImportError(f"No BaseChannel subclass in nanobot.channels.{module_name}")

1
nanobot/channels/slack.py
View File

@@ -21,6 +21,7 @@ class SlackChannel(BaseChannel):
"""Slack channel using Socket Mode."""
name = "slack"
display_name = "Slack"
def __init__(self, config: SlackConfig, bus: MessageBus):
super().__init__(config, bus)

218
nanobot/channels/telegram.py
View File

@@ -20,6 +20,7 @@ from nanobot.config.schema import TelegramConfig
from nanobot.utils.helpers import split_message
TELEGRAM_MAX_MESSAGE_LEN = 4000 # Telegram message character limit
TELEGRAM_REPLY_CONTEXT_MAX_LEN = TELEGRAM_MAX_MESSAGE_LEN # Max length for reply context in user message
def _strip_md(s: str) -> str:
@@ -155,6 +156,7 @@ class TelegramChannel(BaseChannel):
"""
name = "telegram"
display_name = "Telegram"
# Commands registered with Telegram's command menu
BOT_COMMANDS = [
@@ -162,23 +164,20 @@ class TelegramChannel(BaseChannel):
BotCommand("new", "Start a new conversation"),
BotCommand("stop", "Stop the current task"),
BotCommand("help", "Show available commands"),
BotCommand("restart", "Restart the bot"),
]
def __init__(
self,
config: TelegramConfig,
bus: MessageBus,
groq_api_key: str = "",
):
def __init__(self, config: TelegramConfig, bus: MessageBus):
super().__init__(config, bus)
self.config: TelegramConfig = config
self.groq_api_key = groq_api_key
self._app: Application | None = None
self._chat_ids: dict[str, int] = {} # Map sender_id to chat_id for replies
self._typing_tasks: dict[str, asyncio.Task] = {} # chat_id -> typing loop task
self._media_group_buffers: dict[str, dict] = {}
self._media_group_tasks: dict[str, asyncio.Task] = {}
self._message_threads: dict[tuple[str, int], int] = {}
self._bot_user_id: int | None = None
self._bot_username: str | None = None
def is_allowed(self, sender_id: str) -> bool:
"""Preserve Telegram's legacy id|username allowlist matching."""
@@ -223,6 +222,7 @@ class TelegramChannel(BaseChannel):
self._app.add_handler(CommandHandler("start", self._on_start))
self._app.add_handler(CommandHandler("new", self._forward_command))
self._app.add_handler(CommandHandler("stop", self._forward_command))
self._app.add_handler(CommandHandler("restart", self._forward_command))
self._app.add_handler(CommandHandler("help", self._on_help))
# Add message handler for text, photos, voice, documents
@@ -242,6 +242,8 @@ class TelegramChannel(BaseChannel):
# Get bot info and register command menu
bot_info = await self._app.bot.get_me()
self._bot_user_id = getattr(bot_info, "id", None)
self._bot_username = getattr(bot_info, "username", None)
logger.info("Telegram bot @{} connected", bot_info.username)
try:
@@ -452,6 +454,7 @@ class TelegramChannel(BaseChannel):
@staticmethod
def _build_message_metadata(message, user) -> dict:
"""Build common Telegram inbound metadata payload."""
reply_to = getattr(message, "reply_to_message", None)
return {
"message_id": message.message_id,
"user_id": user.id,
@@ -460,8 +463,137 @@ class TelegramChannel(BaseChannel):
"is_group": message.chat.type != "private",
"message_thread_id": getattr(message, "message_thread_id", None),
"is_forum": bool(getattr(message.chat, "is_forum", False)),
"reply_to_message_id": getattr(reply_to, "message_id", None) if reply_to else None,
}
@staticmethod
def _extract_reply_context(message) -> str | None:
"""Extract text from the message being replied to, if any."""
reply = getattr(message, "reply_to_message", None)
if not reply:
return None
text = getattr(reply, "text", None) or getattr(reply, "caption", None) or ""
if len(text) > TELEGRAM_REPLY_CONTEXT_MAX_LEN:
text = text[:TELEGRAM_REPLY_CONTEXT_MAX_LEN] + "..."
return f"[Reply to: {text}]" if text else None
async def _download_message_media(
self, msg, *, add_failure_content: bool = False
) -> tuple[list[str], list[str]]:
"""Download media from a message (current or reply). Returns (media_paths, content_parts)."""
media_file = None
media_type = None
if getattr(msg, "photo", None):
media_file = msg.photo[-1]
media_type = "image"
elif getattr(msg, "voice", None):
media_file = msg.voice
media_type = "voice"
elif getattr(msg, "audio", None):
media_file = msg.audio
media_type = "audio"
elif getattr(msg, "document", None):
media_file = msg.document
media_type = "file"
elif getattr(msg, "video", None):
media_file = msg.video
media_type = "video"
elif getattr(msg, "video_note", None):
media_file = msg.video_note
media_type = "video"
elif getattr(msg, "animation", None):
media_file = msg.animation
media_type = "animation"
if not media_file or not self._app:
return [], []
try:
file = await self._app.bot.get_file(media_file.file_id)
ext = self._get_extension(
media_type,
getattr(media_file, "mime_type", None),
getattr(media_file, "file_name", None),
)
media_dir = get_media_dir("telegram")
file_path = media_dir / f"{media_file.file_id[:16]}{ext}"
await file.download_to_drive(str(file_path))
path_str = str(file_path)
if media_type in ("voice", "audio"):
transcription = await self.transcribe_audio(file_path)
if transcription:
logger.info("Transcribed {}: {}...", media_type, transcription[:50])
return [path_str], [f"[transcription: {transcription}]"]
return [path_str], [f"[{media_type}: {path_str}]"]
return [path_str], [f"[{media_type}: {path_str}]"]
except Exception as e:
logger.warning("Failed to download message media: {}", e)
if add_failure_content:
return [], [f"[{media_type}: download failed]"]
return [], []
async def _ensure_bot_identity(self) -> tuple[int | None, str | None]:
"""Load bot identity once and reuse it for mention/reply checks."""
if self._bot_user_id is not None or self._bot_username is not None:
return self._bot_user_id, self._bot_username
if not self._app:
return None, None
bot_info = await self._app.bot.get_me()
self._bot_user_id = getattr(bot_info, "id", None)
self._bot_username = getattr(bot_info, "username", None)
return self._bot_user_id, self._bot_username
@staticmethod
def _has_mention_entity(
text: str,
entities,
bot_username: str,
bot_id: int | None,
) -> bool:
"""Check Telegram mention entities against the bot username."""
handle = f"@{bot_username}".lower()
for entity in entities or []:
entity_type = getattr(entity, "type", None)
if entity_type == "text_mention":
user = getattr(entity, "user", None)
if user is not None and bot_id is not None and getattr(user, "id", None) == bot_id:
return True
continue
if entity_type != "mention":
continue
offset = getattr(entity, "offset", None)
length = getattr(entity, "length", None)
if offset is None or length is None:
continue
if text[offset : offset + length].lower() == handle:
return True
return handle in text.lower()
async def _is_group_message_for_bot(self, message) -> bool:
"""Allow group messages when policy is open, @mentioned, or replying to the bot."""
if message.chat.type == "private" or self.config.group_policy == "open":
return True
bot_id, bot_username = await self._ensure_bot_identity()
if bot_username:
text = message.text or ""
caption = message.caption or ""
if self._has_mention_entity(
text,
getattr(message, "entities", None),
bot_username,
bot_id,
):
return True
if self._has_mention_entity(
caption,
getattr(message, "caption_entities", None),
bot_username,
bot_id,
):
return True
reply_user = getattr(getattr(message, "reply_to_message", None), "from_user", None)
return bool(bot_id and reply_user and reply_user.id == bot_id)
def _remember_thread_context(self, message) -> None:
"""Cache topic thread id by chat/message id for follow-up replies."""
message_thread_id = getattr(message, "message_thread_id", None)
@@ -482,7 +614,7 @@ class TelegramChannel(BaseChannel):
await self._handle_message(
sender_id=self._sender_id(user),
chat_id=str(message.chat_id),
content=message.text,
content=message.text or "",
metadata=self._build_message_metadata(message, user),
session_key=self._derive_topic_session_key(message),
)
@@ -501,6 +633,9 @@ class TelegramChannel(BaseChannel):
# Store chat_id for replies
self._chat_ids[sender_id] = chat_id
if not await self._is_group_message_for_bot(message):
return
# Build content from text and/or media
content_parts = []
media_paths = []
@@ -511,57 +646,26 @@ class TelegramChannel(BaseChannel):
if message.caption:
content_parts.append(message.caption)
# Handle media files
media_file = None
media_type = None
if message.photo:
media_file = message.photo[-1] # Largest photo
media_type = "image"
elif message.voice:
media_file = message.voice
media_type = "voice"
elif message.audio:
media_file = message.audio
media_type = "audio"
elif message.document:
media_file = message.document
media_type = "file"
# Download media if present
if media_file and self._app:
try:
file = await self._app.bot.get_file(media_file.file_id)
ext = self._get_extension(
media_type,
getattr(media_file, 'mime_type', None),
getattr(media_file, 'file_name', None),
# Download current message media
current_media_paths, current_media_parts = await self._download_message_media(
message, add_failure_content=True
)
media_dir = get_media_dir("telegram")
file_path = media_dir / f"{media_file.file_id[:16]}{ext}"
await file.download_to_drive(str(file_path))
media_paths.append(str(file_path))
# Handle voice transcription
if media_type == "voice" or media_type == "audio":
from nanobot.providers.transcription import GroqTranscriptionProvider
transcriber = GroqTranscriptionProvider(api_key=self.groq_api_key)
transcription = await transcriber.transcribe(file_path)
if transcription:
logger.info("Transcribed {}: {}...", media_type, transcription[:50])
content_parts.append(f"[transcription: {transcription}]")
else:
content_parts.append(f"[{media_type}: {file_path}]")
else:
content_parts.append(f"[{media_type}: {file_path}]")
logger.debug("Downloaded {} to {}", media_type, file_path)
except Exception as e:
logger.error("Failed to download media: {}", e)
content_parts.append(f"[{media_type}: download failed]")
media_paths.extend(current_media_paths)
content_parts.extend(current_media_parts)
if current_media_paths:
logger.debug("Downloaded message media to {}", current_media_paths[0])
# Reply context: text and/or media from the replied-to message
reply = getattr(message, "reply_to_message", None)
if reply is not None:
reply_ctx = self._extract_reply_context(message)
reply_media, reply_media_parts = await self._download_message_media(reply)
if reply_media:
media_paths = reply_media + media_paths
logger.debug("Attached replied-to media: {}", reply_media[0])
tag = reply_ctx or (f"[Reply to: {reply_media_parts[0]}]" if reply_media_parts else None)
if tag:
content_parts.insert(0, tag)
content = "\n".join(content_parts) if content_parts else "[empty message]"
logger.debug("Telegram message from {}: {}...", sender_id, content[:50])

353
nanobot/channels/wecom.py Normal file
View File

@@ -0,0 +1,353 @@
"""WeCom (Enterprise WeChat) channel implementation using wecom_aibot_sdk."""
import asyncio
import importlib.util
import os
from collections import OrderedDict
from typing import Any
from loguru import logger
from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.base import BaseChannel
from nanobot.config.paths import get_media_dir
from nanobot.config.schema import WecomConfig
WECOM_AVAILABLE = importlib.util.find_spec("wecom_aibot_sdk") is not None
# Message type display mapping
MSG_TYPE_MAP = {
"image": "[image]",
"voice": "[voice]",
"file": "[file]",
"mixed": "[mixed content]",
}
class WecomChannel(BaseChannel):
"""
WeCom (Enterprise WeChat) channel using WebSocket long connection.
Uses WebSocket to receive events - no public IP or webhook required.
Requires:
- Bot ID and Secret from WeCom AI Bot platform
"""
name = "wecom"
display_name = "WeCom"
def __init__(self, config: WecomConfig, bus: MessageBus):
super().__init__(config, bus)
self.config: WecomConfig = config
self._client: Any = None
self._processed_message_ids: OrderedDict[str, None] = OrderedDict()
self._loop: asyncio.AbstractEventLoop | None = None
self._generate_req_id = None
# Store frame headers for each chat to enable replies
self._chat_frames: dict[str, Any] = {}
async def start(self) -> None:
"""Start the WeCom bot with WebSocket long connection."""
if not WECOM_AVAILABLE:
logger.error("WeCom SDK not installed. Run: pip install nanobot-ai[wecom]")
return
if not self.config.bot_id or not self.config.secret:
logger.error("WeCom bot_id and secret not configured")
return
from wecom_aibot_sdk import WSClient, generate_req_id
self._running = True
self._loop = asyncio.get_running_loop()
self._generate_req_id = generate_req_id
# Create WebSocket client
self._client = WSClient({
"bot_id": self.config.bot_id,
"secret": self.config.secret,
"reconnect_interval": 1000,
"max_reconnect_attempts": -1, # Infinite reconnect
"heartbeat_interval": 30000,
})
# Register event handlers
self._client.on("connected", self._on_connected)
self._client.on("authenticated", self._on_authenticated)
self._client.on("disconnected", self._on_disconnected)
self._client.on("error", self._on_error)
self._client.on("message.text", self._on_text_message)
self._client.on("message.image", self._on_image_message)
self._client.on("message.voice", self._on_voice_message)
self._client.on("message.file", self._on_file_message)
self._client.on("message.mixed", self._on_mixed_message)
self._client.on("event.enter_chat", self._on_enter_chat)
logger.info("WeCom bot starting with WebSocket long connection")
logger.info("No public IP required - using WebSocket to receive events")
# Connect
await self._client.connect_async()
# Keep running until stopped
while self._running:
await asyncio.sleep(1)
async def stop(self) -> None:
"""Stop the WeCom bot."""
self._running = False
if self._client:
await self._client.disconnect()
logger.info("WeCom bot stopped")
async def _on_connected(self, frame: Any) -> None:
"""Handle WebSocket connected event."""
logger.info("WeCom WebSocket connected")
async def _on_authenticated(self, frame: Any) -> None:
"""Handle authentication success event."""
logger.info("WeCom authenticated successfully")
async def _on_disconnected(self, frame: Any) -> None:
"""Handle WebSocket disconnected event."""
reason = frame.body if hasattr(frame, 'body') else str(frame)
logger.warning("WeCom WebSocket disconnected: {}", reason)
async def _on_error(self, frame: Any) -> None:
"""Handle error event."""
logger.error("WeCom error: {}", frame)
async def _on_text_message(self, frame: Any) -> None:
"""Handle text message."""
await self._process_message(frame, "text")
async def _on_image_message(self, frame: Any) -> None:
"""Handle image message."""
await self._process_message(frame, "image")
async def _on_voice_message(self, frame: Any) -> None:
"""Handle voice message."""
await self._process_message(frame, "voice")
async def _on_file_message(self, frame: Any) -> None:
"""Handle file message."""
await self._process_message(frame, "file")
async def _on_mixed_message(self, frame: Any) -> None:
"""Handle mixed content message."""
await self._process_message(frame, "mixed")
async def _on_enter_chat(self, frame: Any) -> None:
"""Handle enter_chat event (user opens chat with bot)."""
try:
# Extract body from WsFrame dataclass or dict
if hasattr(frame, 'body'):
body = frame.body or {}
elif isinstance(frame, dict):
body = frame.get("body", frame)
else:
body = {}
chat_id = body.get("chatid", "") if isinstance(body, dict) else ""
if chat_id and self.config.welcome_message:
await self._client.reply_welcome(frame, {
"msgtype": "text",
"text": {"content": self.config.welcome_message},
})
except Exception as e:
logger.error("Error handling enter_chat: {}", e)
async def _process_message(self, frame: Any, msg_type: str) -> None:
"""Process incoming message and forward to bus."""
try:
# Extract body from WsFrame dataclass or dict
if hasattr(frame, 'body'):
body = frame.body or {}
elif isinstance(frame, dict):
body = frame.get("body", frame)
else:
body = {}
# Ensure body is a dict
if not isinstance(body, dict):
logger.warning("Invalid body type: {}", type(body))
return
# Extract message info
msg_id = body.get("msgid", "")
if not msg_id:
msg_id = f"{body.get('chatid', '')}_{body.get('sendertime', '')}"
# Deduplication check
if msg_id in self._processed_message_ids:
return
self._processed_message_ids[msg_id] = None
# Trim cache
while len(self._processed_message_ids) > 1000:
self._processed_message_ids.popitem(last=False)
# Extract sender info from "from" field (SDK format)
from_info = body.get("from", {})
sender_id = from_info.get("userid", "unknown") if isinstance(from_info, dict) else "unknown"
# For single chat, chatid is the sender's userid
# For group chat, chatid is provided in body
chat_type = body.get("chattype", "single")
chat_id = body.get("chatid", sender_id)
content_parts = []
if msg_type == "text":
text = body.get("text", {}).get("content", "")
if text:
content_parts.append(text)
elif msg_type == "image":
image_info = body.get("image", {})
file_url = image_info.get("url", "")
aes_key = image_info.get("aeskey", "")
if file_url and aes_key:
file_path = await self._download_and_save_media(file_url, aes_key, "image")
if file_path:
filename = os.path.basename(file_path)
content_parts.append(f"[image: {filename}]\n[Image: source: {file_path}]")
else:
content_parts.append("[image: download failed]")
else:
content_parts.append("[image: download failed]")
elif msg_type == "voice":
voice_info = body.get("voice", {})
# Voice message already contains transcribed content from WeCom
voice_content = voice_info.get("content", "")
if voice_content:
content_parts.append(f"[voice] {voice_content}")
else:
content_parts.append("[voice]")
elif msg_type == "file":
file_info = body.get("file", {})
file_url = file_info.get("url", "")
aes_key = file_info.get("aeskey", "")
file_name = file_info.get("name", "unknown")
if file_url and aes_key:
file_path = await self._download_and_save_media(file_url, aes_key, "file", file_name)
if file_path:
content_parts.append(f"[file: {file_name}]\n[File: source: {file_path}]")
else:
content_parts.append(f"[file: {file_name}: download failed]")
else:
content_parts.append(f"[file: {file_name}: download failed]")
elif msg_type == "mixed":
# Mixed content contains multiple message items
msg_items = body.get("mixed", {}).get("item", [])
for item in msg_items:
item_type = item.get("type", "")
if item_type == "text":
text = item.get("text", {}).get("content", "")
if text:
content_parts.append(text)
else:
content_parts.append(MSG_TYPE_MAP.get(item_type, f"[{item_type}]"))
else:
content_parts.append(MSG_TYPE_MAP.get(msg_type, f"[{msg_type}]"))
content = "\n".join(content_parts) if content_parts else ""
if not content:
return
# Store frame for this chat to enable replies
self._chat_frames[chat_id] = frame
# Forward to message bus
# Note: media paths are included in content for broader model compatibility
await self._handle_message(
sender_id=sender_id,
chat_id=chat_id,
content=content,
media=None,
metadata={
"message_id": msg_id,
"msg_type": msg_type,
"chat_type": chat_type,
}
)
except Exception as e:
logger.error("Error processing WeCom message: {}", e)
async def _download_and_save_media(
self,
file_url: str,
aes_key: str,
media_type: str,
filename: str | None = None,
) -> str | None:
"""
Download and decrypt media from WeCom.
Returns:
file_path or None if download failed
"""
try:
data, fname = await self._client.download_file(file_url, aes_key)
if not data:
logger.warning("Failed to download media from WeCom")
return None
media_dir = get_media_dir("wecom")
if not filename:
filename = fname or f"{media_type}_{hash(file_url) % 100000}"
filename = os.path.basename(filename)
file_path = media_dir / filename
file_path.write_bytes(data)
logger.debug("Downloaded {} to {}", media_type, file_path)
return str(file_path)
except Exception as e:
logger.error("Error downloading media: {}", e)
return None
async def send(self, msg: OutboundMessage) -> None:
"""Send a message through WeCom."""
if not self._client:
logger.warning("WeCom client not initialized")
return
try:
content = msg.content.strip()
if not content:
return
# Get the stored frame for this chat
frame = self._chat_frames.get(msg.chat_id)
if not frame:
logger.warning("No frame found for chat {}, cannot reply", msg.chat_id)
return
# Use streaming reply for better UX
stream_id = self._generate_req_id("stream")
# Send as streaming message with finish=True
await self._client.reply_stream(
frame,
stream_id,
content,
finish=True,
)
logger.debug("WeCom message sent to {}", msg.chat_id)
except Exception as e:
logger.error("Error sending WeCom message: {}", e)

1
nanobot/channels/whatsapp.py
View File

@@ -22,6 +22,7 @@ class WhatsAppChannel(BaseChannel):
"""
name = "whatsapp"
display_name = "WhatsApp"
def __init__(self, config: WhatsAppConfig, bus: MessageBus):
super().__init__(config, bus)

199
nanobot/cli/commands.py
View File

@@ -19,10 +19,12 @@ if sys.platform == "win32":
pass
import typer
from prompt_toolkit import print_formatted_text
from prompt_toolkit import PromptSession
from prompt_toolkit.formatted_text import HTML
from prompt_toolkit.formatted_text import ANSI, HTML
from prompt_toolkit.history import FileHistory
from prompt_toolkit.patch_stdout import patch_stdout
from prompt_toolkit.application import run_in_terminal
from rich.console import Console
from rich.markdown import Markdown
from rich.table import Table
@@ -111,8 +113,25 @@ def _init_prompt_session() -> None:
)
def _make_console() -> Console:
return Console(file=sys.stdout)
def _render_interactive_ansi(render_fn) -> str:
"""Render Rich output to ANSI so prompt_toolkit can print it safely."""
ansi_console = Console(
force_terminal=True,
color_system=console.color_system or "standard",
width=console.width,
)
with ansi_console.capture() as capture:
render_fn(ansi_console)
return capture.get()
def _print_agent_response(response: str, render_markdown: bool) -> None:
"""Render assistant response with consistent terminal styling."""
console = _make_console()
content = response or ""
body = Markdown(content) if render_markdown else Text(content)
console.print()
@@ -121,6 +140,34 @@ def _print_agent_response(response: str, render_markdown: bool) -> None:
console.print()
async def _print_interactive_line(text: str) -> None:
"""Print async interactive updates with prompt_toolkit-safe Rich styling."""
def _write() -> None:
ansi = _render_interactive_ansi(
lambda c: c.print(f" [dim]↳ {text}[/dim]")
)
print_formatted_text(ANSI(ansi), end="")
await run_in_terminal(_write)
async def _print_interactive_response(response: str, render_markdown: bool) -> None:
"""Print async interactive replies with prompt_toolkit-safe Rich styling."""
def _write() -> None:
content = response or ""
ansi = _render_interactive_ansi(
lambda c: (
c.print(),
c.print(f"[cyan]{__logo__} nanobot[/cyan]"),
c.print(Markdown(content) if render_markdown else Text(content)),
c.print(),
)
)
print_formatted_text(ANSI(ansi), end="")
await run_in_terminal(_write)
def _is_exit_command(command: str) -> bool:
"""Return True when input should end interactive chat."""
return command.lower() in EXIT_COMMANDS
@@ -191,6 +238,8 @@ def onboard():
save_config(Config())
console.print(f"[green]✓[/green] Created config at {config_path}")
console.print("[dim]Config template now uses `maxTokens` + `contextWindowTokens`; `memoryWindow` is no longer a runtime setting.[/dim]")
# Create workspace
workspace = get_workspace_path()
@@ -213,6 +262,7 @@ def onboard():
def _make_provider(config: Config):
"""Create the appropriate LLM provider from config."""
from nanobot.providers.base import GenerationSettings
from nanobot.providers.openai_codex_provider import OpenAICodexProvider
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
@@ -222,40 +272,36 @@ def _make_provider(config: Config):
# OpenAI Codex (OAuth)
if provider_name == "openai_codex" or model.startswith("openai-codex/"):
return OpenAICodexProvider(default_model=model)
provider = OpenAICodexProvider(default_model=model)
# Custom: direct OpenAI-compatible endpoint, bypasses LiteLLM
elif provider_name == "custom":
from nanobot.providers.custom_provider import CustomProvider
if provider_name == "custom":
return CustomProvider(
provider = CustomProvider(
api_key=p.api_key if p else "no-key",
api_base=config.get_api_base(model) or "http://localhost:8000/v1",
default_model=model,
)
# Azure OpenAI: direct Azure OpenAI endpoint with deployment name
if provider_name == "azure_openai":
elif provider_name == "azure_openai":
if not p or not p.api_key or not p.api_base:
console.print("[red]Error: Azure OpenAI requires api_key and api_base.[/red]")
console.print("Set them in ~/.nanobot/config.json under providers.azure_openai section")
console.print("Use the model field to specify the deployment name.")
raise typer.Exit(1)
return AzureOpenAIProvider(
provider = AzureOpenAIProvider(
api_key=p.api_key,
api_base=p.api_base,
default_model=model,
)
else:
from nanobot.providers.litellm_provider import LiteLLMProvider
from nanobot.providers.registry import find_by_name
spec = find_by_name(provider_name)
if not model.startswith("bedrock/") and not (p and p.api_key) and not (spec and spec.is_oauth):
if not model.startswith("bedrock/") and not (p and p.api_key) and not (spec and (spec.is_oauth or spec.is_local)):
console.print("[red]Error: No API key configured.[/red]")
console.print("Set one in ~/.nanobot/config.json under providers section")
raise typer.Exit(1)
return LiteLLMProvider(
provider = LiteLLMProvider(
api_key=p.api_key if p else None,
api_base=config.get_api_base(model),
default_model=model,
@@ -263,6 +309,14 @@ def _make_provider(config: Config):
provider_name=provider_name,
)
defaults = config.agents.defaults
provider.generation = GenerationSettings(
temperature=defaults.temperature,
max_tokens=defaults.max_tokens,
reasoning_effort=defaults.reasoning_effort,
)
return provider
def _load_runtime_config(config: str | None = None, workspace: str | None = None) -> Config:
"""Load config and optionally override the active workspace."""
@@ -283,6 +337,16 @@ def _load_runtime_config(config: str | None = None, workspace: str | None = None
return loaded
def _print_deprecated_memory_window_notice(config: Config) -> None:
"""Warn when running with old memoryWindow-only config."""
if config.agents.defaults.should_warn_deprecated_memory_window:
console.print(
"[yellow]Hint:[/yellow] Detected deprecated `memoryWindow` without "
"`contextWindowTokens`. `memoryWindow` is ignored; run "
"[cyan]nanobot onboard[/cyan] to refresh your config template."
)
# ============================================================================
# Gateway / Server
# ============================================================================
@@ -290,7 +354,7 @@ def _load_runtime_config(config: str | None = None, workspace: str | None = None
@app.command()
def gateway(
port: int = typer.Option(18790, "--port", "-p", help="Gateway port"),
port: int | None = typer.Option(None, "--port", "-p", help="Gateway port"),
workspace: str | None = typer.Option(None, "--workspace", "-w", help="Workspace directory"),
verbose: bool = typer.Option(False, "--verbose", "-v", help="Verbose output"),
config: str | None = typer.Option(None, "--config", "-c", help="Path to config file"),
@@ -310,6 +374,8 @@ def gateway(
logging.basicConfig(level=logging.DEBUG)
config = _load_runtime_config(config, workspace)
_print_deprecated_memory_window_notice(config)
port = port if port is not None else config.gateway.port
console.print(f"{__logo__} Starting nanobot gateway on port {port}...")
sync_workspace_templates(config.workspace_path)
@@ -327,11 +393,8 @@ def gateway(
provider=provider,
workspace=config.workspace_path,
model=config.agents.defaults.model,
temperature=config.agents.defaults.temperature,
max_tokens=config.agents.defaults.max_tokens,
max_iterations=config.agents.defaults.max_tool_iterations,
memory_window=config.agents.defaults.memory_window,
reasoning_effort=config.agents.defaults.reasoning_effort,
context_window_tokens=config.agents.defaults.context_window_tokens,
brave_api_key=config.tools.web.search.api_key or None,
web_proxy=config.tools.web.proxy or None,
exec_config=config.tools.exec,
@@ -493,6 +556,7 @@ def agent(
from nanobot.cron.service import CronService
config = _load_runtime_config(config, workspace)
_print_deprecated_memory_window_notice(config)
sync_workspace_templates(config.workspace_path)
bus = MessageBus()
@@ -512,11 +576,8 @@ def agent(
provider=provider,
workspace=config.workspace_path,
model=config.agents.defaults.model,
temperature=config.agents.defaults.temperature,
max_tokens=config.agents.defaults.max_tokens,
max_iterations=config.agents.defaults.max_tool_iterations,
memory_window=config.agents.defaults.memory_window,
reasoning_effort=config.agents.defaults.reasoning_effort,
context_window_tokens=config.agents.defaults.context_window_tokens,
brave_api_key=config.tools.web.search.api_key or None,
web_proxy=config.tools.web.proxy or None,
exec_config=config.tools.exec,
@@ -596,14 +657,15 @@ def agent(
elif ch and not is_tool_hint and not ch.send_progress:
pass
else:
console.print(f" [dim]↳ {msg.content}[/dim]")
await _print_interactive_line(msg.content)
elif not turn_done.is_set():
if msg.content:
turn_response.append(msg.content)
turn_done.set()
elif msg.content:
console.print()
_print_agent_response(msg.content, render_markdown=markdown)
await _print_interactive_response(msg.content, render_markdown=markdown)
except asyncio.TimeoutError:
continue
except asyncio.CancelledError:
@@ -669,6 +731,7 @@ app.add_typer(channels_app, name="channels")
@channels_app.command("status")
def channels_status():
"""Show channel status."""
from nanobot.channels.registry import discover_channel_names, load_channel_class
from nanobot.config.loader import load_config
config = load_config()
@@ -676,84 +739,18 @@ def channels_status():
table = Table(title="Channel Status")
table.add_column("Channel", style="cyan")
table.add_column("Enabled", style="green")
table.add_column("Configuration", style="yellow")
# WhatsApp
wa = config.channels.whatsapp
for modname in sorted(discover_channel_names()):
section = getattr(config.channels, modname, None)
enabled = section and getattr(section, "enabled", False)
try:
cls = load_channel_class(modname)
display = cls.display_name
except ImportError:
display = modname.title()
table.add_row(
"WhatsApp",
"✓" if wa.enabled else "✗",
wa.bridge_url
)
dc = config.channels.discord
table.add_row(
"Discord",
"✓" if dc.enabled else "✗",
dc.gateway_url
)
# Feishu
fs = config.channels.feishu
fs_config = f"app_id: {fs.app_id[:10]}..." if fs.app_id else "[dim]not configured[/dim]"
table.add_row(
"Feishu",
"✓" if fs.enabled else "✗",
fs_config
)
# Mochat
mc = config.channels.mochat
mc_base = mc.base_url or "[dim]not configured[/dim]"
table.add_row(
"Mochat",
"✓" if mc.enabled else "✗",
mc_base
)
# Telegram
tg = config.channels.telegram
tg_config = f"token: {tg.token[:10]}..." if tg.token else "[dim]not configured[/dim]"
table.add_row(
"Telegram",
"✓" if tg.enabled else "✗",
tg_config
)
# Slack
slack = config.channels.slack
slack_config = "socket" if slack.app_token and slack.bot_token else "[dim]not configured[/dim]"
table.add_row(
"Slack",
"✓" if slack.enabled else "✗",
slack_config
)
# DingTalk
dt = config.channels.dingtalk
dt_config = f"client_id: {dt.client_id[:10]}..." if dt.client_id else "[dim]not configured[/dim]"
table.add_row(
"DingTalk",
"✓" if dt.enabled else "✗",
dt_config
)
# QQ
qq = config.channels.qq
qq_config = f"app_id: {qq.app_id[:10]}..." if qq.app_id else "[dim]not configured[/dim]"
table.add_row(
"QQ",
"✓" if qq.enabled else "✗",
qq_config
)
# Email
em = config.channels.email
em_config = em.imap_host if em.imap_host else "[dim]not configured[/dim]"
table.add_row(
"Email",
"✓" if em.enabled else "✗",
em_config
display,
"[green]\u2713[/green]" if enabled else "[dim]\u2717[/dim]",
)
console.print(table)

52
nanobot/config/schema.py
View File

@@ -33,6 +33,7 @@ class TelegramConfig(Base):
None # HTTP/SOCKS5 proxy URL, e.g. "http://127.0.0.1:7890" or "socks5://127.0.0.1:1080"
)
reply_to_message: bool = False # If true, bot replies quote the original message
group_policy: Literal["open", "mention"] = "mention" # "mention" responds when @mentioned or replied to, "open" responds to all
class FeishuConfig(Base):
@@ -47,6 +48,7 @@ class FeishuConfig(Base):
react_emoji: str = (
"THUMBSUP" # Emoji type for message reactions (e.g. THUMBSUP, OK, DONE, SMILE)
)
group_policy: Literal["open", "mention"] = "mention" # "mention" responds when @mentioned, "open" responds to all
class DingTalkConfig(Base):
@@ -199,6 +201,14 @@ class QQConfig(Base):
) # Allowed user openids (empty = public access)
class WecomConfig(Base):
"""WeCom (Enterprise WeChat) AI Bot channel configuration."""
enabled: bool = False
bot_id: str = "" # Bot ID from WeCom AI Bot platform
secret: str = "" # Bot Secret from WeCom AI Bot platform
allow_from: list[str] = Field(default_factory=list) # Allowed user IDs
welcome_message: str = "" # Welcome message for enter_chat event
class ChannelsConfig(Base):
@@ -216,6 +226,7 @@ class ChannelsConfig(Base):
slack: SlackConfig = Field(default_factory=SlackConfig)
qq: QQConfig = Field(default_factory=QQConfig)
matrix: MatrixConfig = Field(default_factory=MatrixConfig)
wecom: WecomConfig = Field(default_factory=WecomConfig)
class AgentDefaults(Base):
@@ -227,11 +238,18 @@ class AgentDefaults(Base):
"auto" # Provider name (e.g. "anthropic", "openrouter") or "auto" for auto-detection
)
max_tokens: int = 8192
context_window_tokens: int = 65_536
temperature: float = 0.1
max_tool_iterations: int = 40
memory_window: int = 100
# Deprecated compatibility field: accepted from old configs but ignored at runtime.
memory_window: int | None = Field(default=None, exclude=True)
reasoning_effort: str | None = None # low / medium / high — enables LLM thinking mode
@property
def should_warn_deprecated_memory_window(self) -> bool:
"""Return True when old memoryWindow is present without contextWindowTokens."""
return self.memory_window is not None and "context_window_tokens" not in self.model_fields_set
class AgentsConfig(Base):
"""Agent configuration."""
@@ -258,14 +276,18 @@ class ProvidersConfig(Base):
deepseek: ProviderConfig = Field(default_factory=ProviderConfig)
groq: ProviderConfig = Field(default_factory=ProviderConfig)
zhipu: ProviderConfig = Field(default_factory=ProviderConfig)
dashscope: ProviderConfig = Field(default_factory=ProviderConfig) # 阿里云通义千问
dashscope: ProviderConfig = Field(default_factory=ProviderConfig)
vllm: ProviderConfig = Field(default_factory=ProviderConfig)
ollama: ProviderConfig = Field(default_factory=ProviderConfig) # Ollama local models
gemini: ProviderConfig = Field(default_factory=ProviderConfig)
moonshot: ProviderConfig = Field(default_factory=ProviderConfig)
minimax: ProviderConfig = Field(default_factory=ProviderConfig)
aihubmix: ProviderConfig = Field(default_factory=ProviderConfig) # AiHubMix API gateway
siliconflow: ProviderConfig = Field(default_factory=ProviderConfig) # SiliconFlow (硅基流动)
volcengine: ProviderConfig = Field(default_factory=ProviderConfig) # VolcEngine (火山引擎)
volcengine_coding_plan: ProviderConfig = Field(default_factory=ProviderConfig) # VolcEngine Coding Plan
byteplus: ProviderConfig = Field(default_factory=ProviderConfig) # BytePlus (VolcEngine international)
byteplus_coding_plan: ProviderConfig = Field(default_factory=ProviderConfig) # BytePlus Coding Plan
openai_codex: ProviderConfig = Field(default_factory=ProviderConfig) # OpenAI Codex (OAuth)
github_copilot: ProviderConfig = Field(default_factory=ProviderConfig) # Github Copilot (OAuth)
@@ -367,16 +389,34 @@ class Config(BaseSettings):
for spec in PROVIDERS:
p = getattr(self.providers, spec.name, None)
if p and model_prefix and normalized_prefix == spec.name:
if spec.is_oauth or p.api_key:
if spec.is_oauth or spec.is_local or p.api_key:
return p, spec.name
# Match by keyword (order follows PROVIDERS registry)
for spec in PROVIDERS:
p = getattr(self.providers, spec.name, None)
if p and any(_kw_matches(kw) for kw in spec.keywords):
if spec.is_oauth or p.api_key:
if spec.is_oauth or spec.is_local or p.api_key:
return p, spec.name
# Fallback: configured local providers can route models without
# provider-specific keywords (for example plain "llama3.2" on Ollama).
# Prefer providers whose detect_by_base_keyword matches the configured api_base
# (e.g. Ollama's "11434" in "http://localhost:11434") over plain registry order.
local_fallback: tuple[ProviderConfig, str] | None = None
for spec in PROVIDERS:
if not spec.is_local:
continue
p = getattr(self.providers, spec.name, None)
if not (p and p.api_base):
continue
if spec.detect_by_base_keyword and spec.detect_by_base_keyword in p.api_base:
return p, spec.name
if local_fallback is None:
local_fallback = (p, spec.name)
if local_fallback:
return local_fallback
# Fallback: gateways first, then others (follows registry order)
# OAuth providers are NOT valid fallbacks — they require explicit model selection
for spec in PROVIDERS:
@@ -403,7 +443,7 @@ class Config(BaseSettings):
return p.api_key if p else None
def get_api_base(self, model: str | None = None) -> str | None:
"""Get API base URL for the given model. Applies default URLs for known gateways."""
"""Get API base URL for the given model. Applies default URLs for gateway/local providers."""
from nanobot.providers.registry import find_by_name
p, name = self._match_provider(model)
@@ -414,7 +454,7 @@ class Config(BaseSettings):
# to avoid polluting the global litellm.api_base.
if name:
spec = find_by_name(name)
if spec and spec.is_gateway and spec.default_api_base:
if spec and (spec.is_gateway or spec.is_local) and spec.default_api_base:
return spec.default_api_base
return None

2
nanobot/heartbeat/service.py
View File

@@ -87,7 +87,7 @@ class HeartbeatService:
Returns (action, tasks) where action is 'skip' or 'run'.
"""
response = await self.provider.chat(
response = await self.provider.chat_with_retry(
messages=[
{"role": "system", "content": "You are a heartbeat agent. Call the heartbeat tool to report your decision."},
{"role": "user", "content": (

7
nanobot/providers/azure_openai_provider.py
View File

@@ -88,6 +88,7 @@ class AzureOpenAIProvider(LLMProvider):
max_tokens: int = 4096,
temperature: float = 0.7,
reasoning_effort: str | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> dict[str, Any]:
"""Prepare the request payload with Azure OpenAI 2024-10-21 compliance."""
payload: dict[str, Any] = {
@@ -106,7 +107,7 @@ class AzureOpenAIProvider(LLMProvider):
if tools:
payload["tools"] = tools
payload["tool_choice"] = "auto"
payload["tool_choice"] = tool_choice or "auto"
return payload
@@ -118,6 +119,7 @@ class AzureOpenAIProvider(LLMProvider):
max_tokens: int = 4096,
temperature: float = 0.7,
reasoning_effort: str | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> LLMResponse:
"""
Send a chat completion request to Azure OpenAI.
@@ -137,7 +139,8 @@ class AzureOpenAIProvider(LLMProvider):
url = self._build_chat_url(deployment_name)
headers = self._build_headers()
payload = self._prepare_request_payload(
deployment_name, messages, tools, max_tokens, temperature, reasoning_effort
deployment_name, messages, tools, max_tokens, temperature, reasoning_effort,
tool_choice=tool_choice,
)
try:

138
nanobot/providers/base.py
View File

@@ -1,9 +1,13 @@
"""Base LLM provider interface."""
import asyncio
import json
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any
from loguru import logger
@dataclass
class ToolCallRequest:
@@ -11,6 +15,24 @@ class ToolCallRequest:
id: str
name: str
arguments: dict[str, Any]
provider_specific_fields: dict[str, Any] | None = None
function_provider_specific_fields: dict[str, Any] | None = None
def to_openai_tool_call(self) -> dict[str, Any]:
"""Serialize to an OpenAI-style tool_call payload."""
tool_call = {
"id": self.id,
"type": "function",
"function": {
"name": self.name,
"arguments": json.dumps(self.arguments, ensure_ascii=False),
},
}
if self.provider_specific_fields:
tool_call["provider_specific_fields"] = self.provider_specific_fields
if self.function_provider_specific_fields:
tool_call["function"]["provider_specific_fields"] = self.function_provider_specific_fields
return tool_call
@dataclass
@@ -29,6 +51,21 @@ class LLMResponse:
return len(self.tool_calls) > 0
@dataclass(frozen=True)
class GenerationSettings:
"""Default generation parameters for LLM calls.
Stored on the provider so every call site inherits the same defaults
without having to pass temperature / max_tokens / reasoning_effort
through every layer. Individual call sites can still override by
passing explicit keyword arguments to chat() / chat_with_retry().
"""
temperature: float = 0.7
max_tokens: int = 4096
reasoning_effort: str | None = None
class LLMProvider(ABC):
"""
Abstract base class for LLM providers.
@@ -37,9 +74,28 @@ class LLMProvider(ABC):
while maintaining a consistent interface.
"""
_CHAT_RETRY_DELAYS = (1, 2, 4)
_TRANSIENT_ERROR_MARKERS = (
"429",
"rate limit",
"500",
"502",
"503",
"504",
"overloaded",
"timeout",
"timed out",
"connection",
"server error",
"temporarily unavailable",
)
_SENTINEL = object()
def __init__(self, api_key: str | None = None, api_base: str | None = None):
self.api_key = api_key
self.api_base = api_base
self.generation: GenerationSettings = GenerationSettings()
@staticmethod
def _sanitize_empty_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
@@ -110,6 +166,7 @@ class LLMProvider(ABC):
max_tokens: int = 4096,
temperature: float = 0.7,
reasoning_effort: str | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> LLMResponse:
"""
Send a chat completion request.
@@ -120,12 +177,93 @@ class LLMProvider(ABC):
model: Model identifier (provider-specific).
max_tokens: Maximum tokens in response.
temperature: Sampling temperature.
tool_choice: Tool selection strategy ("auto", "required", or specific tool dict).
Returns:
LLMResponse with content and/or tool calls.
"""
pass
@classmethod
def _is_transient_error(cls, content: str | None) -> bool:
err = (content or "").lower()
return any(marker in err for marker in cls._TRANSIENT_ERROR_MARKERS)
async def chat_with_retry(
self,
messages: list[dict[str, Any]],
tools: list[dict[str, Any]] | None = None,
model: str | None = None,
max_tokens: object = _SENTINEL,
temperature: object = _SENTINEL,
reasoning_effort: object = _SENTINEL,
tool_choice: str | dict[str, Any] | None = None,
) -> LLMResponse:
"""Call chat() with retry on transient provider failures.
Parameters default to ``self.generation`` when not explicitly passed,
so callers no longer need to thread temperature / max_tokens /
reasoning_effort through every layer.
"""
if max_tokens is self._SENTINEL:
max_tokens = self.generation.max_tokens
if temperature is self._SENTINEL:
temperature = self.generation.temperature
if reasoning_effort is self._SENTINEL:
reasoning_effort = self.generation.reasoning_effort
for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
try:
response = await self.chat(
messages=messages,
tools=tools,
model=model,
max_tokens=max_tokens,
temperature=temperature,
reasoning_effort=reasoning_effort,
tool_choice=tool_choice,
)
except asyncio.CancelledError:
raise
except Exception as exc:
response = LLMResponse(
content=f"Error calling LLM: {exc}",
finish_reason="error",
)
if response.finish_reason != "error":
return response
if not self._is_transient_error(response.content):
return response
err = (response.content or "").lower()
logger.warning(
"LLM transient error (attempt {}/{}), retrying in {}s: {}",
attempt,
len(self._CHAT_RETRY_DELAYS),
delay,
err[:120],
)
await asyncio.sleep(delay)
try:
return await self.chat(
messages=messages,
tools=tools,
model=model,
max_tokens=max_tokens,
temperature=temperature,
reasoning_effort=reasoning_effort,
tool_choice=tool_choice,
)
except asyncio.CancelledError:
raise
except Exception as exc:
return LLMResponse(
content=f"Error calling LLM: {exc}",
finish_reason="error",
)
@abstractmethod
def get_default_model(self) -> str:
"""Get the default model for this provider."""

5
nanobot/providers/custom_provider.py
View File

@@ -25,7 +25,8 @@ class CustomProvider(LLMProvider):
async def chat(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None,
model: str | None = None, max_tokens: int = 4096, temperature: float = 0.7,
reasoning_effort: str | None = None) -> LLMResponse:
reasoning_effort: str | None = None,
tool_choice: str | dict[str, Any] | None = None) -> LLMResponse:
kwargs: dict[str, Any] = {
"model": model or self.default_model,
"messages": self._sanitize_empty_content(messages),
@@ -35,7 +36,7 @@ class CustomProvider(LLMProvider):
if reasoning_effort:
kwargs["reasoning_effort"] = reasoning_effort
if tools:
kwargs.update(tools=tools, tool_choice="auto")
kwargs.update(tools=tools, tool_choice=tool_choice or "auto")
try:
return self._parse(await self._client.chat.completions.create(**kwargs))
except Exception as e:

10
nanobot/providers/litellm_provider.py
View File

@@ -214,6 +214,7 @@ class LiteLLMProvider(LLMProvider):
max_tokens: int = 4096,
temperature: float = 0.7,
reasoning_effort: str | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> LLMResponse:
"""
Send a chat completion request via LiteLLM.
@@ -267,7 +268,7 @@ class LiteLLMProvider(LLMProvider):
if tools:
kwargs["tools"] = tools
kwargs["tool_choice"] = "auto"
kwargs["tool_choice"] = tool_choice or "auto"
try:
response = await acompletion(**kwargs)
@@ -309,10 +310,17 @@ class LiteLLMProvider(LLMProvider):
if isinstance(args, str):
args = json_repair.loads(args)
provider_specific_fields = getattr(tc, "provider_specific_fields", None) or None
function_provider_specific_fields = (
getattr(tc.function, "provider_specific_fields", None) or None
)
tool_calls.append(ToolCallRequest(
id=_short_tool_id(),
name=tc.function.name,
arguments=args,
provider_specific_fields=provider_specific_fields,
function_provider_specific_fields=function_provider_specific_fields,
))
usage = {}

3
nanobot/providers/openai_codex_provider.py
View File

@@ -32,6 +32,7 @@ class OpenAICodexProvider(LLMProvider):
max_tokens: int = 4096,
temperature: float = 0.7,
reasoning_effort: str | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> LLMResponse:
model = model or self.default_model
system_prompt, input_items = _convert_messages(messages)
@@ -48,7 +49,7 @@ class OpenAICodexProvider(LLMProvider):
"text": {"verbosity": "medium"},
"include": ["reasoning.encrypted_content"],
"prompt_cache_key": _prompt_cache_key(messages),
"tool_choice": "auto",
"tool_choice": tool_choice or "auto",
"parallel_tool_calls": True,
}

76
nanobot/providers/registry.py
View File

@@ -145,7 +145,8 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# VolcEngine (火山引擎): OpenAI-compatible gateway
# VolcEngine (火山引擎): OpenAI-compatible gateway, pay-per-use models
ProviderSpec(
name="volcengine",
keywords=("volcengine", "volces", "ark"),
@@ -162,6 +163,62 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# VolcEngine Coding Plan (火山引擎 Coding Plan): same key as volcengine
ProviderSpec(
name="volcengine_coding_plan",
keywords=("volcengine-plan",),
env_key="OPENAI_API_KEY",
display_name="VolcEngine Coding Plan",
litellm_prefix="volcengine",
skip_prefixes=(),
env_extras=(),
is_gateway=True,
is_local=False,
detect_by_key_prefix="",
detect_by_base_keyword="",
default_api_base="https://ark.cn-beijing.volces.com/api/coding/v3",
strip_model_prefix=True,
model_overrides=(),
),
# BytePlus: VolcEngine international, pay-per-use models
ProviderSpec(
name="byteplus",
keywords=("byteplus",),
env_key="OPENAI_API_KEY",
display_name="BytePlus",
litellm_prefix="volcengine",
skip_prefixes=(),
env_extras=(),
is_gateway=True,
is_local=False,
detect_by_key_prefix="",
detect_by_base_keyword="bytepluses",
default_api_base="https://ark.ap-southeast.bytepluses.com/api/v3",
strip_model_prefix=True,
model_overrides=(),
),
# BytePlus Coding Plan: same key as byteplus
ProviderSpec(
name="byteplus_coding_plan",
keywords=("byteplus-plan",),
env_key="OPENAI_API_KEY",
display_name="BytePlus Coding Plan",
litellm_prefix="volcengine",
skip_prefixes=(),
env_extras=(),
is_gateway=True,
is_local=False,
detect_by_key_prefix="",
detect_by_base_keyword="",
default_api_base="https://ark.ap-southeast.bytepluses.com/api/coding/v3",
strip_model_prefix=True,
model_overrides=(),
),
# === Standard providers (matched by model-name keywords) ===============
# Anthropic: LiteLLM recognizes "claude-*" natively, no prefix needed.
ProviderSpec(
@@ -360,6 +417,23 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# === Ollama (local, OpenAI-compatible) ===================================
ProviderSpec(
name="ollama",
keywords=("ollama", "nemotron"),
env_key="OLLAMA_API_KEY",
display_name="Ollama",
litellm_prefix="ollama_chat", # model → ollama_chat/model
skip_prefixes=("ollama/", "ollama_chat/"),
env_extras=(),
is_gateway=False,
is_local=True,
detect_by_key_prefix="",
detect_by_base_keyword="11434",
default_api_base="http://localhost:11434",
strip_model_prefix=False,
model_overrides=(),
),
# === Auxiliary (not a primary LLM provider) ============================
# Groq: mainly used for Whisper voice transcription, also usable for LLM.
# Needs "groq/" prefix for LiteLLM routing. Placed last — it rarely wins fallback.

13
nanobot/skills/skill-creator/SKILL.md
View File

@@ -268,6 +268,8 @@ Skip this step only if the skill being developed already exists, and iteration o
When creating a new skill from scratch, always run the `init_skill.py` script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
For `nanobot`, custom skills should live under the active workspace `skills/` directory so they can be discovered automatically at runtime (for example, `<workspace>/skills/my-skill/SKILL.md`).
Usage:
```bash
@@ -277,9 +279,9 @@ scripts/init_skill.py <skill-name> --path <output-directory> [--resources script
Examples:
```bash
scripts/init_skill.py my-skill --path skills/public
scripts/init_skill.py my-skill --path skills/public --resources scripts,references
scripts/init_skill.py my-skill --path skills/public --resources scripts --examples
scripts/init_skill.py my-skill --path ./workspace/skills
scripts/init_skill.py my-skill --path ./workspace/skills --resources scripts,references
scripts/init_skill.py my-skill --path ./workspace/skills --resources scripts --examples
```
The script:
@@ -326,7 +328,7 @@ Write the YAML frontmatter with `name` and `description`:
- Include all "when to use" information here - Not in the body. The body is only loaded after triggering, so "When to Use This Skill" sections in the body are not helpful to the agent.
- Example description for a `docx` skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when the agent needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
Do not include any other fields in YAML frontmatter.
Keep frontmatter minimal. In `nanobot`, `metadata` and `always` are also supported when needed, but avoid adding extra fields unless they are actually required.
##### Body
@@ -349,7 +351,6 @@ scripts/package_skill.py <path/to/skill-folder> ./dist
The packaging script will:
1. **Validate** the skill automatically, checking:
- YAML frontmatter format and required fields
- Skill naming conventions and directory structure
- Description completeness and quality
@@ -357,6 +358,8 @@ The packaging script will:
2. **Package** the skill if validation passes, creating a .skill file named after the skill (e.g., `my-skill.skill`) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
Security restriction: symlinks are rejected and packaging fails when any symlink is present.
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
### Step 6: Iterate

378
nanobot/skills/skill-creator/scripts/init_skill.py Executable file
View File

@@ -0,0 +1,378 @@
#!/usr/bin/env python3
"""
Skill Initializer - Creates a new skill from template
Usage:
init_skill.py <skill-name> --path <path> [--resources scripts,references,assets] [--examples]
Examples:
init_skill.py my-new-skill --path skills/public
init_skill.py my-new-skill --path skills/public --resources scripts,references
init_skill.py my-api-helper --path skills/private --resources scripts --examples
init_skill.py custom-skill --path /custom/location
"""
import argparse
import re
import sys
from pathlib import Path
MAX_SKILL_NAME_LENGTH = 64
ALLOWED_RESOURCES = {"scripts", "references", "assets"}
SKILL_TEMPLATE = """---
name: {skill_name}
description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]
---
# {skill_title}
## Overview
[TODO: 1-2 sentences explaining what this skill enables]
## Structuring This Skill
[TODO: Choose the structure that best fits this skill's purpose. Common patterns:
**1. Workflow-Based** (best for sequential processes)
- Works well when there are clear step-by-step procedures
- Example: DOCX skill with "Workflow Decision Tree" -> "Reading" -> "Creating" -> "Editing"
- Structure: ## Overview -> ## Workflow Decision Tree -> ## Step 1 -> ## Step 2...
**2. Task-Based** (best for tool collections)
- Works well when the skill offers different operations/capabilities
- Example: PDF skill with "Quick Start" -> "Merge PDFs" -> "Split PDFs" -> "Extract Text"
- Structure: ## Overview -> ## Quick Start -> ## Task Category 1 -> ## Task Category 2...
**3. Reference/Guidelines** (best for standards or specifications)
- Works well for brand guidelines, coding standards, or requirements
- Example: Brand styling with "Brand Guidelines" -> "Colors" -> "Typography" -> "Features"
- Structure: ## Overview -> ## Guidelines -> ## Specifications -> ## Usage...
**4. Capabilities-Based** (best for integrated systems)
- Works well when the skill provides multiple interrelated features
- Example: Product Management with "Core Capabilities" -> numbered capability list
- Structure: ## Overview -> ## Core Capabilities -> ### 1. Feature -> ### 2. Feature...
Patterns can be mixed and matched as needed. Most skills combine patterns (e.g., start with task-based, add workflow for complex operations).
Delete this entire "Structuring This Skill" section when done - it's just guidance.]
## [TODO: Replace with the first main section based on chosen structure]
[TODO: Add content here. See examples in existing skills:
- Code samples for technical skills
- Decision trees for complex workflows
- Concrete examples with realistic user requests
- References to scripts/templates/references as needed]
## Resources (optional)
Create only the resource directories this skill actually needs. Delete this section if no resources are required.
### scripts/
Executable code (Python/Bash/etc.) that can be run directly to perform specific operations.
**Examples from other skills:**
- PDF skill: `fill_fillable_fields.py`, `extract_form_field_info.py` - utilities for PDF manipulation
- DOCX skill: `document.py`, `utilities.py` - Python modules for document processing
**Appropriate for:** Python scripts, shell scripts, or any executable code that performs automation, data processing, or specific operations.
**Note:** Scripts may be executed without loading into context, but can still be read by Codex for patching or environment adjustments.
### references/
Documentation and reference material intended to be loaded into context to inform Codex's process and thinking.
**Examples from other skills:**
- Product management: `communication.md`, `context_building.md` - detailed workflow guides
- BigQuery: API reference documentation and query examples
- Finance: Schema documentation, company policies
**Appropriate for:** In-depth documentation, API references, database schemas, comprehensive guides, or any detailed information that Codex should reference while working.
### assets/
Files not intended to be loaded into context, but rather used within the output Codex produces.
**Examples from other skills:**
- Brand styling: PowerPoint template files (.pptx), logo files
- Frontend builder: HTML/React boilerplate project directories
- Typography: Font files (.ttf, .woff2)
**Appropriate for:** Templates, boilerplate code, document templates, images, icons, fonts, or any files meant to be copied or used in the final output.
---
**Not every skill requires all three types of resources.**
"""
EXAMPLE_SCRIPT = '''#!/usr/bin/env python3
"""
Example helper script for {skill_name}
This is a placeholder script that can be executed directly.
Replace with actual implementation or delete if not needed.
Example real scripts from other skills:
- pdf/scripts/fill_fillable_fields.py - Fills PDF form fields
- pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
"""
def main():
print("This is an example script for {skill_name}")
# TODO: Add actual script logic here
# This could be data processing, file conversion, API calls, etc.
if __name__ == "__main__":
main()
'''
EXAMPLE_REFERENCE = """# Reference Documentation for {skill_title}
This is a placeholder for detailed reference documentation.
Replace with actual reference content or delete if not needed.
Example real reference docs from other skills:
- product-management/references/communication.md - Comprehensive guide for status updates
- product-management/references/context_building.md - Deep-dive on gathering context
- bigquery/references/ - API references and query examples
## When Reference Docs Are Useful
Reference docs are ideal for:
- Comprehensive API documentation
- Detailed workflow guides
- Complex multi-step processes
- Information too lengthy for main SKILL.md
- Content that's only needed for specific use cases
## Structure Suggestions
### API Reference Example
- Overview
- Authentication
- Endpoints with examples
- Error codes
- Rate limits
### Workflow Guide Example
- Prerequisites
- Step-by-step instructions
- Common patterns
- Troubleshooting
- Best practices
"""
EXAMPLE_ASSET = """# Example Asset File
This placeholder represents where asset files would be stored.
Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
Asset files are NOT intended to be loaded into context, but rather used within
the output Codex produces.
Example asset files from other skills:
- Brand guidelines: logo.png, slides_template.pptx
- Frontend builder: hello-world/ directory with HTML/React boilerplate
- Typography: custom-font.ttf, font-family.woff2
- Data: sample_data.csv, test_dataset.json
## Common Asset Types
- Templates: .pptx, .docx, boilerplate directories
- Images: .png, .jpg, .svg, .gif
- Fonts: .ttf, .otf, .woff, .woff2
- Boilerplate code: Project directories, starter files
- Icons: .ico, .svg
- Data files: .csv, .json, .xml, .yaml
Note: This is a text placeholder. Actual assets can be any file type.
"""
def normalize_skill_name(skill_name):
"""Normalize a skill name to lowercase hyphen-case."""
normalized = skill_name.strip().lower()
normalized = re.sub(r"[^a-z0-9]+", "-", normalized)
normalized = normalized.strip("-")
normalized = re.sub(r"-{2,}", "-", normalized)
return normalized
def title_case_skill_name(skill_name):
"""Convert hyphenated skill name to Title Case for display."""
return " ".join(word.capitalize() for word in skill_name.split("-"))
def parse_resources(raw_resources):
if not raw_resources:
return []
resources = [item.strip() for item in raw_resources.split(",") if item.strip()]
invalid = sorted({item for item in resources if item not in ALLOWED_RESOURCES})
if invalid:
allowed = ", ".join(sorted(ALLOWED_RESOURCES))
print(f"[ERROR] Unknown resource type(s): {', '.join(invalid)}")
print(f" Allowed: {allowed}")
sys.exit(1)
deduped = []
seen = set()
for resource in resources:
if resource not in seen:
deduped.append(resource)
seen.add(resource)
return deduped
def create_resource_dirs(skill_dir, skill_name, skill_title, resources, include_examples):
for resource in resources:
resource_dir = skill_dir / resource
resource_dir.mkdir(exist_ok=True)
if resource == "scripts":
if include_examples:
example_script = resource_dir / "example.py"
example_script.write_text(EXAMPLE_SCRIPT.format(skill_name=skill_name))
example_script.chmod(0o755)
print("[OK] Created scripts/example.py")
else:
print("[OK] Created scripts/")
elif resource == "references":
if include_examples:
example_reference = resource_dir / "api_reference.md"
example_reference.write_text(EXAMPLE_REFERENCE.format(skill_title=skill_title))
print("[OK] Created references/api_reference.md")
else:
print("[OK] Created references/")
elif resource == "assets":
if include_examples:
example_asset = resource_dir / "example_asset.txt"
example_asset.write_text(EXAMPLE_ASSET)
print("[OK] Created assets/example_asset.txt")
else:
print("[OK] Created assets/")
def init_skill(skill_name, path, resources, include_examples):
"""
Initialize a new skill directory with template SKILL.md.
Args:
skill_name: Name of the skill
path: Path where the skill directory should be created
resources: Resource directories to create
include_examples: Whether to create example files in resource directories
Returns:
Path to created skill directory, or None if error
"""
# Determine skill directory path
skill_dir = Path(path).resolve() / skill_name
# Check if directory already exists
if skill_dir.exists():
print(f"[ERROR] Skill directory already exists: {skill_dir}")
return None
# Create skill directory
try:
skill_dir.mkdir(parents=True, exist_ok=False)
print(f"[OK] Created skill directory: {skill_dir}")
except Exception as e:
print(f"[ERROR] Error creating directory: {e}")
return None
# Create SKILL.md from template
skill_title = title_case_skill_name(skill_name)
skill_content = SKILL_TEMPLATE.format(skill_name=skill_name, skill_title=skill_title)
skill_md_path = skill_dir / "SKILL.md"
try:
skill_md_path.write_text(skill_content)
print("[OK] Created SKILL.md")
except Exception as e:
print(f"[ERROR] Error creating SKILL.md: {e}")
return None
# Create resource directories if requested
if resources:
try:
create_resource_dirs(skill_dir, skill_name, skill_title, resources, include_examples)
except Exception as e:
print(f"[ERROR] Error creating resource directories: {e}")
return None
# Print next steps
print(f"\n[OK] Skill '{skill_name}' initialized successfully at {skill_dir}")
print("\nNext steps:")
print("1. Edit SKILL.md to complete the TODO items and update the description")
if resources:
if include_examples:
print("2. Customize or delete the example files in scripts/, references/, and assets/")
else:
print("2. Add resources to scripts/, references/, and assets/ as needed")
else:
print("2. Create resource directories only if needed (scripts/, references/, assets/)")
print("3. Run the validator when ready to check the skill structure")
return skill_dir
def main():
parser = argparse.ArgumentParser(
description="Create a new skill directory with a SKILL.md template.",
)
parser.add_argument("skill_name", help="Skill name (normalized to hyphen-case)")
parser.add_argument("--path", required=True, help="Output directory for the skill")
parser.add_argument(
"--resources",
default="",
help="Comma-separated list: scripts,references,assets",
)
parser.add_argument(
"--examples",
action="store_true",
help="Create example files inside the selected resource directories",
)
args = parser.parse_args()
raw_skill_name = args.skill_name
skill_name = normalize_skill_name(raw_skill_name)
if not skill_name:
print("[ERROR] Skill name must include at least one letter or digit.")
sys.exit(1)
if len(skill_name) > MAX_SKILL_NAME_LENGTH:
print(
f"[ERROR] Skill name '{skill_name}' is too long ({len(skill_name)} characters). "
f"Maximum is {MAX_SKILL_NAME_LENGTH} characters."
)
sys.exit(1)
if skill_name != raw_skill_name:
print(f"Note: Normalized skill name from '{raw_skill_name}' to '{skill_name}'.")
resources = parse_resources(args.resources)
if args.examples and not resources:
print("[ERROR] --examples requires --resources to be set.")
sys.exit(1)
path = args.path
print(f"Initializing skill: {skill_name}")
print(f" Location: {path}")
if resources:
print(f" Resources: {', '.join(resources)}")
if args.examples:
print(" Examples: enabled")
else:
print(" Resources: none (create as needed)")
print()
result = init_skill(skill_name, path, resources, args.examples)
if result:
sys.exit(0)
else:
sys.exit(1)
if __name__ == "__main__":
main()

154
nanobot/skills/skill-creator/scripts/package_skill.py Executable file
View File

@@ -0,0 +1,154 @@
#!/usr/bin/env python3
"""
Skill Packager - Creates a distributable .skill file of a skill folder
Usage:
python package_skill.py <path/to/skill-folder> [output-directory]
Example:
python package_skill.py skills/public/my-skill
python package_skill.py skills/public/my-skill ./dist
"""
import sys
import zipfile
from pathlib import Path
from quick_validate import validate_skill
def _is_within(path: Path, root: Path) -> bool:
try:
path.relative_to(root)
return True
except ValueError:
return False
def _cleanup_partial_archive(skill_filename: Path) -> None:
try:
if skill_filename.exists():
skill_filename.unlink()
except OSError:
pass
def package_skill(skill_path, output_dir=None):
"""
Package a skill folder into a .skill file.
Args:
skill_path: Path to the skill folder
output_dir: Optional output directory for the .skill file (defaults to current directory)
Returns:
Path to the created .skill file, or None if error
"""
skill_path = Path(skill_path).resolve()
# Validate skill folder exists
if not skill_path.exists():
print(f"[ERROR] Skill folder not found: {skill_path}")
return None
if not skill_path.is_dir():
print(f"[ERROR] Path is not a directory: {skill_path}")
return None
# Validate SKILL.md exists
skill_md = skill_path / "SKILL.md"
if not skill_md.exists():
print(f"[ERROR] SKILL.md not found in {skill_path}")
return None
# Run validation before packaging
print("Validating skill...")
valid, message = validate_skill(skill_path)
if not valid:
print(f"[ERROR] Validation failed: {message}")
print(" Please fix the validation errors before packaging.")
return None
print(f"[OK] {message}\n")
# Determine output location
skill_name = skill_path.name
if output_dir:
output_path = Path(output_dir).resolve()
output_path.mkdir(parents=True, exist_ok=True)
else:
output_path = Path.cwd()
skill_filename = output_path / f"{skill_name}.skill"
EXCLUDED_DIRS = {".git", ".svn", ".hg", "__pycache__", "node_modules"}
files_to_package = []
resolved_archive = skill_filename.resolve()
for file_path in skill_path.rglob("*"):
# Fail closed on symlinks so the packaged contents are explicit and predictable.
if file_path.is_symlink():
print(f"[ERROR] Symlink not allowed in packaged skill: {file_path}")
_cleanup_partial_archive(skill_filename)
return None
rel_parts = file_path.relative_to(skill_path).parts
if any(part in EXCLUDED_DIRS for part in rel_parts):
continue
if file_path.is_file():
resolved_file = file_path.resolve()
if not _is_within(resolved_file, skill_path):
print(f"[ERROR] File escapes skill root: {file_path}")
_cleanup_partial_archive(skill_filename)
return None
# If output lives under skill_path, avoid writing archive into itself.
if resolved_file == resolved_archive:
print(f"[WARN] Skipping output archive: {file_path}")
continue
files_to_package.append(file_path)
# Create the .skill file (zip format)
try:
with zipfile.ZipFile(skill_filename, "w", zipfile.ZIP_DEFLATED) as zipf:
for file_path in files_to_package:
# Calculate the relative path within the zip.
arcname = Path(skill_name) / file_path.relative_to(skill_path)
zipf.write(file_path, arcname)
print(f" Added: {arcname}")
print(f"\n[OK] Successfully packaged skill to: {skill_filename}")
return skill_filename
except Exception as e:
_cleanup_partial_archive(skill_filename)
print(f"[ERROR] Error creating .skill file: {e}")
return None
def main():
if len(sys.argv) < 2:
print("Usage: python package_skill.py <path/to/skill-folder> [output-directory]")
print("\nExample:")
print(" python package_skill.py skills/public/my-skill")
print(" python package_skill.py skills/public/my-skill ./dist")
sys.exit(1)
skill_path = sys.argv[1]
output_dir = sys.argv[2] if len(sys.argv) > 2 else None
print(f"Packaging skill: {skill_path}")
if output_dir:
print(f" Output directory: {output_dir}")
print()
result = package_skill(skill_path, output_dir)
if result:
sys.exit(0)
else:
sys.exit(1)
if __name__ == "__main__":
main()

213
nanobot/skills/skill-creator/scripts/quick_validate.py Normal file
View File

@@ -0,0 +1,213 @@
#!/usr/bin/env python3
"""
Minimal validator for nanobot skill folders.
"""
import re
import sys
from pathlib import Path
from typing import Optional
try:
import yaml
except ModuleNotFoundError:
yaml = None
MAX_SKILL_NAME_LENGTH = 64
ALLOWED_FRONTMATTER_KEYS = {
"name",
"description",
"metadata",
"always",
"license",
"allowed-tools",
}
ALLOWED_RESOURCE_DIRS = {"scripts", "references", "assets"}
PLACEHOLDER_MARKERS = ("[todo", "todo:")
def _extract_frontmatter(content: str) -> Optional[str]:
lines = content.splitlines()
if not lines or lines[0].strip() != "---":
return None
for i in range(1, len(lines)):
if lines[i].strip() == "---":
return "\n".join(lines[1:i])
return None
def _parse_simple_frontmatter(frontmatter_text: str) -> Optional[dict[str, str]]:
"""Fallback parser for simple frontmatter when PyYAML is unavailable."""
parsed: dict[str, str] = {}
current_key: Optional[str] = None
multiline_key: Optional[str] = None
for raw_line in frontmatter_text.splitlines():
stripped = raw_line.strip()
if not stripped or stripped.startswith("#"):
continue
is_indented = raw_line[:1].isspace()
if is_indented:
if current_key is None:
return None
current_value = parsed[current_key]
parsed[current_key] = f"{current_value}\n{stripped}" if current_value else stripped
continue
if ":" not in stripped:
return None
key, value = stripped.split(":", 1)
key = key.strip()
value = value.strip()
if not key:
return None
if value in {"|", ">"}:
parsed[key] = ""
current_key = key
multiline_key = key
continue
if (value.startswith('"') and value.endswith('"')) or (
value.startswith("'") and value.endswith("'")
):
value = value[1:-1]
parsed[key] = value
current_key = key
multiline_key = None
if multiline_key is not None and multiline_key not in parsed:
return None
return parsed
def _load_frontmatter(frontmatter_text: str) -> tuple[Optional[dict], Optional[str]]:
if yaml is not None:
try:
frontmatter = yaml.safe_load(frontmatter_text)
except yaml.YAMLError as exc:
return None, f"Invalid YAML in frontmatter: {exc}"
if not isinstance(frontmatter, dict):
return None, "Frontmatter must be a YAML dictionary"
return frontmatter, None
frontmatter = _parse_simple_frontmatter(frontmatter_text)
if frontmatter is None:
return None, "Invalid YAML in frontmatter: unsupported syntax without PyYAML installed"
return frontmatter, None
def _validate_skill_name(name: str, folder_name: str) -> Optional[str]:
if not re.fullmatch(r"[a-z0-9]+(?:-[a-z0-9]+)*", name):
return (
f"Name '{name}' should be hyphen-case "
"(lowercase letters, digits, and single hyphens only)"
)
if len(name) > MAX_SKILL_NAME_LENGTH:
return (
f"Name is too long ({len(name)} characters). "
f"Maximum is {MAX_SKILL_NAME_LENGTH} characters."
)
if name != folder_name:
return f"Skill name '{name}' must match directory name '{folder_name}'"
return None
def _validate_description(description: str) -> Optional[str]:
trimmed = description.strip()
if not trimmed:
return "Description cannot be empty"
lowered = trimmed.lower()
if any(marker in lowered for marker in PLACEHOLDER_MARKERS):
return "Description still contains TODO placeholder text"
if "<" in trimmed or ">" in trimmed:
return "Description cannot contain angle brackets (< or >)"
if len(trimmed) > 1024:
return f"Description is too long ({len(trimmed)} characters). Maximum is 1024 characters."
return None
def validate_skill(skill_path):
"""Validate a skill folder structure and required frontmatter."""
skill_path = Path(skill_path).resolve()
if not skill_path.exists():
return False, f"Skill folder not found: {skill_path}"
if not skill_path.is_dir():
return False, f"Path is not a directory: {skill_path}"
skill_md = skill_path / "SKILL.md"
if not skill_md.exists():
return False, "SKILL.md not found"
try:
content = skill_md.read_text(encoding="utf-8")
except OSError as exc:
return False, f"Could not read SKILL.md: {exc}"
frontmatter_text = _extract_frontmatter(content)
if frontmatter_text is None:
return False, "Invalid frontmatter format"
frontmatter, error = _load_frontmatter(frontmatter_text)
if error:
return False, error
unexpected_keys = sorted(set(frontmatter.keys()) - ALLOWED_FRONTMATTER_KEYS)
if unexpected_keys:
allowed = ", ".join(sorted(ALLOWED_FRONTMATTER_KEYS))
unexpected = ", ".join(unexpected_keys)
return (
False,
f"Unexpected key(s) in SKILL.md frontmatter: {unexpected}. Allowed properties are: {allowed}",
)
if "name" not in frontmatter:
return False, "Missing 'name' in frontmatter"
if "description" not in frontmatter:
return False, "Missing 'description' in frontmatter"
name = frontmatter["name"]
if not isinstance(name, str):
return False, f"Name must be a string, got {type(name).__name__}"
name_error = _validate_skill_name(name.strip(), skill_path.name)
if name_error:
return False, name_error
description = frontmatter["description"]
if not isinstance(description, str):
return False, f"Description must be a string, got {type(description).__name__}"
description_error = _validate_description(description)
if description_error:
return False, description_error
always = frontmatter.get("always")
if always is not None and not isinstance(always, bool):
return False, f"'always' must be a boolean, got {type(always).__name__}"
for child in skill_path.iterdir():
if child.name == "SKILL.md":
continue
if child.is_dir() and child.name in ALLOWED_RESOURCE_DIRS:
continue
if child.is_symlink():
continue
return (
False,
f"Unexpected file or directory in skill root: {child.name}. "
"Only SKILL.md, scripts/, references/, and assets/ are allowed.",
)
return True, "Skill is valid!"
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: python quick_validate.py <skill_directory>")
sys.exit(1)
valid, message = validate_skill(sys.argv[1])
print(message)
sys.exit(0 if valid else 1)

104
nanobot/utils/helpers.py
View File

@@ -1,8 +1,12 @@
"""Utility functions for nanobot."""
import json
import re
from datetime import datetime
from pathlib import Path
from typing import Any
import tiktoken
def detect_image_mime(data: bytes) -> str | None:
@@ -68,6 +72,104 @@ def split_message(content: str, max_len: int = 2000) -> list[str]:
return chunks
def build_assistant_message(
content: str | None,
tool_calls: list[dict[str, Any]] | None = None,
reasoning_content: str | None = None,
thinking_blocks: list[dict] | None = None,
) -> dict[str, Any]:
"""Build a provider-safe assistant message with optional reasoning fields."""
msg: dict[str, Any] = {"role": "assistant", "content": content}
if tool_calls:
msg["tool_calls"] = tool_calls
if reasoning_content is not None:
msg["reasoning_content"] = reasoning_content
if thinking_blocks:
msg["thinking_blocks"] = thinking_blocks
return msg
def estimate_prompt_tokens(
messages: list[dict[str, Any]],
tools: list[dict[str, Any]] | None = None,
) -> int:
"""Estimate prompt tokens with tiktoken."""
try:
enc = tiktoken.get_encoding("cl100k_base")
parts: list[str] = []
for msg in messages:
content = msg.get("content")
if isinstance(content, str):
parts.append(content)
elif isinstance(content, list):
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
txt = part.get("text", "")
if txt:
parts.append(txt)
if tools:
parts.append(json.dumps(tools, ensure_ascii=False))
return len(enc.encode("\n".join(parts)))
except Exception:
return 0
def estimate_message_tokens(message: dict[str, Any]) -> int:
"""Estimate prompt tokens contributed by one persisted message."""
content = message.get("content")
parts: list[str] = []
if isinstance(content, str):
parts.append(content)
elif isinstance(content, list):
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
text = part.get("text", "")
if text:
parts.append(text)
else:
parts.append(json.dumps(part, ensure_ascii=False))
elif content is not None:
parts.append(json.dumps(content, ensure_ascii=False))
for key in ("name", "tool_call_id"):
value = message.get(key)
if isinstance(value, str) and value:
parts.append(value)
if message.get("tool_calls"):
parts.append(json.dumps(message["tool_calls"], ensure_ascii=False))
payload = "\n".join(parts)
if not payload:
return 1
try:
enc = tiktoken.get_encoding("cl100k_base")
return max(1, len(enc.encode(payload)))
except Exception:
return max(1, len(payload) // 4)
def estimate_prompt_tokens_chain(
provider: Any,
model: str | None,
messages: list[dict[str, Any]],
tools: list[dict[str, Any]] | None = None,
) -> tuple[int, str]:
"""Estimate prompt tokens via provider counter first, then tiktoken fallback."""
provider_counter = getattr(provider, "estimate_prompt_tokens", None)
if callable(provider_counter):
try:
tokens, source = provider_counter(messages, tools, model)
if isinstance(tokens, (int, float)) and tokens > 0:
return int(tokens), str(source or "provider_counter")
except Exception:
pass
estimated = estimate_prompt_tokens(messages, tools)
if estimated > 0:
return int(estimated), "tiktoken"
return 0, "none"
def sync_workspace_templates(workspace: Path, silent: bool = False) -> list[str]:
"""Sync bundled templates to workspace. Only creates missing files."""
from importlib.resources import files as pkg_files
@@ -88,7 +190,7 @@ def sync_workspace_templates(workspace: Path, silent: bool = False) -> list[str]
added.append(str(dest.relative_to(workspace)))
for item in tpl.iterdir():
if item.name.endswith(".md"):
if item.name.endswith(".md") and not item.name.startswith("."):
_write(item, workspace / item.name)
_write(tpl / "memory" / "MEMORY.md", workspace / "memory" / "MEMORY.md")
_write(None, workspace / "memory" / "HISTORY.md")

26
pyproject.toml
View File

@@ -18,7 +18,7 @@ classifiers = [
dependencies = [
"typer>=0.20.0,<1.0.0",
"litellm>=1.81.5,<2.0.0",
"litellm>=1.82.1,<2.0.0",
"pydantic>=2.12.0,<3.0.0",
"pydantic-settings>=2.12.0,<3.0.0",
"websockets>=16.0,<17.0",
@@ -44,9 +44,13 @@ dependencies = [
"json-repair>=0.57.0,<1.0.0",
"chardet>=3.0.2,<6.0.0",
"openai>=2.8.0",
"tiktoken>=0.12.0,<1.0.0",
]
[project.optional-dependencies]
wecom = [
"wecom-aibot-sdk-python>=0.1.2",
]
matrix = [
"matrix-nio[e2e]>=0.25.2",
"mistune>=3.0.0,<4.0.0",
@@ -68,13 +72,9 @@ nanobot = "nanobot.cli.commands:app"
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["nanobot"]
[tool.hatch.metadata]
allow-direct-references = true
[tool.hatch.build.targets.wheel.sources]
"nanobot" = "nanobot"
# Include non-Python files in skills and templates
[tool.hatch.build]
include = [
"nanobot/**/*.py",
@@ -83,6 +83,15 @@ include = [
"nanobot/skills/**/*.sh",
]
[tool.hatch.build.targets.wheel]
packages = ["nanobot"]
[tool.hatch.build.targets.wheel.sources]
"nanobot" = "nanobot"
[tool.hatch.build.targets.wheel.force-include]
"bridge" = "nanobot/bridge"
[tool.hatch.build.targets.sdist]
include = [
"nanobot/",
@@ -91,9 +100,6 @@ include = [
"LICENSE",
]
[tool.hatch.build.targets.wheel.force-include]
"bridge" = "nanobot/bridge"
[tool.ruff]
line-length = 100
target-version = "py311"

134
tests/test_commands.py
View File

@@ -114,6 +114,64 @@ def test_config_matches_openai_codex_with_hyphen_prefix():
assert config.get_provider_name() == "openai_codex"
def test_config_matches_explicit_ollama_prefix_without_api_key():
config = Config()
config.agents.defaults.model = "ollama/llama3.2"
assert config.get_provider_name() == "ollama"
assert config.get_api_base() == "http://localhost:11434"
def test_config_explicit_ollama_provider_uses_default_localhost_api_base():
config = Config()
config.agents.defaults.provider = "ollama"
config.agents.defaults.model = "llama3.2"
assert config.get_provider_name() == "ollama"
assert config.get_api_base() == "http://localhost:11434"
def test_config_auto_detects_ollama_from_local_api_base():
config = Config.model_validate(
{
"agents": {"defaults": {"provider": "auto", "model": "llama3.2"}},
"providers": {"ollama": {"apiBase": "http://localhost:11434"}},
}
)
assert config.get_provider_name() == "ollama"
assert config.get_api_base() == "http://localhost:11434"
def test_config_prefers_ollama_over_vllm_when_both_local_providers_configured():
config = Config.model_validate(
{
"agents": {"defaults": {"provider": "auto", "model": "llama3.2"}},
"providers": {
"vllm": {"apiBase": "http://localhost:8000"},
"ollama": {"apiBase": "http://localhost:11434"},
},
}
)
assert config.get_provider_name() == "ollama"
assert config.get_api_base() == "http://localhost:11434"
def test_config_falls_back_to_vllm_when_ollama_not_configured():
config = Config.model_validate(
{
"agents": {"defaults": {"provider": "auto", "model": "llama3.2"}},
"providers": {
"vllm": {"apiBase": "http://localhost:8000"},
},
}
)
assert config.get_provider_name() == "vllm"
assert config.get_api_base() == "http://localhost:8000"
def test_find_by_model_prefers_explicit_prefix_over_generic_codex_keyword():
spec = find_by_model("github-copilot/gpt-5.3-codex")
@@ -267,6 +325,16 @@ def test_agent_workspace_override_wins_over_config_workspace(mock_agent_runtime,
assert mock_agent_runtime["agent_loop_cls"].call_args.kwargs["workspace"] == workspace_path
def test_agent_warns_about_deprecated_memory_window(mock_agent_runtime):
mock_agent_runtime["config"].agents.defaults.memory_window = 100
result = runner.invoke(app, ["agent", "-m", "hello"])
assert result.exit_code == 0
assert "memoryWindow" in result.stdout
assert "contextWindowTokens" in result.stdout
def test_gateway_uses_workspace_from_config_by_default(monkeypatch, tmp_path: Path) -> None:
config_file = tmp_path / "instance" / "config.json"
config_file.parent.mkdir(parents=True)
@@ -328,6 +396,28 @@ def test_gateway_workspace_option_overrides_config(monkeypatch, tmp_path: Path)
assert config.workspace_path == override
def test_gateway_warns_about_deprecated_memory_window(monkeypatch, tmp_path: Path) -> None:
config_file = tmp_path / "instance" / "config.json"
config_file.parent.mkdir(parents=True)
config_file.write_text("{}")
config = Config()
config.agents.defaults.memory_window = 100
monkeypatch.setattr("nanobot.config.loader.set_config_path", lambda _path: None)
monkeypatch.setattr("nanobot.config.loader.load_config", lambda _path=None: config)
monkeypatch.setattr("nanobot.cli.commands.sync_workspace_templates", lambda _path: None)
monkeypatch.setattr(
"nanobot.cli.commands._make_provider",
lambda _config: (_ for _ in ()).throw(_StopGateway("stop")),
)
result = runner.invoke(app, ["gateway", "--config", str(config_file)])
assert isinstance(result.exception, _StopGateway)
assert "memoryWindow" in result.stdout
assert "contextWindowTokens" in result.stdout
def test_gateway_uses_config_directory_for_cron_store(monkeypatch, tmp_path: Path) -> None:
config_file = tmp_path / "instance" / "config.json"
config_file.parent.mkdir(parents=True)
@@ -356,3 +446,47 @@ def test_gateway_uses_config_directory_for_cron_store(monkeypatch, tmp_path: Pat
assert isinstance(result.exception, _StopGateway)
assert seen["cron_store"] == config_file.parent / "cron" / "jobs.json"
def test_gateway_uses_configured_port_when_cli_flag_is_missing(monkeypatch, tmp_path: Path) -> None:
config_file = tmp_path / "instance" / "config.json"
config_file.parent.mkdir(parents=True)
config_file.write_text("{}")
config = Config()
config.gateway.port = 18791
monkeypatch.setattr("nanobot.config.loader.set_config_path", lambda _path: None)
monkeypatch.setattr("nanobot.config.loader.load_config", lambda _path=None: config)
monkeypatch.setattr("nanobot.cli.commands.sync_workspace_templates", lambda _path: None)
monkeypatch.setattr(
"nanobot.cli.commands._make_provider",
lambda _config: (_ for _ in ()).throw(_StopGateway("stop")),
)
result = runner.invoke(app, ["gateway", "--config", str(config_file)])
assert isinstance(result.exception, _StopGateway)
assert "port 18791" in result.stdout
def test_gateway_cli_port_overrides_configured_port(monkeypatch, tmp_path: Path) -> None:
config_file = tmp_path / "instance" / "config.json"
config_file.parent.mkdir(parents=True)
config_file.write_text("{}")
config = Config()
config.gateway.port = 18791
monkeypatch.setattr("nanobot.config.loader.set_config_path", lambda _path: None)
monkeypatch.setattr("nanobot.config.loader.load_config", lambda _path=None: config)
monkeypatch.setattr("nanobot.cli.commands.sync_workspace_templates", lambda _path: None)
monkeypatch.setattr(
"nanobot.cli.commands._make_provider",
lambda _config: (_ for _ in ()).throw(_StopGateway("stop")),
)
result = runner.invoke(app, ["gateway", "--config", str(config_file), "--port", "18792"])
assert isinstance(result.exception, _StopGateway)
assert "port 18792" in result.stdout

88
tests/test_config_migration.py Normal file
View File

@@ -0,0 +1,88 @@
import json
from typer.testing import CliRunner
from nanobot.cli.commands import app
from nanobot.config.loader import load_config, save_config
runner = CliRunner()
def test_load_config_keeps_max_tokens_and_warns_on_legacy_memory_window(tmp_path) -> None:
config_path = tmp_path / "config.json"
config_path.write_text(
json.dumps(
{
"agents": {
"defaults": {
"maxTokens": 1234,
"memoryWindow": 42,
}
}
}
),
encoding="utf-8",
)
config = load_config(config_path)
assert config.agents.defaults.max_tokens == 1234
assert config.agents.defaults.context_window_tokens == 65_536
assert config.agents.defaults.should_warn_deprecated_memory_window is True
def test_save_config_writes_context_window_tokens_but_not_memory_window(tmp_path) -> None:
config_path = tmp_path / "config.json"
config_path.write_text(
json.dumps(
{
"agents": {
"defaults": {
"maxTokens": 2222,
"memoryWindow": 30,
}
}
}
),
encoding="utf-8",
)
config = load_config(config_path)
save_config(config, config_path)
saved = json.loads(config_path.read_text(encoding="utf-8"))
defaults = saved["agents"]["defaults"]
assert defaults["maxTokens"] == 2222
assert defaults["contextWindowTokens"] == 65_536
assert "memoryWindow" not in defaults
def test_onboard_refresh_rewrites_legacy_config_template(tmp_path, monkeypatch) -> None:
config_path = tmp_path / "config.json"
workspace = tmp_path / "workspace"
config_path.write_text(
json.dumps(
{
"agents": {
"defaults": {
"maxTokens": 3333,
"memoryWindow": 50,
}
}
}
),
encoding="utf-8",
)
monkeypatch.setattr("nanobot.config.loader.get_config_path", lambda: config_path)
monkeypatch.setattr("nanobot.cli.commands.get_workspace_path", lambda: workspace)
result = runner.invoke(app, ["onboard"], input="n\n")
assert result.exit_code == 0
assert "contextWindowTokens" in result.stdout
saved = json.loads(config_path.read_text(encoding="utf-8"))
defaults = saved["agents"]["defaults"]
assert defaults["maxTokens"] == 3333
assert defaults["contextWindowTokens"] == 65_536
assert "memoryWindow" not in defaults

295
tests/test_consolidate_offset.py
View File

@@ -480,226 +480,35 @@ class TestEmptyAndBoundarySessions:
assert_messages_content(old_messages, 10, 34)
class TestConsolidationDeduplicationGuard:
"""Test that consolidation tasks are deduplicated and serialized."""
class TestNewCommandArchival:
"""Test /new archival behavior with the simplified consolidation flow."""
@pytest.mark.asyncio
async def test_consolidation_guard_prevents_duplicate_tasks(self, tmp_path: Path) -> None:
"""Concurrent messages above memory_window spawn only one consolidation task."""
@staticmethod
def _make_loop(tmp_path: Path):
from nanobot.agent.loop import AgentLoop
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
provider.estimate_prompt_tokens.return_value = (10_000, "test")
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
bus=bus,
provider=provider,
workspace=tmp_path,
model="test-model",
context_window_tokens=1,
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.provider.chat_with_retry = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
session = loop.sessions.get_or_create("cli:test")
for i in range(15):
session.add_message("user", f"msg{i}")
session.add_message("assistant", f"resp{i}")
loop.sessions.save(session)
consolidation_calls = 0
async def _fake_consolidate(_session, archive_all: bool = False) -> None:
nonlocal consolidation_calls
consolidation_calls += 1
await asyncio.sleep(0.05)
loop._consolidate_memory = _fake_consolidate # type: ignore[method-assign]
msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="hello")
await loop._process_message(msg)
await loop._process_message(msg)
await asyncio.sleep(0.1)
assert consolidation_calls == 1, (
f"Expected exactly 1 consolidation, got {consolidation_calls}"
)
@pytest.mark.asyncio
async def test_new_command_guard_prevents_concurrent_consolidation(
self, tmp_path: Path
) -> None:
"""/new command does not run consolidation concurrently with in-flight consolidation."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
session = loop.sessions.get_or_create("cli:test")
for i in range(15):
session.add_message("user", f"msg{i}")
session.add_message("assistant", f"resp{i}")
loop.sessions.save(session)
consolidation_calls = 0
active = 0
max_active = 0
async def _fake_consolidate(_session, archive_all: bool = False) -> None:
nonlocal consolidation_calls, active, max_active
consolidation_calls += 1
active += 1
max_active = max(max_active, active)
await asyncio.sleep(0.05)
active -= 1
loop._consolidate_memory = _fake_consolidate # type: ignore[method-assign]
msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="hello")
await loop._process_message(msg)
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
await loop._process_message(new_msg)
await asyncio.sleep(0.1)
assert consolidation_calls == 2, (
f"Expected normal + /new consolidations, got {consolidation_calls}"
)
assert max_active == 1, (
f"Expected serialized consolidation, observed concurrency={max_active}"
)
@pytest.mark.asyncio
async def test_consolidation_tasks_are_referenced(self, tmp_path: Path) -> None:
"""create_task results are tracked in _consolidation_tasks while in flight."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
session = loop.sessions.get_or_create("cli:test")
for i in range(15):
session.add_message("user", f"msg{i}")
session.add_message("assistant", f"resp{i}")
loop.sessions.save(session)
started = asyncio.Event()
async def _slow_consolidate(_session, archive_all: bool = False) -> None:
started.set()
await asyncio.sleep(0.1)
loop._consolidate_memory = _slow_consolidate # type: ignore[method-assign]
msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="hello")
await loop._process_message(msg)
await started.wait()
assert len(loop._consolidation_tasks) == 1, "Task must be referenced while in-flight"
await asyncio.sleep(0.15)
assert len(loop._consolidation_tasks) == 0, (
"Task reference must be removed after completion"
)
@pytest.mark.asyncio
async def test_new_waits_for_inflight_consolidation_and_preserves_messages(
self, tmp_path: Path
) -> None:
"""/new waits for in-flight consolidation and archives before clear."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
session = loop.sessions.get_or_create("cli:test")
for i in range(15):
session.add_message("user", f"msg{i}")
session.add_message("assistant", f"resp{i}")
loop.sessions.save(session)
started = asyncio.Event()
release = asyncio.Event()
archived_count = 0
async def _fake_consolidate(sess, archive_all: bool = False) -> bool:
nonlocal archived_count
if archive_all:
archived_count = len(sess.messages)
return True
started.set()
await release.wait()
return True
loop._consolidate_memory = _fake_consolidate # type: ignore[method-assign]
msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="hello")
await loop._process_message(msg)
await started.wait()
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
pending_new = asyncio.create_task(loop._process_message(new_msg))
await asyncio.sleep(0.02)
assert not pending_new.done(), "/new should wait while consolidation is in-flight"
release.set()
response = await pending_new
assert response is not None
assert "new session started" in response.content.lower()
assert archived_count > 0, "Expected /new archival to process a non-empty snapshot"
session_after = loop.sessions.get_or_create("cli:test")
assert session_after.messages == [], "Session should be cleared after successful archival"
return loop
@pytest.mark.asyncio
async def test_new_does_not_clear_session_when_archive_fails(self, tmp_path: Path) -> None:
"""/new must keep session data if archive step reports failure."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
loop = self._make_loop(tmp_path)
session = loop.sessions.get_or_create("cli:test")
for i in range(5):
session.add_message("user", f"msg{i}")
@@ -707,111 +516,61 @@ class TestConsolidationDeduplicationGuard:
loop.sessions.save(session)
before_count = len(session.messages)
async def _failing_consolidate(sess, archive_all: bool = False) -> bool:
if archive_all:
async def _failing_consolidate(_messages) -> bool:
return False
return True
loop._consolidate_memory = _failing_consolidate # type: ignore[method-assign]
loop.memory_consolidator.consolidate_messages = _failing_consolidate # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
response = await loop._process_message(new_msg)
assert response is not None
assert "failed" in response.content.lower()
session_after = loop.sessions.get_or_create("cli:test")
assert len(session_after.messages) == before_count, (
"Session must remain intact when /new archival fails"
)
assert len(loop.sessions.get_or_create("cli:test").messages) == before_count
@pytest.mark.asyncio
async def test_new_archives_only_unconsolidated_messages_after_inflight_task(
self, tmp_path: Path
) -> None:
"""/new should archive only messages not yet consolidated by prior task."""
from nanobot.agent.loop import AgentLoop
async def test_new_archives_only_unconsolidated_messages(self, tmp_path: Path) -> None:
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
loop = self._make_loop(tmp_path)
session = loop.sessions.get_or_create("cli:test")
for i in range(15):
session.add_message("user", f"msg{i}")
session.add_message("assistant", f"resp{i}")
session.last_consolidated = len(session.messages) - 3
loop.sessions.save(session)
started = asyncio.Event()
release = asyncio.Event()
archived_count = -1
async def _fake_consolidate(sess, archive_all: bool = False) -> bool:
async def _fake_consolidate(messages) -> bool:
nonlocal archived_count
if archive_all:
archived_count = len(sess.messages)
archived_count = len(messages)
return True
started.set()
await release.wait()
sess.last_consolidated = len(sess.messages) - 3
return True
loop._consolidate_memory = _fake_consolidate # type: ignore[method-assign]
msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="hello")
await loop._process_message(msg)
await started.wait()
loop.memory_consolidator.consolidate_messages = _fake_consolidate # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
pending_new = asyncio.create_task(loop._process_message(new_msg))
await asyncio.sleep(0.02)
assert not pending_new.done()
release.set()
response = await pending_new
response = await loop._process_message(new_msg)
assert response is not None
assert "new session started" in response.content.lower()
assert archived_count == 3, (
f"Expected only unconsolidated tail to archive, got {archived_count}"
)
assert archived_count == 3
@pytest.mark.asyncio
async def test_new_clears_session_and_responds(self, tmp_path: Path) -> None:
"""/new clears session and returns confirmation."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.events import InboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
loop = AgentLoop(
bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10
)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
loop = self._make_loop(tmp_path)
session = loop.sessions.get_or_create("cli:test")
for i in range(3):
session.add_message("user", f"msg{i}")
session.add_message("assistant", f"resp{i}")
loop.sessions.save(session)
async def _ok_consolidate(sess, archive_all: bool = False) -> bool:
async def _ok_consolidate(_messages) -> bool:
return True
loop._consolidate_memory = _ok_consolidate # type: ignore[method-assign]
loop.memory_consolidator.consolidate_messages = _ok_consolidate # type: ignore[method-assign]
new_msg = InboundMessage(channel="cli", sender_id="user", chat_id="test", content="/new")
response = await loop._process_message(new_msg)

47
tests/test_dingtalk_channel.py
View File

@@ -1,9 +1,11 @@
import asyncio
from types import SimpleNamespace
import pytest
from nanobot.bus.queue import MessageBus
from nanobot.channels.dingtalk import DingTalkChannel
import nanobot.channels.dingtalk as dingtalk_module
from nanobot.channels.dingtalk import DingTalkChannel, NanobotDingTalkHandler
from nanobot.config.schema import DingTalkConfig
@@ -64,3 +66,46 @@ async def test_group_send_uses_group_messages_api() -> None:
assert call["url"] == "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
assert call["json"]["openConversationId"] == "conv123"
assert call["json"]["msgKey"] == "sampleMarkdown"
@pytest.mark.asyncio
async def test_handler_uses_voice_recognition_text_when_text_is_empty(monkeypatch) -> None:
bus = MessageBus()
channel = DingTalkChannel(
DingTalkConfig(client_id="app", client_secret="secret", allow_from=["user1"]),
bus,
)
handler = NanobotDingTalkHandler(channel)
class _FakeChatbotMessage:
text = None
extensions = {"content": {"recognition": "voice transcript"}}
sender_staff_id = "user1"
sender_id = "fallback-user"
sender_nick = "Alice"
message_type = "audio"
@staticmethod
def from_dict(_data):
return _FakeChatbotMessage()
monkeypatch.setattr(dingtalk_module, "ChatbotMessage", _FakeChatbotMessage)
monkeypatch.setattr(dingtalk_module, "AckMessage", SimpleNamespace(STATUS_OK="OK"))
status, body = await handler.process(
SimpleNamespace(
data={
"conversationType": "2",
"conversationId": "conv123",
"text": {"content": ""},
}
)
)
await asyncio.gather(*list(channel._background_tasks))
msg = await bus.consume_inbound()
assert (status, body) == ("OK", "OK")
assert msg.content == "voice transcript"
assert msg.sender_id == "user1"
assert msg.chat_id == "group:conv123"

251
tests/test_filesystem_tools.py Normal file
View File

@@ -0,0 +1,251 @@
"""Tests for enhanced filesystem tools: ReadFileTool, EditFileTool, ListDirTool."""
import pytest
from nanobot.agent.tools.filesystem import (
EditFileTool,
ListDirTool,
ReadFileTool,
_find_match,
)
# ---------------------------------------------------------------------------
# ReadFileTool
# ---------------------------------------------------------------------------
class TestReadFileTool:
@pytest.fixture()
def tool(self, tmp_path):
return ReadFileTool(workspace=tmp_path)
@pytest.fixture()
def sample_file(self, tmp_path):
f = tmp_path / "sample.txt"
f.write_text("\n".join(f"line {i}" for i in range(1, 21)), encoding="utf-8")
return f
@pytest.mark.asyncio
async def test_basic_read_has_line_numbers(self, tool, sample_file):
result = await tool.execute(path=str(sample_file))
assert "1| line 1" in result
assert "20| line 20" in result
@pytest.mark.asyncio
async def test_offset_and_limit(self, tool, sample_file):
result = await tool.execute(path=str(sample_file), offset=5, limit=3)
assert "5| line 5" in result
assert "7| line 7" in result
assert "8| line 8" not in result
assert "Use offset=8 to continue" in result
@pytest.mark.asyncio
async def test_offset_beyond_end(self, tool, sample_file):
result = await tool.execute(path=str(sample_file), offset=999)
assert "Error" in result
assert "beyond end" in result
@pytest.mark.asyncio
async def test_end_of_file_marker(self, tool, sample_file):
result = await tool.execute(path=str(sample_file), offset=1, limit=9999)
assert "End of file" in result
@pytest.mark.asyncio
async def test_empty_file(self, tool, tmp_path):
f = tmp_path / "empty.txt"
f.write_text("", encoding="utf-8")
result = await tool.execute(path=str(f))
assert "Empty file" in result
@pytest.mark.asyncio
async def test_file_not_found(self, tool, tmp_path):
result = await tool.execute(path=str(tmp_path / "nope.txt"))
assert "Error" in result
assert "not found" in result
@pytest.mark.asyncio
async def test_char_budget_trims(self, tool, tmp_path):
"""When the selected slice exceeds _MAX_CHARS the output is trimmed."""
f = tmp_path / "big.txt"
# Each line is ~110 chars, 2000 lines ≈ 220 KB > 128 KB limit
f.write_text("\n".join("x" * 110 for _ in range(2000)), encoding="utf-8")
result = await tool.execute(path=str(f))
assert len(result) <= ReadFileTool._MAX_CHARS + 500 # small margin for footer
assert "Use offset=" in result
# ---------------------------------------------------------------------------
# _find_match (unit tests for the helper)
# ---------------------------------------------------------------------------
class TestFindMatch:
def test_exact_match(self):
match, count = _find_match("hello world", "world")
assert match == "world"
assert count == 1
def test_exact_no_match(self):
match, count = _find_match("hello world", "xyz")
assert match is None
assert count == 0
def test_crlf_normalisation(self):
# Caller normalises CRLF before calling _find_match, so test with
# pre-normalised content to verify exact match still works.
content = "line1\nline2\nline3"
old_text = "line1\nline2\nline3"
match, count = _find_match(content, old_text)
assert match is not None
assert count == 1
def test_line_trim_fallback(self):
content = " def foo():\n pass\n"
old_text = "def foo():\n pass"
match, count = _find_match(content, old_text)
assert match is not None
assert count == 1
# The returned match should be the *original* indented text
assert " def foo():" in match
def test_line_trim_multiple_candidates(self):
content = " a\n b\n a\n b\n"
old_text = "a\nb"
match, count = _find_match(content, old_text)
assert count == 2
def test_empty_old_text(self):
match, count = _find_match("hello", "")
# Empty string is always "in" any string via exact match
assert match == ""
# ---------------------------------------------------------------------------
# EditFileTool
# ---------------------------------------------------------------------------
class TestEditFileTool:
@pytest.fixture()
def tool(self, tmp_path):
return EditFileTool(workspace=tmp_path)
@pytest.mark.asyncio
async def test_exact_match(self, tool, tmp_path):
f = tmp_path / "a.py"
f.write_text("hello world", encoding="utf-8")
result = await tool.execute(path=str(f), old_text="world", new_text="earth")
assert "Successfully" in result
assert f.read_text() == "hello earth"
@pytest.mark.asyncio
async def test_crlf_normalisation(self, tool, tmp_path):
f = tmp_path / "crlf.py"
f.write_bytes(b"line1\r\nline2\r\nline3")
result = await tool.execute(
path=str(f), old_text="line1\nline2", new_text="LINE1\nLINE2",
)
assert "Successfully" in result
raw = f.read_bytes()
assert b"LINE1" in raw
# CRLF line endings should be preserved throughout the file
assert b"\r\n" in raw
@pytest.mark.asyncio
async def test_trim_fallback(self, tool, tmp_path):
f = tmp_path / "indent.py"
f.write_text(" def foo():\n pass\n", encoding="utf-8")
result = await tool.execute(
path=str(f), old_text="def foo():\n pass", new_text="def bar():\n return 1",
)
assert "Successfully" in result
assert "bar" in f.read_text()
@pytest.mark.asyncio
async def test_ambiguous_match(self, tool, tmp_path):
f = tmp_path / "dup.py"
f.write_text("aaa\nbbb\naaa\nbbb\n", encoding="utf-8")
result = await tool.execute(path=str(f), old_text="aaa\nbbb", new_text="xxx")
assert "appears" in result.lower() or "Warning" in result
@pytest.mark.asyncio
async def test_replace_all(self, tool, tmp_path):
f = tmp_path / "multi.py"
f.write_text("foo bar foo bar foo", encoding="utf-8")
result = await tool.execute(
path=str(f), old_text="foo", new_text="baz", replace_all=True,
)
assert "Successfully" in result
assert f.read_text() == "baz bar baz bar baz"
@pytest.mark.asyncio
async def test_not_found(self, tool, tmp_path):
f = tmp_path / "nf.py"
f.write_text("hello", encoding="utf-8")
result = await tool.execute(path=str(f), old_text="xyz", new_text="abc")
assert "Error" in result
assert "not found" in result
# ---------------------------------------------------------------------------
# ListDirTool
# ---------------------------------------------------------------------------
class TestListDirTool:
@pytest.fixture()
def tool(self, tmp_path):
return ListDirTool(workspace=tmp_path)
@pytest.fixture()
def populated_dir(self, tmp_path):
(tmp_path / "src").mkdir()
(tmp_path / "src" / "main.py").write_text("pass")
(tmp_path / "src" / "utils.py").write_text("pass")
(tmp_path / "README.md").write_text("hi")
(tmp_path / ".git").mkdir()
(tmp_path / ".git" / "config").write_text("x")
(tmp_path / "node_modules").mkdir()
(tmp_path / "node_modules" / "pkg").mkdir()
return tmp_path
@pytest.mark.asyncio
async def test_basic_list(self, tool, populated_dir):
result = await tool.execute(path=str(populated_dir))
assert "README.md" in result
assert "src" in result
# .git and node_modules should be ignored
assert ".git" not in result
assert "node_modules" not in result
@pytest.mark.asyncio
async def test_recursive(self, tool, populated_dir):
result = await tool.execute(path=str(populated_dir), recursive=True)
assert "src/main.py" in result
assert "src/utils.py" in result
assert "README.md" in result
# Ignored dirs should not appear
assert ".git" not in result
assert "node_modules" not in result
@pytest.mark.asyncio
async def test_max_entries_truncation(self, tool, tmp_path):
for i in range(10):
(tmp_path / f"file_{i}.txt").write_text("x")
result = await tool.execute(path=str(tmp_path), max_entries=3)
assert "truncated" in result
assert "3 of 10" in result
@pytest.mark.asyncio
async def test_empty_dir(self, tool, tmp_path):
d = tmp_path / "empty"
d.mkdir()
result = await tool.execute(path=str(d))
assert "empty" in result.lower()
@pytest.mark.asyncio
async def test_not_found(self, tool, tmp_path):
result = await tool.execute(path=str(tmp_path / "nope"))
assert "Error" in result
assert "not found" in result

53
tests/test_gemini_thought_signature.py Normal file
View File

@@ -0,0 +1,53 @@
from types import SimpleNamespace
from nanobot.providers.base import ToolCallRequest
from nanobot.providers.litellm_provider import LiteLLMProvider
def test_litellm_parse_response_preserves_tool_call_provider_fields() -> None:
provider = LiteLLMProvider(default_model="gemini/gemini-3-flash")
response = SimpleNamespace(
choices=[
SimpleNamespace(
finish_reason="tool_calls",
message=SimpleNamespace(
content=None,
tool_calls=[
SimpleNamespace(
id="call_123",
function=SimpleNamespace(
name="read_file",
arguments='{"path":"todo.md"}',
provider_specific_fields={"inner": "value"},
),
provider_specific_fields={"thought_signature": "signed-token"},
)
],
),
)
],
usage=None,
)
parsed = provider._parse_response(response)
assert len(parsed.tool_calls) == 1
assert parsed.tool_calls[0].provider_specific_fields == {"thought_signature": "signed-token"}
assert parsed.tool_calls[0].function_provider_specific_fields == {"inner": "value"}
def test_tool_call_request_serializes_provider_fields() -> None:
tool_call = ToolCallRequest(
id="abc123xyz",
name="read_file",
arguments={"path": "todo.md"},
provider_specific_fields={"thought_signature": "signed-token"},
function_provider_specific_fields={"inner": "value"},
)
message = tool_call.to_openai_tool_call()
assert message["provider_specific_fields"] == {"thought_signature": "signed-token"}
assert message["function"]["provider_specific_fields"] == {"inner": "value"}
assert message["function"]["arguments"] == '{"path": "todo.md"}'

47
tests/test_heartbeat_service.py
View File

@@ -3,18 +3,24 @@ import asyncio
import pytest
from nanobot.heartbeat.service import HeartbeatService
from nanobot.providers.base import LLMResponse, ToolCallRequest
from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
class DummyProvider:
class DummyProvider(LLMProvider):
def __init__(self, responses: list[LLMResponse]):
super().__init__()
self._responses = list(responses)
self.calls = 0
async def chat(self, *args, **kwargs) -> LLMResponse:
self.calls += 1
if self._responses:
return self._responses.pop(0)
return LLMResponse(content="", tool_calls=[])
def get_default_model(self) -> str:
return "test-model"
@pytest.mark.asyncio
async def test_start_is_idempotent(tmp_path) -> None:
@@ -115,3 +121,40 @@ async def test_trigger_now_returns_none_when_decision_is_skip(tmp_path) -> None:
)
assert await service.trigger_now() is None
@pytest.mark.asyncio
async def test_decide_retries_transient_error_then_succeeds(tmp_path, monkeypatch) -> None:
provider = DummyProvider([
LLMResponse(content="429 rate limit", finish_reason="error"),
LLMResponse(
content="",
tool_calls=[
ToolCallRequest(
id="hb_1",
name="heartbeat",
arguments={"action": "run", "tasks": "check open tasks"},
)
],
),
])
delays: list[int] = []
async def _fake_sleep(delay: int) -> None:
delays.append(delay)
monkeypatch.setattr(asyncio, "sleep", _fake_sleep)
service = HeartbeatService(
workspace=tmp_path,
provider=provider,
model="openai/gpt-4o-mini",
)
action, tasks = await service._decide("heartbeat content")
assert action == "run"
assert tasks == "check open tasks"
assert provider.calls == 2
assert delays == [1]

190
tests/test_loop_consolidation_tokens.py Normal file
View File

@@ -0,0 +1,190 @@
from unittest.mock import AsyncMock, MagicMock
import pytest
from nanobot.agent.loop import AgentLoop
import nanobot.agent.memory as memory_module
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse
def _make_loop(tmp_path, *, estimated_tokens: int, context_window_tokens: int) -> AgentLoop:
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
provider.estimate_prompt_tokens.return_value = (estimated_tokens, "test-counter")
provider.chat_with_retry = AsyncMock(return_value=LLMResponse(content="ok", tool_calls=[]))
loop = AgentLoop(
bus=MessageBus(),
provider=provider,
workspace=tmp_path,
model="test-model",
context_window_tokens=context_window_tokens,
)
loop.tools.get_definitions = MagicMock(return_value=[])
return loop
@pytest.mark.asyncio
async def test_prompt_below_threshold_does_not_consolidate(tmp_path) -> None:
loop = _make_loop(tmp_path, estimated_tokens=100, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
await loop.process_direct("hello", session_key="cli:test")
loop.memory_consolidator.consolidate_messages.assert_not_awaited()
@pytest.mark.asyncio
async def test_prompt_above_threshold_triggers_consolidation(tmp_path, monkeypatch) -> None:
loop = _make_loop(tmp_path, estimated_tokens=1000, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
{"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
{"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
{"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
]
loop.sessions.save(session)
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _message: 500)
await loop.process_direct("hello", session_key="cli:test")
assert loop.memory_consolidator.consolidate_messages.await_count >= 1
@pytest.mark.asyncio
async def test_prompt_above_threshold_archives_until_next_user_boundary(tmp_path, monkeypatch) -> None:
loop = _make_loop(tmp_path, estimated_tokens=1000, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
{"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
{"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
{"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
{"role": "assistant", "content": "a2", "timestamp": "2026-01-01T00:00:03"},
{"role": "user", "content": "u3", "timestamp": "2026-01-01T00:00:04"},
]
loop.sessions.save(session)
token_map = {"u1": 120, "a1": 120, "u2": 120, "a2": 120, "u3": 120}
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda message: token_map[message["content"]])
await loop.memory_consolidator.maybe_consolidate_by_tokens(session)
archived_chunk = loop.memory_consolidator.consolidate_messages.await_args.args[0]
assert [message["content"] for message in archived_chunk] == ["u1", "a1", "u2", "a2"]
assert session.last_consolidated == 4
@pytest.mark.asyncio
async def test_consolidation_loops_until_target_met(tmp_path, monkeypatch) -> None:
"""Verify maybe_consolidate_by_tokens keeps looping until under threshold."""
loop = _make_loop(tmp_path, estimated_tokens=0, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
{"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
{"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
{"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
{"role": "assistant", "content": "a2", "timestamp": "2026-01-01T00:00:03"},
{"role": "user", "content": "u3", "timestamp": "2026-01-01T00:00:04"},
{"role": "assistant", "content": "a3", "timestamp": "2026-01-01T00:00:05"},
{"role": "user", "content": "u4", "timestamp": "2026-01-01T00:00:06"},
]
loop.sessions.save(session)
call_count = [0]
def mock_estimate(_session):
call_count[0] += 1
if call_count[0] == 1:
return (500, "test")
if call_count[0] == 2:
return (300, "test")
return (80, "test")
loop.memory_consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
await loop.memory_consolidator.maybe_consolidate_by_tokens(session)
assert loop.memory_consolidator.consolidate_messages.await_count == 2
assert session.last_consolidated == 6
@pytest.mark.asyncio
async def test_consolidation_continues_below_trigger_until_half_target(tmp_path, monkeypatch) -> None:
"""Once triggered, consolidation should continue until it drops below half threshold."""
loop = _make_loop(tmp_path, estimated_tokens=0, context_window_tokens=200)
loop.memory_consolidator.consolidate_messages = AsyncMock(return_value=True) # type: ignore[method-assign]
session = loop.sessions.get_or_create("cli:test")
session.messages = [
{"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
{"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
{"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
{"role": "assistant", "content": "a2", "timestamp": "2026-01-01T00:00:03"},
{"role": "user", "content": "u3", "timestamp": "2026-01-01T00:00:04"},
{"role": "assistant", "content": "a3", "timestamp": "2026-01-01T00:00:05"},
{"role": "user", "content": "u4", "timestamp": "2026-01-01T00:00:06"},
]
loop.sessions.save(session)
call_count = [0]
def mock_estimate(_session):
call_count[0] += 1
if call_count[0] == 1:
return (500, "test")
if call_count[0] == 2:
return (150, "test")
return (80, "test")
loop.memory_consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
await loop.memory_consolidator.maybe_consolidate_by_tokens(session)
assert loop.memory_consolidator.consolidate_messages.await_count == 2
assert session.last_consolidated == 6
@pytest.mark.asyncio
async def test_preflight_consolidation_before_llm_call(tmp_path, monkeypatch) -> None:
"""Verify preflight consolidation runs before the LLM call in process_direct."""
order: list[str] = []
loop = _make_loop(tmp_path, estimated_tokens=0, context_window_tokens=200)
async def track_consolidate(messages):
order.append("consolidate")
return True
loop.memory_consolidator.consolidate_messages = track_consolidate # type: ignore[method-assign]
async def track_llm(*args, **kwargs):
order.append("llm")
return LLMResponse(content="ok", tool_calls=[])
loop.provider.chat_with_retry = track_llm
session = loop.sessions.get_or_create("cli:test")
session.messages = [
{"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
{"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
{"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
]
loop.sessions.save(session)
monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 500)
call_count = [0]
def mock_estimate(_session):
call_count[0] += 1
return (1000 if call_count[0] <= 1 else 80, "test")
loop.memory_consolidator.estimate_session_prompt_tokens = mock_estimate # type: ignore[method-assign]
await loop.process_direct("hello", session_key="cli:test")
assert "consolidate" in order
assert "llm" in order
assert order.index("consolidate") < order.index("llm")

16
tests/test_loop_save_turn.py
View File

@@ -5,7 +5,7 @@ from nanobot.session.manager import Session
def _mk_loop() -> AgentLoop:
loop = AgentLoop.__new__(AgentLoop)
loop._TOOL_RESULT_MAX_CHARS = 500
loop._TOOL_RESULT_MAX_CHARS = AgentLoop._TOOL_RESULT_MAX_CHARS
return loop
@@ -39,3 +39,17 @@ def test_save_turn_keeps_image_placeholder_after_runtime_strip() -> None:
skip=0,
)
assert session.messages[0]["content"] == [{"type": "text", "text": "[image]"}]
def test_save_turn_keeps_tool_results_under_16k() -> None:
loop = _mk_loop()
session = Session(key="test:tool-result")
content = "x" * 12_000
loop._save_turn(
session,
[{"role": "tool", "tool_call_id": "call_1", "name": "read_file", "content": content}],
skip=0,
)
assert session.messages[0]["content"] == content

205
tests/test_memory_consolidation_types.py
View File

@@ -7,23 +7,20 @@ tool call response, it should serialize them to JSON instead of raising TypeErro
import json
from pathlib import Path
from unittest.mock import AsyncMock, MagicMock
from unittest.mock import AsyncMock
import pytest
from nanobot.agent.memory import MemoryStore
from nanobot.providers.base import LLMResponse, ToolCallRequest
from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
def _make_session(message_count: int = 30, memory_window: int = 50):
"""Create a mock session with messages."""
session = MagicMock()
session.messages = [
def _make_messages(message_count: int = 30):
"""Create a list of mock messages."""
return [
{"role": "user", "content": f"msg{i}", "timestamp": "2026-01-01 00:00"}
for i in range(message_count)
]
session.last_consolidated = 0
return session
def _make_tool_response(history_entry, memory_update):
@@ -43,6 +40,22 @@ def _make_tool_response(history_entry, memory_update):
)
class ScriptedProvider(LLMProvider):
def __init__(self, responses: list[LLMResponse]):
super().__init__()
self._responses = list(responses)
self.calls = 0
async def chat(self, *args, **kwargs) -> LLMResponse:
self.calls += 1
if self._responses:
return self._responses.pop(0)
return LLMResponse(content="", tool_calls=[])
def get_default_model(self) -> str:
return "test-model"
class TestMemoryConsolidationTypeHandling:
"""Test that consolidation handles various argument types correctly."""
@@ -57,9 +70,10 @@ class TestMemoryConsolidationTypeHandling:
memory_update="# Memory\nUser likes testing.",
)
)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert store.history_file.exists()
@@ -77,9 +91,10 @@ class TestMemoryConsolidationTypeHandling:
memory_update={"facts": ["User likes testing"], "topics": ["testing"]},
)
)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert store.history_file.exists()
@@ -111,9 +126,10 @@ class TestMemoryConsolidationTypeHandling:
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert "User discussed testing." in store.history_file.read_text()
@@ -126,21 +142,23 @@ class TestMemoryConsolidationTypeHandling:
provider.chat = AsyncMock(
return_value=LLMResponse(content="I summarized the conversation.", tool_calls=[])
)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
@pytest.mark.asyncio
async def test_skips_when_few_messages(self, tmp_path: Path) -> None:
"""Consolidation should be a no-op when messages < keep_count."""
async def test_skips_when_message_chunk_is_empty(self, tmp_path: Path) -> None:
"""Consolidation should be a no-op when the selected chunk is empty."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
session = _make_session(message_count=10)
provider.chat_with_retry = provider.chat
messages: list[dict] = []
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
provider.chat.assert_not_called()
@@ -165,9 +183,10 @@ class TestMemoryConsolidationTypeHandling:
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert "User discussed testing." in store.history_file.read_text()
@@ -190,9 +209,10 @@ class TestMemoryConsolidationTypeHandling:
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
@@ -213,9 +233,10 @@ class TestMemoryConsolidationTypeHandling:
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
provider.chat_with_retry = provider.chat
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
@@ -224,7 +245,7 @@ class TestMemoryConsolidationTypeHandling:
"""Do not persist partial results when required fields are missing."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
provider.chat_with_retry = AsyncMock(
return_value=LLMResponse(
content=None,
tool_calls=[
@@ -236,21 +257,20 @@ class TestMemoryConsolidationTypeHandling:
],
)
)
session = _make_session(message_count=60)
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
assert session.last_consolidated == 0
@pytest.mark.asyncio
async def test_missing_memory_update_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Do not append history if memory_update is missing."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
provider.chat_with_retry = AsyncMock(
return_value=LLMResponse(
content=None,
tool_calls=[
@@ -262,51 +282,152 @@ class TestMemoryConsolidationTypeHandling:
],
)
)
session = _make_session(message_count=60)
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
assert session.last_consolidated == 0
@pytest.mark.asyncio
async def test_null_required_field_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Null required fields should be rejected before persistence."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
provider.chat_with_retry = AsyncMock(
return_value=_make_tool_response(
history_entry=None,
memory_update="# Memory\nUser likes testing.",
)
)
session = _make_session(message_count=60)
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
assert session.last_consolidated == 0
@pytest.mark.asyncio
async def test_empty_history_entry_returns_false_without_writing(self, tmp_path: Path) -> None:
"""Empty history entries should be rejected to avoid blank archival records."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat = AsyncMock(
provider.chat_with_retry = AsyncMock(
return_value=_make_tool_response(
history_entry=" ",
memory_update="# Memory\nUser likes testing.",
)
)
session = _make_session(message_count=60)
messages = _make_messages(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()
assert not store.memory_file.exists()
assert session.last_consolidated == 0
@pytest.mark.asyncio
async def test_retries_transient_error_then_succeeds(self, tmp_path: Path, monkeypatch) -> None:
store = MemoryStore(tmp_path)
provider = ScriptedProvider([
LLMResponse(content="503 server error", finish_reason="error"),
_make_tool_response(
history_entry="[2026-01-01] User discussed testing.",
memory_update="# Memory\nUser likes testing.",
),
])
messages = _make_messages(message_count=60)
delays: list[int] = []
async def _fake_sleep(delay: int) -> None:
delays.append(delay)
monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert provider.calls == 2
assert delays == [1]
@pytest.mark.asyncio
async def test_consolidation_delegates_to_provider_defaults(self, tmp_path: Path) -> None:
"""Consolidation no longer passes generation params — the provider owns them."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(
return_value=_make_tool_response(
history_entry="[2026-01-01] User discussed testing.",
memory_update="# Memory\nUser likes testing.",
)
)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
provider.chat_with_retry.assert_awaited_once()
_, kwargs = provider.chat_with_retry.await_args
assert kwargs["model"] == "test-model"
assert "temperature" not in kwargs
assert "max_tokens" not in kwargs
assert "reasoning_effort" not in kwargs
@pytest.mark.asyncio
async def test_tool_choice_fallback_on_unsupported_error(self, tmp_path: Path) -> None:
"""Forced tool_choice rejected by provider -> retry with auto and succeed."""
store = MemoryStore(tmp_path)
error_resp = LLMResponse(
content="Error calling LLM: litellm.BadRequestError: "
"The tool_choice parameter does not support being set to required or object",
finish_reason="error",
tool_calls=[],
)
ok_resp = _make_tool_response(
history_entry="[2026-01-01] Fallback worked.",
memory_update="# Memory\nFallback OK.",
)
call_log: list[dict] = []
async def _tracking_chat(**kwargs):
call_log.append(kwargs)
return error_resp if len(call_log) == 1 else ok_resp
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(side_effect=_tracking_chat)
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is True
assert len(call_log) == 2
assert isinstance(call_log[0]["tool_choice"], dict)
assert call_log[1]["tool_choice"] == "auto"
assert "Fallback worked." in store.history_file.read_text()
@pytest.mark.asyncio
async def test_tool_choice_fallback_auto_no_tool_call(self, tmp_path: Path) -> None:
"""Forced rejected, auto retry also produces no tool call -> return False."""
store = MemoryStore(tmp_path)
error_resp = LLMResponse(
content="Error: tool_choice must be none or auto",
finish_reason="error",
tool_calls=[],
)
no_tool_resp = LLMResponse(
content="Here is a summary.",
finish_reason="stop",
tool_calls=[],
)
provider = AsyncMock()
provider.chat_with_retry = AsyncMock(side_effect=[error_resp, no_tool_resp])
messages = _make_messages(message_count=60)
result = await store.consolidate(messages, provider, "test-model")
assert result is False
assert not store.history_file.exists()

10
tests/test_message_tool_suppress.py
View File

@@ -16,7 +16,7 @@ def _make_loop(tmp_path: Path) -> AgentLoop:
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
return AgentLoop(bus=bus, provider=provider, workspace=tmp_path, model="test-model", memory_window=10)
return AgentLoop(bus=bus, provider=provider, workspace=tmp_path, model="test-model")
class TestMessageToolSuppressLogic:
@@ -33,7 +33,7 @@ class TestMessageToolSuppressLogic:
LLMResponse(content="", tool_calls=[tool_call]),
LLMResponse(content="Done", tool_calls=[]),
])
loop.provider.chat = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.provider.chat_with_retry = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.tools.get_definitions = MagicMock(return_value=[])
sent: list[OutboundMessage] = []
@@ -58,7 +58,7 @@ class TestMessageToolSuppressLogic:
LLMResponse(content="", tool_calls=[tool_call]),
LLMResponse(content="I've sent the email.", tool_calls=[]),
])
loop.provider.chat = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.provider.chat_with_retry = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.tools.get_definitions = MagicMock(return_value=[])
sent: list[OutboundMessage] = []
@@ -77,7 +77,7 @@ class TestMessageToolSuppressLogic:
@pytest.mark.asyncio
async def test_not_suppress_when_no_message_tool_used(self, tmp_path: Path) -> None:
loop = _make_loop(tmp_path)
loop.provider.chat = AsyncMock(return_value=LLMResponse(content="Hello!", tool_calls=[]))
loop.provider.chat_with_retry = AsyncMock(return_value=LLMResponse(content="Hello!", tool_calls=[]))
loop.tools.get_definitions = MagicMock(return_value=[])
msg = InboundMessage(channel="feishu", sender_id="user1", chat_id="chat123", content="Hi")
@@ -98,7 +98,7 @@ class TestMessageToolSuppressLogic:
),
LLMResponse(content="Done", tool_calls=[]),
])
loop.provider.chat = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.provider.chat_with_retry = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.tools.get_definitions = MagicMock(return_value=[])
loop.tools.execute = AsyncMock(return_value="ok")

125
tests/test_provider_retry.py Normal file
View File

@@ -0,0 +1,125 @@
import asyncio
import pytest
from nanobot.providers.base import GenerationSettings, LLMProvider, LLMResponse
class ScriptedProvider(LLMProvider):
def __init__(self, responses):
super().__init__()
self._responses = list(responses)
self.calls = 0
self.last_kwargs: dict = {}
async def chat(self, *args, **kwargs) -> LLMResponse:
self.calls += 1
self.last_kwargs = kwargs
response = self._responses.pop(0)
if isinstance(response, BaseException):
raise response
return response
def get_default_model(self) -> str:
return "test-model"
@pytest.mark.asyncio
async def test_chat_with_retry_retries_transient_error_then_succeeds(monkeypatch) -> None:
provider = ScriptedProvider([
LLMResponse(content="429 rate limit", finish_reason="error"),
LLMResponse(content="ok"),
])
delays: list[int] = []
async def _fake_sleep(delay: int) -> None:
delays.append(delay)
monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
response = await provider.chat_with_retry(messages=[{"role": "user", "content": "hello"}])
assert response.finish_reason == "stop"
assert response.content == "ok"
assert provider.calls == 2
assert delays == [1]
@pytest.mark.asyncio
async def test_chat_with_retry_does_not_retry_non_transient_error(monkeypatch) -> None:
provider = ScriptedProvider([
LLMResponse(content="401 unauthorized", finish_reason="error"),
])
delays: list[int] = []
async def _fake_sleep(delay: int) -> None:
delays.append(delay)
monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
response = await provider.chat_with_retry(messages=[{"role": "user", "content": "hello"}])
assert response.content == "401 unauthorized"
assert provider.calls == 1
assert delays == []
@pytest.mark.asyncio
async def test_chat_with_retry_returns_final_error_after_retries(monkeypatch) -> None:
provider = ScriptedProvider([
LLMResponse(content="429 rate limit a", finish_reason="error"),
LLMResponse(content="429 rate limit b", finish_reason="error"),
LLMResponse(content="429 rate limit c", finish_reason="error"),
LLMResponse(content="503 final server error", finish_reason="error"),
])
delays: list[int] = []
async def _fake_sleep(delay: int) -> None:
delays.append(delay)
monkeypatch.setattr("nanobot.providers.base.asyncio.sleep", _fake_sleep)
response = await provider.chat_with_retry(messages=[{"role": "user", "content": "hello"}])
assert response.content == "503 final server error"
assert provider.calls == 4
assert delays == [1, 2, 4]
@pytest.mark.asyncio
async def test_chat_with_retry_preserves_cancelled_error() -> None:
provider = ScriptedProvider([asyncio.CancelledError()])
with pytest.raises(asyncio.CancelledError):
await provider.chat_with_retry(messages=[{"role": "user", "content": "hello"}])
@pytest.mark.asyncio
async def test_chat_with_retry_uses_provider_generation_defaults() -> None:
"""When callers omit generation params, provider.generation defaults are used."""
provider = ScriptedProvider([LLMResponse(content="ok")])
provider.generation = GenerationSettings(temperature=0.2, max_tokens=321, reasoning_effort="high")
await provider.chat_with_retry(messages=[{"role": "user", "content": "hello"}])
assert provider.last_kwargs["temperature"] == 0.2
assert provider.last_kwargs["max_tokens"] == 321
assert provider.last_kwargs["reasoning_effort"] == "high"
@pytest.mark.asyncio
async def test_chat_with_retry_explicit_override_beats_defaults() -> None:
"""Explicit kwargs should override provider.generation defaults."""
provider = ScriptedProvider([LLMResponse(content="ok")])
provider.generation = GenerationSettings(temperature=0.2, max_tokens=321, reasoning_effort="high")
await provider.chat_with_retry(
messages=[{"role": "user", "content": "hello"}],
temperature=0.9,
max_tokens=9999,
reasoning_effort="low",
)
assert provider.last_kwargs["temperature"] == 0.9
assert provider.last_kwargs["max_tokens"] == 9999
assert provider.last_kwargs["reasoning_effort"] == "low"

38
tests/test_qq_channel.py
View File

@@ -44,7 +44,7 @@ async def test_on_group_message_routes_to_group_chat_id() -> None:
@pytest.mark.asyncio
async def test_send_group_message_uses_group_api_with_msg_seq() -> None:
async def test_send_group_message_uses_plain_text_group_api_with_msg_seq() -> None:
channel = QQChannel(QQConfig(app_id="app", secret="secret", allow_from=["*"]), MessageBus())
channel._client = _FakeClient()
channel._chat_type_cache["group123"] = "group"
@@ -60,7 +60,37 @@ async def test_send_group_message_uses_group_api_with_msg_seq() -> None:
assert len(channel._client.api.group_calls) == 1
call = channel._client.api.group_calls[0]
assert call["group_openid"] == "group123"
assert call["msg_id"] == "msg1"
assert call["msg_seq"] == 2
assert call == {
"group_openid": "group123",
"msg_type": 0,
"content": "hello",
"msg_id": "msg1",
"msg_seq": 2,
}
assert not channel._client.api.c2c_calls
@pytest.mark.asyncio
async def test_send_c2c_message_uses_plain_text_c2c_api_with_msg_seq() -> None:
channel = QQChannel(QQConfig(app_id="app", secret="secret", allow_from=["*"]), MessageBus())
channel._client = _FakeClient()
await channel.send(
OutboundMessage(
channel="qq",
chat_id="user123",
content="hello",
metadata={"message_id": "msg1"},
)
)
assert len(channel._client.api.c2c_calls) == 1
call = channel._client.api.c2c_calls[0]
assert call == {
"openid": "user123",
"msg_type": 0,
"content": "hello",
"msg_id": "msg1",
"msg_seq": 2,
}
assert not channel._client.api.group_calls

76
tests/test_restart_command.py Normal file
View File

@@ -0,0 +1,76 @@
"""Tests for /restart slash command."""
from __future__ import annotations
import asyncio
from unittest.mock import MagicMock, patch
import pytest
from nanobot.bus.events import InboundMessage
def _make_loop():
"""Create a minimal AgentLoop with mocked dependencies."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.queue import MessageBus
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
workspace = MagicMock()
workspace.__truediv__ = MagicMock(return_value=MagicMock())
with patch("nanobot.agent.loop.ContextBuilder"), \
patch("nanobot.agent.loop.SessionManager"), \
patch("nanobot.agent.loop.SubagentManager"):
loop = AgentLoop(bus=bus, provider=provider, workspace=workspace)
return loop, bus
class TestRestartCommand:
@pytest.mark.asyncio
async def test_restart_sends_message_and_calls_execv(self):
loop, bus = _make_loop()
msg = InboundMessage(channel="cli", sender_id="user", chat_id="direct", content="/restart")
with patch("nanobot.agent.loop.os.execv") as mock_execv:
await loop._handle_restart(msg)
out = await asyncio.wait_for(bus.consume_outbound(), timeout=1.0)
assert "Restarting" in out.content
await asyncio.sleep(1.5)
mock_execv.assert_called_once()
@pytest.mark.asyncio
async def test_restart_intercepted_in_run_loop(self):
"""Verify /restart is handled at the run-loop level, not inside _dispatch."""
loop, bus = _make_loop()
msg = InboundMessage(channel="telegram", sender_id="u1", chat_id="c1", content="/restart")
with patch.object(loop, "_handle_restart") as mock_handle:
mock_handle.return_value = None
await bus.publish_inbound(msg)
loop._running = True
run_task = asyncio.create_task(loop.run())
await asyncio.sleep(0.1)
loop._running = False
run_task.cancel()
try:
await run_task
except asyncio.CancelledError:
pass
mock_handle.assert_called_once()
@pytest.mark.asyncio
async def test_help_includes_restart(self):
loop, bus = _make_loop()
msg = InboundMessage(channel="telegram", sender_id="u1", chat_id="c1", content="/help")
response = await loop._process_message(msg)
assert response is not None
assert "/restart" in response.content

127
tests/test_skill_creator_scripts.py Normal file
View File

@@ -0,0 +1,127 @@
import importlib
import shutil
import sys
import zipfile
from pathlib import Path
SCRIPT_DIR = Path("nanobot/skills/skill-creator/scripts").resolve()
if str(SCRIPT_DIR) not in sys.path:
sys.path.insert(0, str(SCRIPT_DIR))
init_skill = importlib.import_module("init_skill")
package_skill = importlib.import_module("package_skill")
quick_validate = importlib.import_module("quick_validate")
def test_init_skill_creates_expected_files(tmp_path: Path) -> None:
skill_dir = init_skill.init_skill(
"demo-skill",
tmp_path,
["scripts", "references", "assets"],
include_examples=True,
)
assert skill_dir == tmp_path / "demo-skill"
assert (skill_dir / "SKILL.md").exists()
assert (skill_dir / "scripts" / "example.py").exists()
assert (skill_dir / "references" / "api_reference.md").exists()
assert (skill_dir / "assets" / "example_asset.txt").exists()
def test_validate_skill_accepts_existing_skill_creator() -> None:
valid, message = quick_validate.validate_skill(
Path("nanobot/skills/skill-creator").resolve()
)
assert valid, message
def test_validate_skill_rejects_placeholder_description(tmp_path: Path) -> None:
skill_dir = tmp_path / "placeholder-skill"
skill_dir.mkdir()
(skill_dir / "SKILL.md").write_text(
"---\n"
"name: placeholder-skill\n"
'description: "[TODO: fill me in]"\n'
"---\n"
"# Placeholder\n",
encoding="utf-8",
)
valid, message = quick_validate.validate_skill(skill_dir)
assert not valid
assert "TODO placeholder" in message
def test_validate_skill_rejects_root_files_outside_allowed_dirs(tmp_path: Path) -> None:
skill_dir = tmp_path / "bad-root-skill"
skill_dir.mkdir()
(skill_dir / "SKILL.md").write_text(
"---\n"
"name: bad-root-skill\n"
"description: Valid description\n"
"---\n"
"# Skill\n",
encoding="utf-8",
)
(skill_dir / "README.md").write_text("extra\n", encoding="utf-8")
valid, message = quick_validate.validate_skill(skill_dir)
assert not valid
assert "Unexpected file or directory in skill root" in message
def test_package_skill_creates_archive(tmp_path: Path) -> None:
skill_dir = tmp_path / "package-me"
skill_dir.mkdir()
(skill_dir / "SKILL.md").write_text(
"---\n"
"name: package-me\n"
"description: Package this skill.\n"
"---\n"
"# Skill\n",
encoding="utf-8",
)
scripts_dir = skill_dir / "scripts"
scripts_dir.mkdir()
(scripts_dir / "helper.py").write_text("print('ok')\n", encoding="utf-8")
archive_path = package_skill.package_skill(skill_dir, tmp_path / "dist")
assert archive_path == (tmp_path / "dist" / "package-me.skill")
assert archive_path.exists()
with zipfile.ZipFile(archive_path, "r") as archive:
names = set(archive.namelist())
assert "package-me/SKILL.md" in names
assert "package-me/scripts/helper.py" in names
def test_package_skill_rejects_symlink(tmp_path: Path) -> None:
skill_dir = tmp_path / "symlink-skill"
skill_dir.mkdir()
(skill_dir / "SKILL.md").write_text(
"---\n"
"name: symlink-skill\n"
"description: Reject symlinks during packaging.\n"
"---\n"
"# Skill\n",
encoding="utf-8",
)
scripts_dir = skill_dir / "scripts"
scripts_dir.mkdir()
target = tmp_path / "outside.txt"
target.write_text("secret\n", encoding="utf-8")
link = scripts_dir / "outside.txt"
try:
link.symlink_to(target)
except (OSError, NotImplementedError):
return
archive_path = package_skill.package_skill(skill_dir, tmp_path / "dist")
assert archive_path is None
assert not (tmp_path / "dist" / "symlink-skill.skill").exists()

43
tests/test_task_cancel.py
View File

@@ -165,3 +165,46 @@ class TestSubagentCancellation:
provider.get_default_model.return_value = "test-model"
mgr = SubagentManager(provider=provider, workspace=MagicMock(), bus=bus)
assert await mgr.cancel_by_session("nonexistent") == 0
@pytest.mark.asyncio
async def test_subagent_preserves_reasoning_fields_in_tool_turn(self, monkeypatch, tmp_path):
from nanobot.agent.subagent import SubagentManager
from nanobot.bus.queue import MessageBus
from nanobot.providers.base import LLMResponse, ToolCallRequest
bus = MessageBus()
provider = MagicMock()
provider.get_default_model.return_value = "test-model"
captured_second_call: list[dict] = []
call_count = {"n": 0}
async def scripted_chat_with_retry(*, messages, **kwargs):
call_count["n"] += 1
if call_count["n"] == 1:
return LLMResponse(
content="thinking",
tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
reasoning_content="hidden reasoning",
thinking_blocks=[{"type": "thinking", "thinking": "step"}],
)
captured_second_call[:] = messages
return LLMResponse(content="done", tool_calls=[])
provider.chat_with_retry = scripted_chat_with_retry
mgr = SubagentManager(provider=provider, workspace=tmp_path, bus=bus)
async def fake_execute(self, name, arguments):
return "tool result"
monkeypatch.setattr("nanobot.agent.tools.registry.ToolRegistry.execute", fake_execute)
await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})
assistant_messages = [
msg for msg in captured_second_call
if msg.get("role") == "assistant" and msg.get("tool_calls")
]
assert len(assistant_messages) == 1
assert assistant_messages[0]["reasoning_content"] == "hidden reasoning"
assert assistant_messages[0]["thinking_blocks"] == [{"type": "thinking", "thinking": "step"}]

419
tests/test_telegram_channel.py
View File

@@ -1,10 +1,13 @@
import asyncio
from pathlib import Path
from types import SimpleNamespace
from unittest.mock import AsyncMock
import pytest
from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.telegram import TelegramChannel
from nanobot.channels.telegram import TELEGRAM_REPLY_CONTEXT_MAX_LEN, TelegramChannel
from nanobot.config.schema import TelegramConfig
@@ -27,9 +30,11 @@ class _FakeUpdater:
class _FakeBot:
def __init__(self) -> None:
self.sent_messages: list[dict] = []
self.get_me_calls = 0
async def get_me(self):
return SimpleNamespace(username="nanobot_test")
self.get_me_calls += 1
return SimpleNamespace(id=999, username="nanobot_test")
async def set_my_commands(self, commands) -> None:
self.commands = commands
@@ -37,6 +42,15 @@ class _FakeBot:
async def send_message(self, **kwargs) -> None:
self.sent_messages.append(kwargs)
async def send_chat_action(self, **kwargs) -> None:
pass
async def get_file(self, file_id: str):
"""Return a fake file that 'downloads' to a path (for reply-to-media tests)."""
async def _fake_download(path) -> None:
pass
return SimpleNamespace(download_to_drive=_fake_download)
class _FakeApp:
def __init__(self, on_start_polling) -> None:
@@ -87,6 +101,35 @@ class _FakeBuilder:
return self.app
def _make_telegram_update(
*,
chat_type: str = "group",
text: str | None = None,
caption: str | None = None,
entities=None,
caption_entities=None,
reply_to_message=None,
):
user = SimpleNamespace(id=12345, username="alice", first_name="Alice")
message = SimpleNamespace(
chat=SimpleNamespace(type=chat_type, is_forum=False),
chat_id=-100123,
text=text,
caption=caption,
entities=entities or [],
caption_entities=caption_entities or [],
reply_to_message=reply_to_message,
photo=None,
voice=None,
audio=None,
document=None,
media_group_id=None,
message_thread_id=None,
message_id=1,
)
return SimpleNamespace(message=message, effective_user=user)
@pytest.mark.asyncio
async def test_start_uses_request_proxy_without_builder_proxy(monkeypatch) -> None:
config = TelegramConfig(
@@ -131,6 +174,10 @@ def test_get_extension_falls_back_to_original_filename() -> None:
assert channel._get_extension("file", None, "archive.tar.gz") == ".tar.gz"
def test_telegram_group_policy_defaults_to_mention() -> None:
assert TelegramConfig().group_policy == "mention"
def test_is_allowed_accepts_legacy_telegram_id_username_formats() -> None:
channel = TelegramChannel(TelegramConfig(allow_from=["12345", "alice", "67890|bob"]), MessageBus())
@@ -182,3 +229,371 @@ async def test_send_reply_infers_topic_from_message_id_cache() -> None:
assert channel._app.bot.sent_messages[0]["message_thread_id"] == 42
assert channel._app.bot.sent_messages[0]["reply_parameters"].message_id == 10
@pytest.mark.asyncio
async def test_group_policy_mention_ignores_unmentioned_group_message() -> None:
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="mention"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
await channel._on_message(_make_telegram_update(text="hello everyone"), None)
assert handled == []
assert channel._app.bot.get_me_calls == 1
@pytest.mark.asyncio
async def test_group_policy_mention_accepts_text_mention_and_caches_bot_identity() -> None:
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="mention"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
mention = SimpleNamespace(type="mention", offset=0, length=13)
await channel._on_message(_make_telegram_update(text="@nanobot_test hi", entities=[mention]), None)
await channel._on_message(_make_telegram_update(text="@nanobot_test again", entities=[mention]), None)
assert len(handled) == 2
assert channel._app.bot.get_me_calls == 1
@pytest.mark.asyncio
async def test_group_policy_mention_accepts_caption_mention() -> None:
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="mention"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
mention = SimpleNamespace(type="mention", offset=0, length=13)
await channel._on_message(
_make_telegram_update(caption="@nanobot_test photo", caption_entities=[mention]),
None,
)
assert len(handled) == 1
assert handled[0]["content"] == "@nanobot_test photo"
@pytest.mark.asyncio
async def test_group_policy_mention_accepts_reply_to_bot() -> None:
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="mention"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
reply = SimpleNamespace(from_user=SimpleNamespace(id=999))
await channel._on_message(_make_telegram_update(text="reply", reply_to_message=reply), None)
assert len(handled) == 1
@pytest.mark.asyncio
async def test_group_policy_open_accepts_plain_group_message() -> None:
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="open"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
await channel._on_message(_make_telegram_update(text="hello group"), None)
assert len(handled) == 1
assert channel._app.bot.get_me_calls == 0
def test_extract_reply_context_no_reply() -> None:
"""When there is no reply_to_message, _extract_reply_context returns None."""
message = SimpleNamespace(reply_to_message=None)
assert TelegramChannel._extract_reply_context(message) is None
def test_extract_reply_context_with_text() -> None:
"""When reply has text, return prefixed string."""
reply = SimpleNamespace(text="Hello world", caption=None)
message = SimpleNamespace(reply_to_message=reply)
assert TelegramChannel._extract_reply_context(message) == "[Reply to: Hello world]"
def test_extract_reply_context_with_caption_only() -> None:
"""When reply has only caption (no text), caption is used."""
reply = SimpleNamespace(text=None, caption="Photo caption")
message = SimpleNamespace(reply_to_message=reply)
assert TelegramChannel._extract_reply_context(message) == "[Reply to: Photo caption]"
def test_extract_reply_context_truncation() -> None:
"""Reply text is truncated at TELEGRAM_REPLY_CONTEXT_MAX_LEN."""
long_text = "x" * (TELEGRAM_REPLY_CONTEXT_MAX_LEN + 100)
reply = SimpleNamespace(text=long_text, caption=None)
message = SimpleNamespace(reply_to_message=reply)
result = TelegramChannel._extract_reply_context(message)
assert result is not None
assert result.startswith("[Reply to: ")
assert result.endswith("...]")
assert len(result) == len("[Reply to: ]") + TELEGRAM_REPLY_CONTEXT_MAX_LEN + len("...")
def test_extract_reply_context_no_text_returns_none() -> None:
"""When reply has no text/caption, _extract_reply_context returns None (media handled separately)."""
reply = SimpleNamespace(text=None, caption=None)
message = SimpleNamespace(reply_to_message=reply)
assert TelegramChannel._extract_reply_context(message) is None
@pytest.mark.asyncio
async def test_on_message_includes_reply_context() -> None:
"""When user replies to a message, content passed to bus starts with reply context."""
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="open"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
reply = SimpleNamespace(text="Hello", message_id=2, from_user=SimpleNamespace(id=1))
update = _make_telegram_update(text="translate this", reply_to_message=reply)
await channel._on_message(update, None)
assert len(handled) == 1
assert handled[0]["content"].startswith("[Reply to: Hello]")
assert "translate this" in handled[0]["content"]
@pytest.mark.asyncio
async def test_download_message_media_returns_path_when_download_succeeds(
monkeypatch, tmp_path
) -> None:
"""_download_message_media returns (paths, content_parts) when bot.get_file and download succeed."""
media_dir = tmp_path / "media" / "telegram"
media_dir.mkdir(parents=True)
monkeypatch.setattr(
"nanobot.channels.telegram.get_media_dir",
lambda channel=None: media_dir if channel else tmp_path / "media",
)
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"]),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
channel._app.bot.get_file = AsyncMock(
return_value=SimpleNamespace(download_to_drive=AsyncMock(return_value=None))
)
msg = SimpleNamespace(
photo=[SimpleNamespace(file_id="fid123", mime_type="image/jpeg")],
voice=None,
audio=None,
document=None,
video=None,
video_note=None,
animation=None,
)
paths, parts = await channel._download_message_media(msg)
assert len(paths) == 1
assert len(parts) == 1
assert "fid123" in paths[0]
assert "[image:" in parts[0]
@pytest.mark.asyncio
async def test_on_message_attaches_reply_to_media_when_available(monkeypatch, tmp_path) -> None:
"""When user replies to a message with media, that media is downloaded and attached to the turn."""
media_dir = tmp_path / "media" / "telegram"
media_dir.mkdir(parents=True)
monkeypatch.setattr(
"nanobot.channels.telegram.get_media_dir",
lambda channel=None: media_dir if channel else tmp_path / "media",
)
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="open"),
MessageBus(),
)
app = _FakeApp(lambda: None)
app.bot.get_file = AsyncMock(
return_value=SimpleNamespace(download_to_drive=AsyncMock(return_value=None))
)
channel._app = app
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
reply_with_photo = SimpleNamespace(
text=None,
caption=None,
photo=[SimpleNamespace(file_id="reply_photo_fid", mime_type="image/jpeg")],
document=None,
voice=None,
audio=None,
video=None,
video_note=None,
animation=None,
)
update = _make_telegram_update(
text="what is the image?",
reply_to_message=reply_with_photo,
)
await channel._on_message(update, None)
assert len(handled) == 1
assert handled[0]["content"].startswith("[Reply to: [image:")
assert "what is the image?" in handled[0]["content"]
assert len(handled[0]["media"]) == 1
assert "reply_photo_fid" in handled[0]["media"][0]
@pytest.mark.asyncio
async def test_on_message_reply_to_media_fallback_when_download_fails() -> None:
"""When reply has media but download fails, no media attached and no reply tag."""
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="open"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
channel._app.bot.get_file = None
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
reply_with_photo = SimpleNamespace(
text=None,
caption=None,
photo=[SimpleNamespace(file_id="x", mime_type="image/jpeg")],
document=None,
voice=None,
audio=None,
video=None,
video_note=None,
animation=None,
)
update = _make_telegram_update(text="what is this?", reply_to_message=reply_with_photo)
await channel._on_message(update, None)
assert len(handled) == 1
assert "what is this?" in handled[0]["content"]
assert handled[0]["media"] == []
@pytest.mark.asyncio
async def test_on_message_reply_to_caption_and_media(monkeypatch, tmp_path) -> None:
"""When replying to a message with caption + photo, both text context and media are included."""
media_dir = tmp_path / "media" / "telegram"
media_dir.mkdir(parents=True)
monkeypatch.setattr(
"nanobot.channels.telegram.get_media_dir",
lambda channel=None: media_dir if channel else tmp_path / "media",
)
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="open"),
MessageBus(),
)
app = _FakeApp(lambda: None)
app.bot.get_file = AsyncMock(
return_value=SimpleNamespace(download_to_drive=AsyncMock(return_value=None))
)
channel._app = app
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
channel._start_typing = lambda _chat_id: None
reply_with_caption_and_photo = SimpleNamespace(
text=None,
caption="A cute cat",
photo=[SimpleNamespace(file_id="cat_fid", mime_type="image/jpeg")],
document=None,
voice=None,
audio=None,
video=None,
video_note=None,
animation=None,
)
update = _make_telegram_update(
text="what breed is this?",
reply_to_message=reply_with_caption_and_photo,
)
await channel._on_message(update, None)
assert len(handled) == 1
assert "[Reply to: A cute cat]" in handled[0]["content"]
assert "what breed is this?" in handled[0]["content"]
assert len(handled[0]["media"]) == 1
assert "cat_fid" in handled[0]["media"][0]
@pytest.mark.asyncio
async def test_forward_command_does_not_inject_reply_context() -> None:
"""Slash commands forwarded via _forward_command must not include reply context."""
channel = TelegramChannel(
TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], group_policy="open"),
MessageBus(),
)
channel._app = _FakeApp(lambda: None)
handled = []
async def capture_handle(**kwargs) -> None:
handled.append(kwargs)
channel._handle_message = capture_handle
reply = SimpleNamespace(text="some old message", message_id=2, from_user=SimpleNamespace(id=1))
update = _make_telegram_update(text="/new", reply_to_message=reply)
await channel._forward_command(update, None)
assert len(handled) == 1
assert handled[0]["content"] == "/new"

67
tests/test_tool_validation.py
View File

@@ -108,6 +108,32 @@ def test_exec_extract_absolute_paths_captures_posix_absolute_paths() -> None:
assert "/tmp/out.txt" in paths
def test_exec_extract_absolute_paths_captures_home_paths() -> None:
cmd = "cat ~/.nanobot/config.json > ~/out.txt"
paths = ExecTool._extract_absolute_paths(cmd)
assert "~/.nanobot/config.json" in paths
assert "~/out.txt" in paths
def test_exec_extract_absolute_paths_captures_quoted_paths() -> None:
cmd = 'cat "/tmp/data.txt" "~/.nanobot/config.json"'
paths = ExecTool._extract_absolute_paths(cmd)
assert "/tmp/data.txt" in paths
assert "~/.nanobot/config.json" in paths
def test_exec_guard_blocks_home_path_outside_workspace(tmp_path) -> None:
tool = ExecTool(restrict_to_workspace=True)
error = tool._guard_command("cat ~/.nanobot/config.json", str(tmp_path))
assert error == "Error: Command blocked by safety guard (path outside working dir)"
def test_exec_guard_blocks_quoted_home_path_outside_workspace(tmp_path) -> None:
tool = ExecTool(restrict_to_workspace=True)
error = tool._guard_command('cat "~/.nanobot/config.json"', str(tmp_path))
assert error == "Error: Command blocked by safety guard (path outside working dir)"
# --- cast_params tests ---
@@ -337,3 +363,44 @@ def test_cast_params_single_value_not_auto_wrapped_to_array() -> None:
assert result["items"] == 5 # Not wrapped to [5]
result = tool.cast_params({"items": "text"})
assert result["items"] == "text" # Not wrapped to ["text"]
# --- ExecTool enhancement tests ---
async def test_exec_always_returns_exit_code() -> None:
"""Exit code should appear in output even on success (exit 0)."""
tool = ExecTool()
result = await tool.execute(command="echo hello")
assert "Exit code: 0" in result
assert "hello" in result
async def test_exec_head_tail_truncation() -> None:
"""Long output should preserve both head and tail."""
tool = ExecTool()
# Generate output that exceeds _MAX_OUTPUT
big = "A" * 6000 + "\n" + "B" * 6000
result = await tool.execute(command=f"echo '{big}'")
assert "chars truncated" in result
# Head portion should start with As
assert result.startswith("A")
# Tail portion should end with the exit code which comes after Bs
assert "Exit code:" in result
async def test_exec_timeout_parameter() -> None:
"""LLM-supplied timeout should override the constructor default."""
tool = ExecTool(timeout=60)
# A very short timeout should cause the command to be killed
result = await tool.execute(command="sleep 10", timeout=1)
assert "timed out" in result
assert "1 seconds" in result
async def test_exec_timeout_capped_at_max() -> None:
"""Timeout values above _MAX_TIMEOUT should be clamped."""
tool = ExecTool()
# Should not raise — just clamp to 600
result = await tool.execute(command="echo ok", timeout=9999)
assert "Exit code: 0" in result