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Merge main into pr-1482

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Re-bin
2026-03-07 15:33:24 +00:00
parent 832e2e8ecd 8a72931b74
commit 515b3588af
40 changed files with 2355 additions and 325 deletions
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3
.gitignore vendored
View File

@@ -1,3 +1,4 @@
.worktrees/
.assets
.env
*.pyc
@@ -19,4 +20,4 @@ __pycache__/
poetry.lock
.pytest_cache/
botpy.log
tests/

47
README.md
View File

@@ -12,11 +12,11 @@
</p>
</div>
🐈 **nanobot** is an **ultra-lightweight** personal AI assistant inspired by [OpenClaw](https://github.com/openclaw/openclaw)
🐈 **nanobot** is an **ultra-lightweight** personal AI assistant inspired by [OpenClaw](https://github.com/openclaw/openclaw).
⚡️ Delivers core agent functionality in just **~4,000** lines of code — **99% smaller** than Clawdbot's 430k+ lines.
⚡️ Delivers core agent functionality with **99% fewer lines of code** than OpenClaw.
📏 Real-time line count: **3,935 lines** (run `bash core_agent_lines.sh` to verify anytime)
📏 Real-time line count: run `bash core_agent_lines.sh` to verify anytime.
## 📢 News
@@ -293,12 +293,18 @@ If you prefer to configure manually, add the following to `~/.nanobot/config.jso
"discord": {
"enabled": true,
"token": "YOUR_BOT_TOKEN",
"allowFrom": ["YOUR_USER_ID"]
"allowFrom": ["YOUR_USER_ID"],
"groupPolicy": "mention"
}
}
}
```
> `groupPolicy` controls how the bot responds in group channels:
> - `"mention"` (default) — Only respond when @mentioned
> - `"open"` — Respond to all messages
> DMs always respond when the sender is in `allowFrom`.
**5. Invite the bot**
- OAuth2 → URL Generator
- Scopes: `bot`
@@ -414,6 +420,10 @@ nanobot channels login
nanobot gateway
```
> WhatsApp bridge updates are not applied automatically for existing installations.
> If you upgrade nanobot and need the latest WhatsApp bridge, run:
> `rm -rf ~/.nanobot/bridge && nanobot channels login`
</details>
<details>
@@ -658,12 +668,14 @@ Config file: `~/.nanobot/config.json`
> - **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) |
| `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) |
| `deepseek` | LLM (DeepSeek direct) | [platform.deepseek.com](https://platform.deepseek.com) |
| `groq` | LLM + **Voice transcription** (Whisper) | [console.groq.com](https://console.groq.com) |
@@ -884,6 +896,33 @@ MCP tools are automatically discovered and registered on startup. The LLM can us
| `channels.*.allowFrom` | `[]` (allow all) | Whitelist of user IDs. Empty = allow everyone; non-empty = only listed users can interact. |
## Multiple Instances
Run multiple nanobot instances simultaneously, each with its own workspace and configuration.
```bash
# Instance A - Telegram bot
nanobot gateway -w ~/.nanobot/botA -p 18791
# Instance B - Discord bot
nanobot gateway -w ~/.nanobot/botB -p 18792
# Instance C - Using custom config file
nanobot gateway -w ~/.nanobot/botC -c ~/.nanobot/botC/config.json -p 18793
```
| Option | Short | Description |
|--------|-------|-------------|
| `--workspace` | `-w` | Workspace directory (default: `~/.nanobot/workspace`) |
| `--config` | `-c` | Config file path (default: `~/.nanobot/config.json`) |
| `--port` | `-p` | Gateway port (default: `18790`) |
Each instance has its own:
- Workspace directory (MEMORY.md, HEARTBEAT.md, session files)
- Cron jobs storage (`workspace/cron/jobs.json`)
- Configuration (if using `--config`)
## CLI Reference
| Command | Description |

89
bridge/src/whatsapp.ts
View File

@@ -9,11 +9,17 @@ import makeWASocket, {
useMultiFileAuthState,
fetchLatestBaileysVersion,
makeCacheableSignalKeyStore,
downloadMediaMessage,
extractMessageContent as baileysExtractMessageContent,
} from '@whiskeysockets/baileys';
import { Boom } from '@hapi/boom';
import qrcode from 'qrcode-terminal';
import pino from 'pino';
import { writeFile, mkdir } from 'fs/promises';
import { join } from 'path';
import { homedir } from 'os';
import { randomBytes } from 'crypto';
const VERSION = '0.1.0';
@@ -24,6 +30,7 @@ export interface InboundMessage {
content: string;
timestamp: number;
isGroup: boolean;
media?: string[];
}
export interface WhatsAppClientOptions {
@@ -110,14 +117,33 @@ export class WhatsAppClient {
if (type !== 'notify') return;
for (const msg of messages) {
// Skip own messages
if (msg.key.fromMe) continue;
// Skip status updates
if (msg.key.remoteJid === 'status@broadcast') continue;
const content = this.extractMessageContent(msg);
if (!content) continue;
const unwrapped = baileysExtractMessageContent(msg.message);
if (!unwrapped) continue;
const content = this.getTextContent(unwrapped);
let fallbackContent: string | null = null;
const mediaPaths: string[] = [];
if (unwrapped.imageMessage) {
fallbackContent = '[Image]';
const path = await this.downloadMedia(msg, unwrapped.imageMessage.mimetype ?? undefined);
if (path) mediaPaths.push(path);
} else if (unwrapped.documentMessage) {
fallbackContent = '[Document]';
const path = await this.downloadMedia(msg, unwrapped.documentMessage.mimetype ?? undefined,
unwrapped.documentMessage.fileName ?? undefined);
if (path) mediaPaths.push(path);
} else if (unwrapped.videoMessage) {
fallbackContent = '[Video]';
const path = await this.downloadMedia(msg, unwrapped.videoMessage.mimetype ?? undefined);
if (path) mediaPaths.push(path);
}
const finalContent = content || (mediaPaths.length === 0 ? fallbackContent : '') || '';
if (!finalContent && mediaPaths.length === 0) continue;
const isGroup = msg.key.remoteJid?.endsWith('@g.us') || false;
@@ -125,18 +151,45 @@ export class WhatsAppClient {
id: msg.key.id || '',
sender: msg.key.remoteJid || '',
pn: msg.key.remoteJidAlt || '',
content,
content: finalContent,
timestamp: msg.messageTimestamp as number,
isGroup,
...(mediaPaths.length > 0 ? { media: mediaPaths } : {}),
});
}
});
}
private extractMessageContent(msg: any): string | null {
const message = msg.message;
if (!message) return null;
private async downloadMedia(msg: any, mimetype?: string, fileName?: string): Promise<string | null> {
try {
const mediaDir = join(homedir(), '.nanobot', 'media');
await mkdir(mediaDir, { recursive: true });
const buffer = await downloadMediaMessage(msg, 'buffer', {}) as Buffer;
let outFilename: string;
if (fileName) {
// Documents have a filename — use it with a unique prefix to avoid collisions
const prefix = `wa_${Date.now()}_${randomBytes(4).toString('hex')}_`;
outFilename = prefix + fileName;
} else {
const mime = mimetype || 'application/octet-stream';
// Derive extension from mimetype subtype (e.g. "image/png" → ".png", "application/pdf" → ".pdf")
const ext = '.' + (mime.split('/').pop()?.split(';')[0] || 'bin');
outFilename = `wa_${Date.now()}_${randomBytes(4).toString('hex')}${ext}`;
}
const filepath = join(mediaDir, outFilename);
await writeFile(filepath, buffer);
return filepath;
} catch (err) {
console.error('Failed to download media:', err);
return null;
}
}
private getTextContent(message: any): string | null {
// Text message
if (message.conversation) {
return message.conversation;
@@ -147,19 +200,19 @@ export class WhatsAppClient {
return message.extendedTextMessage.text;
}
// Image with caption
if (message.imageMessage?.caption) {
return `[Image] ${message.imageMessage.caption}`;
// Image with optional caption
if (message.imageMessage) {
return message.imageMessage.caption || '';
}
// Video with caption
if (message.videoMessage?.caption) {
return `[Video] ${message.videoMessage.caption}`;
// Video with optional caption
if (message.videoMessage) {
return message.videoMessage.caption || '';
}
// Document with caption
if (message.documentMessage?.caption) {
return `[Document] ${message.documentMessage.caption}`;
// Document with optional caption
if (message.documentMessage) {
return message.documentMessage.caption || '';
}
// Voice/Audio message

11
nanobot/agent/context.py
View File

@@ -10,6 +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
class ContextBuilder:
@@ -136,10 +137,14 @@ Reply directly with text for conversations. Only use the 'message' tool to send
images = []
for path in media:
p = Path(path)
mime, _ = mimetypes.guess_type(path)
if not p.is_file() or not mime or not mime.startswith("image/"):
if not p.is_file():
continue
b64 = base64.b64encode(p.read_bytes()).decode()
raw = p.read_bytes()
# Detect real MIME type from magic bytes; fallback to filename guess
mime = detect_image_mime(raw) or mimetypes.guess_type(path)[0]
if not mime or not mime.startswith("image/"):
continue
b64 = base64.b64encode(raw).decode()
images.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}})
if not images:

6
nanobot/agent/loop.py
View File

@@ -202,9 +202,9 @@ class AgentLoop:
if response.has_tool_calls:
if on_progress:
clean = self._strip_think(response.content)
if clean:
await on_progress(clean)
thought = self._strip_think(response.content)
if thought:
await on_progress(thought)
await on_progress(self._tool_hint(response.tool_calls), tool_hint=True)
tool_call_dicts = [

7
nanobot/agent/memory.py
View File

@@ -128,6 +128,13 @@ class MemoryStore:
# 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__)
return False

79
nanobot/agent/tools/base.py
View File

@@ -52,8 +52,79 @@ class Tool(ABC):
"""
pass
def cast_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Apply safe schema-driven casts before validation."""
schema = self.parameters or {}
if schema.get("type", "object") != "object":
return params
return self._cast_object(params, schema)
def _cast_object(self, obj: Any, schema: dict[str, Any]) -> dict[str, Any]:
"""Cast an object (dict) according to schema."""
if not isinstance(obj, dict):
return obj
props = schema.get("properties", {})
result = {}
for key, value in obj.items():
if key in props:
result[key] = self._cast_value(value, props[key])
else:
result[key] = value
return result
def _cast_value(self, val: Any, schema: dict[str, Any]) -> Any:
"""Cast a single value according to schema."""
target_type = schema.get("type")
if target_type == "boolean" and isinstance(val, bool):
return val
if target_type == "integer" and isinstance(val, int) and not isinstance(val, bool):
return val
if target_type in self._TYPE_MAP and target_type not in ("boolean", "integer", "array", "object"):
expected = self._TYPE_MAP[target_type]
if isinstance(val, expected):
return val
if target_type == "integer" and isinstance(val, str):
try:
return int(val)
except ValueError:
return val
if target_type == "number" and isinstance(val, str):
try:
return float(val)
except ValueError:
return val
if target_type == "string":
return val if val is None else str(val)
if target_type == "boolean" and isinstance(val, str):
val_lower = val.lower()
if val_lower in ("true", "1", "yes"):
return True
if val_lower in ("false", "0", "no"):
return False
return val
if target_type == "array" and isinstance(val, list):
item_schema = schema.get("items")
return [self._cast_value(item, item_schema) for item in val] if item_schema else val
if target_type == "object" and isinstance(val, dict):
return self._cast_object(val, schema)
return val
def validate_params(self, params: dict[str, Any]) -> list[str]:
"""Validate tool parameters against JSON schema. Returns error list (empty if valid)."""
if not isinstance(params, dict):
return [f"parameters must be an object, got {type(params).__name__}"]
schema = self.parameters or {}
if schema.get("type", "object") != "object":
raise ValueError(f"Schema must be object type, got {schema.get('type')!r}")
@@ -61,7 +132,13 @@ class Tool(ABC):
def _validate(self, val: Any, schema: dict[str, Any], path: str) -> list[str]:
t, label = schema.get("type"), path or "parameter"
if t in self._TYPE_MAP and not isinstance(val, self._TYPE_MAP[t]):
if t == "integer" and (not isinstance(val, int) or isinstance(val, bool)):
return [f"{label} should be integer"]
if t == "number" and (
not isinstance(val, self._TYPE_MAP[t]) or isinstance(val, bool)
):
return [f"{label} should be number"]
if t in self._TYPE_MAP and t not in ("integer", "number") and not isinstance(val, self._TYPE_MAP[t]):
return [f"{label} should be {t}"]
errors = []

5
nanobot/agent/tools/cron.py
View File

@@ -122,7 +122,10 @@ class CronTool(Tool):
elif at:
from datetime import datetime
dt = datetime.fromisoformat(at)
try:
dt = datetime.fromisoformat(at)
except ValueError:
return f"Error: invalid ISO datetime format '{at}'. Expected format: YYYY-MM-DDTHH:MM:SS"
at_ms = int(dt.timestamp() * 1000)
schedule = CronSchedule(kind="at", at_ms=at_ms)
delete_after = True

11
nanobot/agent/tools/filesystem.py
View File

@@ -26,6 +26,8 @@ def _resolve_path(
class ReadFileTool(Tool):
"""Tool to read file contents."""
_MAX_CHARS = 128_000 # ~128 KB — prevents OOM from reading huge files into LLM context
def __init__(self, workspace: Path | None = None, allowed_dir: Path | None = None):
self._workspace = workspace
self._allowed_dir = allowed_dir
@@ -54,7 +56,16 @@ class ReadFileTool(Tool):
if not file_path.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."
)
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
except PermissionError as e:
return f"Error: {e}"

39
nanobot/agent/tools/mcp.py
View File

@@ -58,17 +58,48 @@ async def connect_mcp_servers(
) -> None:
"""Connect to configured MCP servers and register their tools."""
from mcp import ClientSession, StdioServerParameters
from mcp.client.sse import sse_client
from mcp.client.stdio import stdio_client
from mcp.client.streamable_http import streamable_http_client
for name, cfg in mcp_servers.items():
try:
if cfg.command:
transport_type = cfg.type
if not transport_type:
if cfg.command:
transport_type = "stdio"
elif cfg.url:
# Convention: URLs ending with /sse use SSE transport; others use streamableHttp
transport_type = (
"sse" if cfg.url.rstrip("/").endswith("/sse") else "streamableHttp"
)
else:
logger.warning("MCP server '{}': no command or url configured, skipping", name)
continue
if transport_type == "stdio":
params = StdioServerParameters(
command=cfg.command, args=cfg.args, env=cfg.env or None
)
read, write = await stack.enter_async_context(stdio_client(params))
elif cfg.url:
from mcp.client.streamable_http import streamable_http_client
elif transport_type == "sse":
def httpx_client_factory(
headers: dict[str, str] | None = None,
timeout: httpx.Timeout | None = None,
auth: httpx.Auth | None = None,
) -> httpx.AsyncClient:
merged_headers = {**(cfg.headers or {}), **(headers or {})}
return httpx.AsyncClient(
headers=merged_headers or None,
follow_redirects=True,
timeout=timeout,
auth=auth,
)
read, write = await stack.enter_async_context(
sse_client(cfg.url, httpx_client_factory=httpx_client_factory)
)
elif transport_type == "streamableHttp":
# Always provide an explicit httpx client so MCP HTTP transport does not
# inherit httpx's default 5s timeout and preempt the higher-level tool timeout.
http_client = await stack.enter_async_context(
@@ -82,7 +113,7 @@ async def connect_mcp_servers(
streamable_http_client(cfg.url, http_client=http_client)
)
else:
logger.warning("MCP server '{}': no command or url configured, skipping", name)
logger.warning("MCP server '{}': unknown transport type '{}'", name, transport_type)
continue
session = await stack.enter_async_context(ClientSession(read, write))

2
nanobot/agent/tools/message.py
View File

@@ -96,7 +96,7 @@ class MessageTool(Tool):
media=media or [],
metadata={
"message_id": message_id,
}
},
)
try:

4
nanobot/agent/tools/registry.py
View File

@@ -44,6 +44,10 @@ class ToolRegistry:
return f"Error: Tool '{name}' not found. Available: {', '.join(self.tool_names)}"
try:
# Attempt to cast parameters to match schema types
params = tool.cast_params(params)
# Validate parameters
errors = tool.validate_params(params)
if errors:
return f"Error: Invalid parameters for tool '{name}': " + "; ".join(errors) + _HINT

129
nanobot/channels/discord.py
View File

@@ -13,34 +13,13 @@ from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.base import BaseChannel
from nanobot.config.schema import DiscordConfig
from nanobot.utils.helpers import split_message
DISCORD_API_BASE = "https://discord.com/api/v10"
MAX_ATTACHMENT_BYTES = 20 * 1024 * 1024 # 20MB
MAX_MESSAGE_LEN = 2000 # Discord message character limit
def _split_message(content: str, max_len: int = MAX_MESSAGE_LEN) -> list[str]:
"""Split content into chunks within max_len, preferring line breaks."""
if not content:
return []
if len(content) <= max_len:
return [content]
chunks: list[str] = []
while content:
if len(content) <= max_len:
chunks.append(content)
break
cut = content[:max_len]
pos = cut.rfind('\n')
if pos <= 0:
pos = cut.rfind(' ')
if pos <= 0:
pos = max_len
chunks.append(content[:pos])
content = content[pos:].lstrip()
return chunks
class DiscordChannel(BaseChannel):
"""Discord channel using Gateway websocket."""
@@ -54,6 +33,7 @@ class DiscordChannel(BaseChannel):
self._heartbeat_task: asyncio.Task | None = None
self._typing_tasks: dict[str, asyncio.Task] = {}
self._http: httpx.AsyncClient | None = None
self._bot_user_id: str | None = None
async def start(self) -> None:
"""Start the Discord gateway connection."""
@@ -95,7 +75,7 @@ class DiscordChannel(BaseChannel):
self._http = None
async def send(self, msg: OutboundMessage) -> None:
"""Send a message through Discord REST API."""
"""Send a message through Discord REST API, including file attachments."""
if not self._http:
logger.warning("Discord HTTP client not initialized")
return
@@ -104,15 +84,31 @@ class DiscordChannel(BaseChannel):
headers = {"Authorization": f"Bot {self.config.token}"}
try:
chunks = _split_message(msg.content or "")
sent_media = False
failed_media: list[str] = []
# Send file attachments first
for media_path in msg.media or []:
if await self._send_file(url, headers, media_path, reply_to=msg.reply_to):
sent_media = True
else:
failed_media.append(Path(media_path).name)
# Send text content
chunks = split_message(msg.content or "", MAX_MESSAGE_LEN)
if not chunks and failed_media and not sent_media:
chunks = split_message(
"\n".join(f"[attachment: {name} - send failed]" for name in failed_media),
MAX_MESSAGE_LEN,
)
if not chunks:
return
for i, chunk in enumerate(chunks):
payload: dict[str, Any] = {"content": chunk}
# Only set reply reference on the first chunk
if i == 0 and msg.reply_to:
# Let the first successful attachment carry the reply if present.
if i == 0 and msg.reply_to and not sent_media:
payload["message_reference"] = {"message_id": msg.reply_to}
payload["allowed_mentions"] = {"replied_user": False}
@@ -143,6 +139,54 @@ class DiscordChannel(BaseChannel):
await asyncio.sleep(1)
return False
async def _send_file(
self,
url: str,
headers: dict[str, str],
file_path: str,
reply_to: str | None = None,
) -> bool:
"""Send a file attachment via Discord REST API using multipart/form-data."""
path = Path(file_path)
if not path.is_file():
logger.warning("Discord file not found, skipping: {}", file_path)
return False
if path.stat().st_size > MAX_ATTACHMENT_BYTES:
logger.warning("Discord file too large (>20MB), skipping: {}", path.name)
return False
payload_json: dict[str, Any] = {}
if reply_to:
payload_json["message_reference"] = {"message_id": reply_to}
payload_json["allowed_mentions"] = {"replied_user": False}
for attempt in range(3):
try:
with open(path, "rb") as f:
files = {"files[0]": (path.name, f, "application/octet-stream")}
data: dict[str, Any] = {}
if payload_json:
data["payload_json"] = json.dumps(payload_json)
response = await self._http.post(
url, headers=headers, files=files, data=data
)
if response.status_code == 429:
resp_data = response.json()
retry_after = float(resp_data.get("retry_after", 1.0))
logger.warning("Discord rate limited, retrying in {}s", retry_after)
await asyncio.sleep(retry_after)
continue
response.raise_for_status()
logger.info("Discord file sent: {}", path.name)
return True
except Exception as e:
if attempt == 2:
logger.error("Error sending Discord file {}: {}", path.name, e)
else:
await asyncio.sleep(1)
return False
async def _gateway_loop(self) -> None:
"""Main gateway loop: identify, heartbeat, dispatch events."""
if not self._ws:
@@ -170,6 +214,10 @@ class DiscordChannel(BaseChannel):
await self._identify()
elif op == 0 and event_type == "READY":
logger.info("Discord gateway READY")
# Capture bot user ID for mention detection
user_data = payload.get("user") or {}
self._bot_user_id = user_data.get("id")
logger.info("Discord bot connected as user {}", self._bot_user_id)
elif op == 0 and event_type == "MESSAGE_CREATE":
await self._handle_message_create(payload)
elif op == 7:
@@ -226,6 +274,7 @@ class DiscordChannel(BaseChannel):
sender_id = str(author.get("id", ""))
channel_id = str(payload.get("channel_id", ""))
content = payload.get("content") or ""
guild_id = payload.get("guild_id")
if not sender_id or not channel_id:
return
@@ -233,6 +282,11 @@ class DiscordChannel(BaseChannel):
if not self.is_allowed(sender_id):
return
# Check group channel policy (DMs always respond if is_allowed passes)
if guild_id is not None:
if not self._should_respond_in_group(payload, content):
return
content_parts = [content] if content else []
media_paths: list[str] = []
media_dir = Path.home() / ".nanobot" / "media"
@@ -269,11 +323,32 @@ class DiscordChannel(BaseChannel):
media=media_paths,
metadata={
"message_id": str(payload.get("id", "")),
"guild_id": payload.get("guild_id"),
"guild_id": guild_id,
"reply_to": reply_to,
},
)
def _should_respond_in_group(self, payload: dict[str, Any], content: str) -> bool:
"""Check if bot should respond in a group channel based on policy."""
if self.config.group_policy == "open":
return True
if self.config.group_policy == "mention":
# Check if bot was mentioned in the message
if self._bot_user_id:
# Check mentions array
mentions = payload.get("mentions") or []
for mention in mentions:
if str(mention.get("id")) == self._bot_user_id:
return True
# Also check content for mention format <@USER_ID>
if f"<@{self._bot_user_id}>" in content or f"<@!{self._bot_user_id}>" in content:
return True
logger.debug("Discord message in {} ignored (bot not mentioned)", payload.get("channel_id"))
return False
return True
async def _start_typing(self, channel_id: str) -> None:
"""Start periodic typing indicator for a channel."""
await self._stop_typing(channel_id)

307
nanobot/channels/feishu.py
View File

@@ -16,26 +16,9 @@ from nanobot.bus.queue import MessageBus
from nanobot.channels.base import BaseChannel
from nanobot.config.schema import FeishuConfig
try:
import lark_oapi as lark
from lark_oapi.api.im.v1 import (
CreateFileRequest,
CreateFileRequestBody,
CreateImageRequest,
CreateImageRequestBody,
CreateMessageReactionRequest,
CreateMessageReactionRequestBody,
CreateMessageRequest,
CreateMessageRequestBody,
Emoji,
GetMessageResourceRequest,
P2ImMessageReceiveV1,
)
FEISHU_AVAILABLE = True
except ImportError:
FEISHU_AVAILABLE = False
lark = None
Emoji = None
import importlib.util
FEISHU_AVAILABLE = importlib.util.find_spec("lark_oapi") is not None
# Message type display mapping
MSG_TYPE_MAP = {
@@ -261,15 +244,22 @@ class FeishuChannel(BaseChannel):
name = "feishu"
def __init__(self, config: FeishuConfig, bus: MessageBus):
def __init__(self, config: FeishuConfig, bus: MessageBus, groq_api_key: str = ""):
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
self._processed_message_ids: OrderedDict[str, None] = OrderedDict() # Ordered dedup cache
self._loop: asyncio.AbstractEventLoop | None = None
@staticmethod
def _register_optional_event(builder: Any, method_name: str, handler: Any) -> Any:
"""Register an event handler only when the SDK supports it."""
method = getattr(builder, method_name, None)
return method(handler) if callable(method) else builder
async def start(self) -> None:
"""Start the Feishu bot with WebSocket long connection."""
if not FEISHU_AVAILABLE:
@@ -280,6 +270,7 @@ class FeishuChannel(BaseChannel):
logger.error("Feishu app_id and app_secret not configured")
return
import lark_oapi as lark
self._running = True
self._loop = asyncio.get_running_loop()
@@ -289,14 +280,24 @@ class FeishuChannel(BaseChannel):
.app_secret(self.config.app_secret) \
.log_level(lark.LogLevel.INFO) \
.build()
# Create event handler (only register message receive, ignore other events)
event_handler = lark.EventDispatcherHandler.builder(
builder = lark.EventDispatcherHandler.builder(
self.config.encrypt_key or "",
self.config.verification_token or "",
).register_p2_im_message_receive_v1(
self._on_message_sync
).build()
)
builder = self._register_optional_event(
builder, "register_p2_im_message_reaction_created_v1", self._on_reaction_created
)
builder = self._register_optional_event(
builder, "register_p2_im_message_message_read_v1", self._on_message_read
)
builder = self._register_optional_event(
builder,
"register_p2_im_chat_access_event_bot_p2p_chat_entered_v1",
self._on_bot_p2p_chat_entered,
)
event_handler = builder.build()
# Create WebSocket client for long connection
self._ws_client = lark.ws.Client(
@@ -306,16 +307,28 @@ class FeishuChannel(BaseChannel):
log_level=lark.LogLevel.INFO
)
# Start WebSocket client in a separate thread with reconnect loop
# Start WebSocket client in a separate thread with reconnect loop.
# A dedicated event loop is created for this thread so that lark_oapi's
# module-level `loop = asyncio.get_event_loop()` picks up an idle loop
# instead of the already-running main asyncio loop, which would cause
# "This event loop is already running" errors.
def run_ws():
while self._running:
try:
self._ws_client.start()
except Exception as e:
logger.warning("Feishu WebSocket error: {}", e)
if self._running:
import time
time.sleep(5)
import time
import lark_oapi.ws.client as _lark_ws_client
ws_loop = asyncio.new_event_loop()
asyncio.set_event_loop(ws_loop)
# Patch the module-level loop used by lark's ws Client.start()
_lark_ws_client.loop = ws_loop
try:
while self._running:
try:
self._ws_client.start()
except Exception as e:
logger.warning("Feishu WebSocket error: {}", e)
if self._running:
time.sleep(5)
finally:
ws_loop.close()
self._ws_thread = threading.Thread(target=run_ws, daemon=True)
self._ws_thread.start()
@@ -340,6 +353,7 @@ class FeishuChannel(BaseChannel):
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
try:
request = CreateMessageReactionRequest.builder() \
.message_id(message_id) \
@@ -364,7 +378,7 @@ class FeishuChannel(BaseChannel):
Common emoji types: THUMBSUP, OK, EYES, DONE, OnIt, HEART
"""
if not self._client or not Emoji:
if not self._client:
return
loop = asyncio.get_running_loop()
@@ -413,6 +427,34 @@ class FeishuChannel(BaseChannel):
elements.extend(self._split_headings(remaining))
return elements or [{"tag": "markdown", "content": content}]
@staticmethod
def _split_elements_by_table_limit(elements: list[dict], max_tables: int = 1) -> list[list[dict]]:
"""Split card elements into groups with at most *max_tables* table elements each.
Feishu cards have a hard limit of one table per card (API error 11310).
When the rendered content contains multiple markdown tables each table is
placed in a separate card message so every table reaches the user.
"""
if not elements:
return [[]]
groups: list[list[dict]] = []
current: list[dict] = []
table_count = 0
for el in elements:
if el.get("tag") == "table":
if table_count >= max_tables:
if current:
groups.append(current)
current = []
table_count = 0
current.append(el)
table_count += 1
else:
current.append(el)
if current:
groups.append(current)
return groups or [[]]
def _split_headings(self, content: str) -> list[dict]:
"""Split content by headings, converting headings to div elements."""
protected = content
@@ -447,8 +489,124 @@ class FeishuChannel(BaseChannel):
return elements or [{"tag": "markdown", "content": content}]
# ── Smart format detection ──────────────────────────────────────────
# Patterns that indicate "complex" markdown needing card rendering
_COMPLEX_MD_RE = re.compile(
r"```" # fenced code block
r"|^\|.+\|.*\n\s*\|[-:\s|]+\|" # markdown table (header + separator)
r"|^#{1,6}\s+" # headings
, re.MULTILINE,
)
# Simple markdown patterns (bold, italic, strikethrough)
_SIMPLE_MD_RE = re.compile(
r"\*\*.+?\*\*" # **bold**
r"|__.+?__" # __bold__
r"|(?<!\*)\*(?!\*)(.+?)(?<!\*)\*(?!\*)" # *italic* (single *)
r"|~~.+?~~" # ~~strikethrough~~
, re.DOTALL,
)
# Markdown link: [text](url)
_MD_LINK_RE = re.compile(r"\[([^\]]+)\]\((https?://[^\)]+)\)")
# Unordered list items
_LIST_RE = re.compile(r"^[\s]*[-*+]\s+", re.MULTILINE)
# Ordered list items
_OLIST_RE = re.compile(r"^[\s]*\d+\.\s+", re.MULTILINE)
# Max length for plain text format
_TEXT_MAX_LEN = 200
# Max length for post (rich text) format; beyond this, use card
_POST_MAX_LEN = 2000
@classmethod
def _detect_msg_format(cls, content: str) -> str:
"""Determine the optimal Feishu message format for *content*.
Returns one of:
- ``"text"`` – plain text, short and no markdown
- ``"post"`` – rich text (links only, moderate length)
- ``"interactive"`` – card with full markdown rendering
"""
stripped = content.strip()
# Complex markdown (code blocks, tables, headings) → always card
if cls._COMPLEX_MD_RE.search(stripped):
return "interactive"
# Long content → card (better readability with card layout)
if len(stripped) > cls._POST_MAX_LEN:
return "interactive"
# Has bold/italic/strikethrough → card (post format can't render these)
if cls._SIMPLE_MD_RE.search(stripped):
return "interactive"
# Has list items → card (post format can't render list bullets well)
if cls._LIST_RE.search(stripped) or cls._OLIST_RE.search(stripped):
return "interactive"
# Has links → post format (supports <a> tags)
if cls._MD_LINK_RE.search(stripped):
return "post"
# Short plain text → text format
if len(stripped) <= cls._TEXT_MAX_LEN:
return "text"
# Medium plain text without any formatting → post format
return "post"
@classmethod
def _markdown_to_post(cls, content: str) -> str:
"""Convert markdown content to Feishu post message JSON.
Handles links ``[text](url)`` as ``a`` tags; everything else as ``text`` tags.
Each line becomes a paragraph (row) in the post body.
"""
lines = content.strip().split("\n")
paragraphs: list[list[dict]] = []
for line in lines:
elements: list[dict] = []
last_end = 0
for m in cls._MD_LINK_RE.finditer(line):
# Text before this link
before = line[last_end:m.start()]
if before:
elements.append({"tag": "text", "text": before})
elements.append({
"tag": "a",
"text": m.group(1),
"href": m.group(2),
})
last_end = m.end()
# Remaining text after last link
remaining = line[last_end:]
if remaining:
elements.append({"tag": "text", "text": remaining})
# Empty line → empty paragraph for spacing
if not elements:
elements.append({"tag": "text", "text": ""})
paragraphs.append(elements)
post_body = {
"zh_cn": {
"content": paragraphs,
}
}
return json.dumps(post_body, ensure_ascii=False)
_IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".gif", ".bmp", ".webp", ".ico", ".tiff", ".tif"}
_AUDIO_EXTS = {".opus"}
_VIDEO_EXTS = {".mp4", ".mov", ".avi"}
_FILE_TYPE_MAP = {
".opus": "opus", ".mp4": "mp4", ".pdf": "pdf", ".doc": "doc", ".docx": "doc",
".xls": "xls", ".xlsx": "xls", ".ppt": "ppt", ".pptx": "ppt",
@@ -456,6 +614,7 @@ class FeishuChannel(BaseChannel):
def _upload_image_sync(self, file_path: str) -> str | None:
"""Upload an image to Feishu and return the image_key."""
from lark_oapi.api.im.v1 import CreateImageRequest, CreateImageRequestBody
try:
with open(file_path, "rb") as f:
request = CreateImageRequest.builder() \
@@ -479,6 +638,7 @@ class FeishuChannel(BaseChannel):
def _upload_file_sync(self, file_path: str) -> str | None:
"""Upload a file to Feishu and return the file_key."""
from lark_oapi.api.im.v1 import CreateFileRequest, CreateFileRequestBody
ext = os.path.splitext(file_path)[1].lower()
file_type = self._FILE_TYPE_MAP.get(ext, "stream")
file_name = os.path.basename(file_path)
@@ -506,6 +666,7 @@ class FeishuChannel(BaseChannel):
def _download_image_sync(self, message_id: str, image_key: str) -> tuple[bytes | None, str | None]:
"""Download an image from Feishu message by message_id and image_key."""
from lark_oapi.api.im.v1 import GetMessageResourceRequest
try:
request = GetMessageResourceRequest.builder() \
.message_id(message_id) \
@@ -530,6 +691,13 @@ class FeishuChannel(BaseChannel):
self, message_id: str, file_key: str, resource_type: str = "file"
) -> tuple[bytes | None, str | None]:
"""Download a file/audio/media from a Feishu message by message_id and file_key."""
from lark_oapi.api.im.v1 import GetMessageResourceRequest
# Feishu API only accepts 'image' or 'file' as type parameter
# Convert 'audio' to 'file' for API compatibility
if resource_type == "audio":
resource_type = "file"
try:
request = (
GetMessageResourceRequest.builder()
@@ -598,6 +766,7 @@ class FeishuChannel(BaseChannel):
def _send_message_sync(self, receive_id_type: str, receive_id: str, msg_type: str, content: str) -> bool:
"""Send a single message (text/image/file/interactive) synchronously."""
from lark_oapi.api.im.v1 import CreateMessageRequest, CreateMessageRequestBody
try:
request = CreateMessageRequest.builder() \
.receive_id_type(receive_id_type) \
@@ -646,23 +815,50 @@ class FeishuChannel(BaseChannel):
else:
key = await loop.run_in_executor(None, self._upload_file_sync, file_path)
if key:
media_type = "audio" if ext in self._AUDIO_EXTS else "file"
# Use msg_type "media" for audio/video so users can play inline;
# "file" for everything else (documents, archives, etc.)
if ext in self._AUDIO_EXTS or ext in self._VIDEO_EXTS:
media_type = "media"
else:
media_type = "file"
await loop.run_in_executor(
None, self._send_message_sync,
receive_id_type, msg.chat_id, media_type, json.dumps({"file_key": key}, ensure_ascii=False),
)
if msg.content and msg.content.strip():
card = {"config": {"wide_screen_mode": True}, "elements": self._build_card_elements(msg.content)}
await loop.run_in_executor(
None, self._send_message_sync,
receive_id_type, msg.chat_id, "interactive", json.dumps(card, ensure_ascii=False),
)
fmt = self._detect_msg_format(msg.content)
if fmt == "text":
# Short plain text – send as simple text message
text_body = json.dumps({"text": msg.content.strip()}, ensure_ascii=False)
await loop.run_in_executor(
None, self._send_message_sync,
receive_id_type, msg.chat_id, "text", text_body,
)
elif fmt == "post":
# Medium content with links – send as rich-text post
post_body = self._markdown_to_post(msg.content)
await loop.run_in_executor(
None, self._send_message_sync,
receive_id_type, msg.chat_id, "post", post_body,
)
else:
# Complex / long content – send as interactive card
elements = self._build_card_elements(msg.content)
for chunk in self._split_elements_by_table_limit(elements):
card = {"config": {"wide_screen_mode": True}, "elements": chunk}
await loop.run_in_executor(
None, self._send_message_sync,
receive_id_type, msg.chat_id, "interactive", json.dumps(card, ensure_ascii=False),
)
except Exception as e:
logger.error("Error sending Feishu message: {}", e)
def _on_message_sync(self, data: "P2ImMessageReceiveV1") -> None:
def _on_message_sync(self, data: Any) -> None:
"""
Sync handler for incoming messages (called from WebSocket thread).
Schedules async handling in the main event loop.
@@ -670,7 +866,7 @@ class FeishuChannel(BaseChannel):
if self._loop and self._loop.is_running():
asyncio.run_coroutine_threadsafe(self._on_message(data), self._loop)
async def _on_message(self, data: "P2ImMessageReceiveV1") -> None:
async def _on_message(self, data: Any) -> None:
"""Handle incoming message from Feishu."""
try:
event = data.event
@@ -730,6 +926,18 @@ class FeishuChannel(BaseChannel):
file_path, content_text = await self._download_and_save_media(msg_type, content_json, message_id)
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 transcription:
content_text = f"[transcription: {transcription}]"
except Exception as e:
logger.warning("Failed to transcribe audio: {}", e)
content_parts.append(content_text)
elif msg_type in ("share_chat", "share_user", "interactive", "share_calendar_event", "system", "merge_forward"):
@@ -762,3 +970,16 @@ class FeishuChannel(BaseChannel):
except Exception as e:
logger.error("Error processing Feishu message: {}", e)
def _on_reaction_created(self, data: Any) -> None:
"""Ignore reaction events so they do not generate SDK noise."""
pass
def _on_message_read(self, data: Any) -> None:
"""Ignore read events so they do not generate SDK noise."""
pass
def _on_bot_p2p_chat_entered(self, data: Any) -> None:
"""Ignore p2p-enter events when a user opens a bot chat."""
logger.debug("Bot entered p2p chat (user opened chat window)")
pass

3
nanobot/channels/manager.py
View File

@@ -74,7 +74,8 @@ class ChannelManager:
try:
from nanobot.channels.feishu import FeishuChannel
self.channels["feishu"] = FeishuChannel(
self.config.channels.feishu, self.bus
self.config.channels.feishu, self.bus,
groq_api_key=self.config.providers.groq.api_key,
)
logger.info("Feishu channel enabled")
except ImportError as e:

4
nanobot/channels/qq.py
View File

@@ -56,6 +56,7 @@ class QQChannel(BaseChannel):
self.config: QQConfig = config
self._client: "botpy.Client | None" = None
self._processed_ids: deque = deque(maxlen=1000)
self._msg_seq: int = 1 # 消息序列号，避免被 QQ API 去重
async def start(self) -> None:
"""Start the QQ bot."""
@@ -102,11 +103,13 @@ class QQChannel(BaseChannel):
return
try:
msg_id = msg.metadata.get("message_id")
self._msg_seq += 1 # 递增序列号
await self._client.api.post_c2c_message(
openid=msg.chat_id,
msg_type=0,
content=msg.content,
msg_id=msg_id,
msg_seq=self._msg_seq, # 添加序列号避免去重
)
except Exception as e:
logger.error("Error sending QQ message: {}", e)
@@ -133,3 +136,4 @@ class QQChannel(BaseChannel):
)
except Exception:
logger.exception("Error handling QQ message")

199
nanobot/channels/telegram.py
View File

@@ -4,6 +4,8 @@ from __future__ import annotations
import asyncio
import re
import time
import unicodedata
from loguru import logger
from telegram import BotCommand, ReplyParameters, Update
@@ -14,6 +16,50 @@ from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.base import BaseChannel
from nanobot.config.schema import TelegramConfig
from nanobot.utils.helpers import split_message
TELEGRAM_MAX_MESSAGE_LEN = 4000 # Telegram message character limit
def _strip_md(s: str) -> str:
"""Strip markdown inline formatting from text."""
s = re.sub(r'\*\*(.+?)\*\*', r'\1', s)
s = re.sub(r'__(.+?)__', r'\1', s)
s = re.sub(r'~~(.+?)~~', r'\1', s)
s = re.sub(r'`([^`]+)`', r'\1', s)
return s.strip()
def _render_table_box(table_lines: list[str]) -> str:
"""Convert markdown pipe-table to compact aligned text for <pre> display."""
def dw(s: str) -> int:
return sum(2 if unicodedata.east_asian_width(c) in ('W', 'F') else 1 for c in s)
rows: list[list[str]] = []
has_sep = False
for line in table_lines:
cells = [_strip_md(c) for c in line.strip().strip('|').split('|')]
if all(re.match(r'^:?-+:?$', c) for c in cells if c):
has_sep = True
continue
rows.append(cells)
if not rows or not has_sep:
return '\n'.join(table_lines)
ncols = max(len(r) for r in rows)
for r in rows:
r.extend([''] * (ncols - len(r)))
widths = [max(dw(r[c]) for r in rows) for c in range(ncols)]
def dr(cells: list[str]) -> str:
return ' '.join(f'{c}{" " * (w - dw(c))}' for c, w in zip(cells, widths))
out = [dr(rows[0])]
out.append(' '.join('─' * w for w in widths))
for row in rows[1:]:
out.append(dr(row))
return '\n'.join(out)
def _markdown_to_telegram_html(text: str) -> str:
@@ -31,6 +77,27 @@ def _markdown_to_telegram_html(text: str) -> str:
text = re.sub(r'```[\w]*\n?([\s\S]*?)```', save_code_block, text)
# 1.5. Convert markdown tables to box-drawing (reuse code_block placeholders)
lines = text.split('\n')
rebuilt: list[str] = []
li = 0
while li < len(lines):
if re.match(r'^\s*\|.+\|', lines[li]):
tbl: list[str] = []
while li < len(lines) and re.match(r'^\s*\|.+\|', lines[li]):
tbl.append(lines[li])
li += 1
box = _render_table_box(tbl)
if box != '\n'.join(tbl):
code_blocks.append(box)
rebuilt.append(f"\x00CB{len(code_blocks) - 1}\x00")
else:
rebuilt.extend(tbl)
else:
rebuilt.append(lines[li])
li += 1
text = '\n'.join(rebuilt)
# 2. Extract and protect inline code
inline_codes: list[str] = []
def save_inline_code(m: re.Match) -> str:
@@ -79,26 +146,6 @@ def _markdown_to_telegram_html(text: str) -> str:
return text
def _split_message(content: str, max_len: int = 4000) -> list[str]:
"""Split content into chunks within max_len, preferring line breaks."""
if len(content) <= max_len:
return [content]
chunks: list[str] = []
while content:
if len(content) <= max_len:
chunks.append(content)
break
cut = content[:max_len]
pos = cut.rfind('\n')
if pos == -1:
pos = cut.rfind(' ')
if pos == -1:
pos = max_len
chunks.append(content[:pos])
content = content[pos:].lstrip()
return chunks
class TelegramChannel(BaseChannel):
"""
Telegram channel using long polling.
@@ -130,6 +177,7 @@ class TelegramChannel(BaseChannel):
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] = {}
async def start(self) -> None:
"""Start the Telegram bot with long polling."""
@@ -140,16 +188,21 @@ class TelegramChannel(BaseChannel):
self._running = True
# Build the application with larger connection pool to avoid pool-timeout on long runs
req = HTTPXRequest(connection_pool_size=16, pool_timeout=5.0, connect_timeout=30.0, read_timeout=30.0)
req = HTTPXRequest(
connection_pool_size=16,
pool_timeout=5.0,
connect_timeout=30.0,
read_timeout=30.0,
proxy=self.config.proxy if self.config.proxy else None,
)
builder = Application.builder().token(self.config.token).request(req).get_updates_request(req)
if self.config.proxy:
builder = builder.proxy(self.config.proxy).get_updates_proxy(self.config.proxy)
self._app = builder.build()
self._app.add_error_handler(self._on_error)
# Add command handlers
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("help", self._on_help))
# Add message handler for text, photos, voice, documents
@@ -225,21 +278,25 @@ class TelegramChannel(BaseChannel):
logger.warning("Telegram bot not running")
return
self._stop_typing(msg.chat_id)
# Only stop typing indicator for final responses
if not msg.metadata.get("_progress", False):
self._stop_typing(msg.chat_id)
try:
chat_id = int(msg.chat_id)
except ValueError:
logger.error("Invalid chat_id: {}", msg.chat_id)
return
reply_to_message_id = msg.metadata.get("message_id")
message_thread_id = msg.metadata.get("message_thread_id")
if message_thread_id is None and reply_to_message_id is not None:
message_thread_id = self._message_threads.get((msg.chat_id, reply_to_message_id))
thread_kwargs = {}
if message_thread_id is not None:
thread_kwargs["message_thread_id"] = message_thread_id
reply_params = None
if self.config.reply_to_message:
reply_to_message_id = msg.metadata.get("message_id")
if reply_to_message_id:
reply_params = ReplyParameters(
message_id=reply_to_message_id,
@@ -275,27 +332,65 @@ class TelegramChannel(BaseChannel):
# Send text content
if msg.content and msg.content != "[empty message]":
for chunk in _split_message(msg.content):
try:
html = _markdown_to_telegram_html(chunk)
await self._app.bot.send_message(
chat_id=chat_id,
text=html,
parse_mode="HTML",
reply_parameters=reply_params,
**thread_kwargs,
)
except Exception as e:
logger.warning("HTML parse failed, falling back to plain text: {}", e)
try:
await self._app.bot.send_message(
chat_id=chat_id,
text=chunk,
reply_parameters=reply_params,
**thread_kwargs,
)
except Exception as e2:
logger.error("Error sending Telegram message: {}", e2)
is_progress = msg.metadata.get("_progress", False)
for chunk in split_message(msg.content, TELEGRAM_MAX_MESSAGE_LEN):
# Final response: simulate streaming via draft, then persist
if not is_progress:
await self._send_with_streaming(chat_id, chunk, reply_params, thread_kwargs)
else:
await self._send_text(chat_id, chunk, reply_params, thread_kwargs)
async def _send_text(
self,
chat_id: int,
text: str,
reply_params=None,
thread_kwargs: dict | None = None,
) -> None:
"""Send a plain text message with HTML fallback."""
try:
html = _markdown_to_telegram_html(text)
await self._app.bot.send_message(
chat_id=chat_id, text=html, parse_mode="HTML",
reply_parameters=reply_params,
**(thread_kwargs or {}),
)
except Exception as e:
logger.warning("HTML parse failed, falling back to plain text: {}", e)
try:
await self._app.bot.send_message(
chat_id=chat_id,
text=text,
reply_parameters=reply_params,
**(thread_kwargs or {}),
)
except Exception as e2:
logger.error("Error sending Telegram message: {}", e2)
async def _send_with_streaming(
self,
chat_id: int,
text: str,
reply_params=None,
thread_kwargs: dict | None = None,
) -> None:
"""Simulate streaming via send_message_draft, then persist with send_message."""
draft_id = int(time.time() * 1000) % (2**31)
try:
step = max(len(text) // 8, 40)
for i in range(step, len(text), step):
await self._app.bot.send_message_draft(
chat_id=chat_id, draft_id=draft_id, text=text[:i],
)
await asyncio.sleep(0.04)
await self._app.bot.send_message_draft(
chat_id=chat_id, draft_id=draft_id, text=text,
)
await asyncio.sleep(0.15)
except Exception:
pass
await self._send_text(chat_id, text, reply_params, thread_kwargs)
async def _on_start(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""Handle /start command."""
@@ -347,12 +442,23 @@ class TelegramChannel(BaseChannel):
"is_forum": bool(getattr(message.chat, "is_forum", False)),
}
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)
if message_thread_id is None:
return
key = (str(message.chat_id), message.message_id)
self._message_threads[key] = message_thread_id
if len(self._message_threads) > 1000:
self._message_threads.pop(next(iter(self._message_threads)))
async def _forward_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
"""Forward slash commands to the bus for unified handling in AgentLoop."""
if not update.message or not update.effective_user:
return
message = update.message
user = update.effective_user
self._remember_thread_context(message)
await self._handle_message(
sender_id=self._sender_id(user),
chat_id=str(message.chat_id),
@@ -370,6 +476,7 @@ class TelegramChannel(BaseChannel):
user = update.effective_user
chat_id = message.chat_id
sender_id = self._sender_id(user)
self._remember_thread_context(message)
# Store chat_id for replies
self._chat_ids[sender_id] = chat_id

13
nanobot/channels/whatsapp.py
View File

@@ -2,6 +2,7 @@
import asyncio
import json
import mimetypes
from collections import OrderedDict
from loguru import logger
@@ -128,10 +129,22 @@ class WhatsAppChannel(BaseChannel):
logger.info("Voice message received from {}, but direct download from bridge is not yet supported.", sender_id)
content = "[Voice Message: Transcription not available for WhatsApp yet]"
# Extract media paths (images/documents/videos downloaded by the bridge)
media_paths = data.get("media") or []
# Build content tags matching Telegram's pattern: [image: /path] or [file: /path]
if media_paths:
for p in media_paths:
mime, _ = mimetypes.guess_type(p)
media_type = "image" if mime and mime.startswith("image/") else "file"
media_tag = f"[{media_type}: {p}]"
content = f"{content}\n{media_tag}" if content else media_tag
await self._handle_message(
sender_id=sender_id,
chat_id=sender, # Use full LID for replies
content=content,
media=media_paths,
metadata={
"message_id": message_id,
"timestamp": data.get("timestamp"),

62
nanobot/cli/commands.py
View File

@@ -7,6 +7,18 @@ import signal
import sys
from pathlib import Path
# Force UTF-8 encoding for Windows console
if sys.platform == "win32":
import locale
if sys.stdout.encoding != "utf-8":
os.environ["PYTHONIOENCODING"] = "utf-8"
# Re-open stdout/stderr with UTF-8 encoding
try:
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
sys.stderr.reconfigure(encoding="utf-8", errors="replace")
except Exception:
pass
import typer
from prompt_toolkit import PromptSession
from prompt_toolkit.formatted_text import HTML
@@ -200,9 +212,8 @@ def onboard():
def _make_provider(config: Config):
"""Create the appropriate LLM provider from config."""
from nanobot.providers.custom_provider import CustomProvider
from nanobot.providers.litellm_provider import LiteLLMProvider
from nanobot.providers.openai_codex_provider import OpenAICodexProvider
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
model = config.agents.defaults.model
provider_name = config.get_provider_name(model)
@@ -213,6 +224,7 @@ def _make_provider(config: Config):
return OpenAICodexProvider(default_model=model)
# Custom: direct OpenAI-compatible endpoint, bypasses LiteLLM
from nanobot.providers.custom_provider import CustomProvider
if provider_name == "custom":
return CustomProvider(
api_key=p.api_key if p else "no-key",
@@ -220,6 +232,21 @@ def _make_provider(config: Config):
default_model=model,
)
# Azure OpenAI: direct Azure OpenAI endpoint with deployment name
if 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(
api_key=p.api_key,
api_base=p.api_base,
default_model=model,
)
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):
@@ -244,13 +271,15 @@ def _make_provider(config: Config):
@app.command()
def gateway(
port: int = typer.Option(18790, "--port", "-p", help="Gateway port"),
workspace: str | None = typer.Option(None, "--workspace", "-w", help="Workspace directory"),
config: str | None = typer.Option(None, "--config", "-c", help="Config file path"),
verbose: bool = typer.Option(False, "--verbose", "-v", help="Verbose output"),
):
"""Start the nanobot gateway."""
from nanobot.agent.loop import AgentLoop
from nanobot.bus.queue import MessageBus
from nanobot.channels.manager import ChannelManager
from nanobot.config.loader import get_data_dir, load_config
from nanobot.config.loader import load_config
from nanobot.cron.service import CronService
from nanobot.cron.types import CronJob
from nanobot.heartbeat.service import HeartbeatService
@@ -260,16 +289,20 @@ def gateway(
import logging
logging.basicConfig(level=logging.DEBUG)
console.print(f"{__logo__} Starting nanobot gateway on port {port}...")
config_path = Path(config) if config else None
config = load_config(config_path)
if workspace:
config.agents.defaults.workspace = workspace
config = load_config()
console.print(f"{__logo__} Starting nanobot gateway on port {port}...")
sync_workspace_templates(config.workspace_path)
bus = MessageBus()
provider = _make_provider(config)
session_manager = SessionManager(config.workspace_path)
# Create cron service first (callback set after agent creation)
cron_store_path = get_data_dir() / "cron" / "jobs.json"
# Use workspace path for per-instance cron store
cron_store_path = config.workspace_path / "cron" / "jobs.json"
cron = CronService(cron_store_path)
# Create agent with cron service
@@ -511,12 +544,21 @@ def agent(
else:
cli_channel, cli_chat_id = "cli", session_id
def _exit_on_sigint(signum, frame):
def _handle_signal(signum, frame):
sig_name = signal.Signals(signum).name
_restore_terminal()
console.print("\nGoodbye!")
os._exit(0)
console.print(f"\nReceived {sig_name}, goodbye!")
sys.exit(0)
signal.signal(signal.SIGINT, _exit_on_sigint)
signal.signal(signal.SIGINT, _handle_signal)
signal.signal(signal.SIGTERM, _handle_signal)
# SIGHUP is not available on Windows
if hasattr(signal, 'SIGHUP'):
signal.signal(signal.SIGHUP, _handle_signal)
# Ignore SIGPIPE to prevent silent process termination when writing to closed pipes
# SIGPIPE is not available on Windows
if hasattr(signal, 'SIGPIPE'):
signal.signal(signal.SIGPIPE, signal.SIG_IGN)
async def run_interactive():
bus_task = asyncio.create_task(agent_loop.run())

69
nanobot/config/schema.py
View File

@@ -29,7 +29,9 @@ class TelegramConfig(Base):
enabled: bool = False
token: str = "" # Bot token from @BotFather
allow_from: list[str] = Field(default_factory=list) # Allowed user IDs or usernames
proxy: str | None = None # HTTP/SOCKS5 proxy URL, e.g. "http://127.0.0.1:7890" or "socks5://127.0.0.1:1080"
proxy: str | None = (
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
@@ -42,7 +44,9 @@ class FeishuConfig(Base):
encrypt_key: str = "" # Encrypt Key for event subscription (optional)
verification_token: str = "" # Verification Token for event subscription (optional)
allow_from: list[str] = Field(default_factory=list) # Allowed user open_ids
react_emoji: str = "THUMBSUP" # Emoji type for message reactions (e.g. THUMBSUP, OK, DONE, SMILE)
react_emoji: str = (
"THUMBSUP" # Emoji type for message reactions (e.g. THUMBSUP, OK, DONE, SMILE)
)
class DingTalkConfig(Base):
@@ -62,6 +66,7 @@ class DiscordConfig(Base):
allow_from: list[str] = Field(default_factory=list) # Allowed user IDs
gateway_url: str = "wss://gateway.discord.gg/?v=10&encoding=json"
intents: int = 37377 # GUILDS + GUILD_MESSAGES + DIRECT_MESSAGES + MESSAGE_CONTENT
group_policy: Literal["mention", "open"] = "mention"
class MatrixConfig(Base):
@@ -72,9 +77,13 @@ class MatrixConfig(Base):
access_token: str = ""
user_id: str = "" # @bot:matrix.org
device_id: str = ""
e2ee_enabled: bool = True # Enable Matrix E2EE support (encryption + encrypted room handling).
sync_stop_grace_seconds: int = 2 # Max seconds to wait for sync_forever to stop gracefully before cancellation fallback.
max_media_bytes: int = 20 * 1024 * 1024 # Max attachment size accepted for Matrix media handling (inbound + outbound).
e2ee_enabled: bool = True # Enable Matrix E2EE support (encryption + encrypted room handling).
sync_stop_grace_seconds: int = (
2 # Max seconds to wait for sync_forever to stop gracefully before cancellation fallback.
)
max_media_bytes: int = (
20 * 1024 * 1024
) # Max attachment size accepted for Matrix media handling (inbound + outbound).
allow_from: list[str] = Field(default_factory=list)
group_policy: Literal["open", "mention", "allowlist"] = "open"
group_allow_from: list[str] = Field(default_factory=list)
@@ -105,7 +114,9 @@ class EmailConfig(Base):
from_address: str = ""
# Behavior
auto_reply_enabled: bool = True # If false, inbound email is read but no automatic reply is sent
auto_reply_enabled: bool = (
True # If false, inbound email is read but no automatic reply is sent
)
poll_interval_seconds: int = 30
mark_seen: bool = True
max_body_chars: int = 12000
@@ -183,27 +194,17 @@ class QQConfig(Base):
enabled: bool = False
app_id: str = "" # 机器人 ID (AppID) from q.qq.com
secret: str = "" # 机器人密钥 (AppSecret) from q.qq.com
allow_from: list[str] = Field(default_factory=list) # Allowed user openids (empty = public access)
allow_from: list[str] = Field(
default_factory=list
) # Allowed user openids (empty = public access)
class MatrixConfig(Base):
"""Matrix (Element) channel configuration."""
enabled: bool = False
homeserver: str = "https://matrix.org"
access_token: str = ""
user_id: str = "" # e.g. @bot:matrix.org
device_id: str = ""
e2ee_enabled: bool = True # end-to-end encryption support
sync_stop_grace_seconds: int = 2 # graceful sync_forever shutdown timeout
max_media_bytes: int = 20 * 1024 * 1024 # inbound + outbound attachment limit
allow_from: list[str] = Field(default_factory=list)
group_policy: Literal["open", "mention", "allowlist"] = "open"
group_allow_from: list[str] = Field(default_factory=list)
allow_room_mentions: bool = False
class ChannelsConfig(Base):
"""Configuration for chat channels."""
send_progress: bool = True # stream agent's text progress to the channel
send_progress: bool = True # stream agent's text progress to the channel
send_tool_hints: bool = False # stream tool-call hints (e.g. read_file("…"))
whatsapp: WhatsAppConfig = Field(default_factory=WhatsAppConfig)
telegram: TelegramConfig = Field(default_factory=TelegramConfig)
@@ -222,7 +223,9 @@ class AgentDefaults(Base):
workspace: str = "~/.nanobot/workspace"
model: str = "anthropic/claude-opus-4-5"
provider: str = "auto" # Provider name (e.g. "anthropic", "openrouter") or "auto" for auto-detection
provider: str = (
"auto" # Provider name (e.g. "anthropic", "openrouter") or "auto" for auto-detection
)
max_tokens: int = 8192
temperature: float = 0.1
max_tool_iterations: int = 40
@@ -248,6 +251,7 @@ class ProvidersConfig(Base):
"""Configuration for LLM providers."""
custom: ProviderConfig = Field(default_factory=ProviderConfig) # Any OpenAI-compatible endpoint
azure_openai: ProviderConfig = Field(default_factory=ProviderConfig) # Azure OpenAI (model = deployment name)
anthropic: ProviderConfig = Field(default_factory=ProviderConfig)
openai: ProviderConfig = Field(default_factory=ProviderConfig)
openrouter: ProviderConfig = Field(default_factory=ProviderConfig)
@@ -260,8 +264,8 @@ class ProvidersConfig(Base):
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) API gateway
volcengine: ProviderConfig = Field(default_factory=ProviderConfig) # VolcEngine (火山引擎) API gateway
siliconflow: ProviderConfig = Field(default_factory=ProviderConfig) # SiliconFlow (硅基流动)
volcengine: ProviderConfig = Field(default_factory=ProviderConfig) # VolcEngine (火山引擎)
openai_codex: ProviderConfig = Field(default_factory=ProviderConfig) # OpenAI Codex (OAuth)
github_copilot: ProviderConfig = Field(default_factory=ProviderConfig) # Github Copilot (OAuth)
@@ -291,7 +295,9 @@ class WebSearchConfig(Base):
class WebToolsConfig(Base):
"""Web tools configuration."""
proxy: str | None = None # HTTP/SOCKS5 proxy URL, e.g. "http://127.0.0.1:7890" or "socks5://127.0.0.1:1080"
proxy: str | None = (
None # HTTP/SOCKS5 proxy URL, e.g. "http://127.0.0.1:7890" or "socks5://127.0.0.1:1080"
)
search: WebSearchConfig = Field(default_factory=WebSearchConfig)
@@ -305,12 +311,13 @@ class ExecToolConfig(Base):
class MCPServerConfig(Base):
"""MCP server connection configuration (stdio or HTTP)."""
type: Literal["stdio", "sse", "streamableHttp"] | None = None # auto-detected if omitted
command: str = "" # Stdio: command to run (e.g. "npx")
args: list[str] = Field(default_factory=list) # Stdio: command arguments
env: dict[str, str] = Field(default_factory=dict) # Stdio: extra env vars
url: str = "" # HTTP: streamable HTTP endpoint URL
headers: dict[str, str] = Field(default_factory=dict) # HTTP: Custom HTTP Headers
tool_timeout: int = 30 # Seconds before a tool call is cancelled
url: str = "" # HTTP/SSE: endpoint URL
headers: dict[str, str] = Field(default_factory=dict) # HTTP/SSE: custom headers
tool_timeout: int = 30 # seconds before a tool call is cancelled
class ToolsConfig(Base):
@@ -336,7 +343,9 @@ class Config(BaseSettings):
"""Get expanded workspace path."""
return Path(self.agents.defaults.workspace).expanduser()
def _match_provider(self, model: str | None = None) -> tuple["ProviderConfig | None", str | None]:
def _match_provider(
self, model: str | None = None
) -> tuple["ProviderConfig | None", str | None]:
"""Match provider config and its registry name. Returns (config, spec_name)."""
from nanobot.providers.registry import PROVIDERS

3
nanobot/providers/__init__.py
View File

@@ -3,5 +3,6 @@
from nanobot.providers.base import LLMProvider, LLMResponse
from nanobot.providers.litellm_provider import LiteLLMProvider
from nanobot.providers.openai_codex_provider import OpenAICodexProvider
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
__all__ = ["LLMProvider", "LLMResponse", "LiteLLMProvider", "OpenAICodexProvider"]
__all__ = ["LLMProvider", "LLMResponse", "LiteLLMProvider", "OpenAICodexProvider", "AzureOpenAIProvider"]

210
nanobot/providers/azure_openai_provider.py Normal file
View File

@@ -0,0 +1,210 @@
"""Azure OpenAI provider implementation with API version 2024-10-21."""
from __future__ import annotations
import uuid
from typing import Any
from urllib.parse import urljoin
import httpx
import json_repair
from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
_AZURE_MSG_KEYS = frozenset({"role", "content", "tool_calls", "tool_call_id", "name"})
class AzureOpenAIProvider(LLMProvider):
"""
Azure OpenAI provider with API version 2024-10-21 compliance.
Features:
- Hardcoded API version 2024-10-21
- Uses model field as Azure deployment name in URL path
- Uses api-key header instead of Authorization Bearer
- Uses max_completion_tokens instead of max_tokens
- Direct HTTP calls, bypasses LiteLLM
"""
def __init__(
self,
api_key: str = "",
api_base: str = "",
default_model: str = "gpt-5.2-chat",
):
super().__init__(api_key, api_base)
self.default_model = default_model
self.api_version = "2024-10-21"
# Validate required parameters
if not api_key:
raise ValueError("Azure OpenAI api_key is required")
if not api_base:
raise ValueError("Azure OpenAI api_base is required")
# Ensure api_base ends with /
if not api_base.endswith('/'):
api_base += '/'
self.api_base = api_base
def _build_chat_url(self, deployment_name: str) -> str:
"""Build the Azure OpenAI chat completions URL."""
# Azure OpenAI URL format:
# https://{resource}.openai.azure.com/openai/deployments/{deployment}/chat/completions?api-version={version}
base_url = self.api_base
if not base_url.endswith('/'):
base_url += '/'
url = urljoin(
base_url,
f"openai/deployments/{deployment_name}/chat/completions"
)
return f"{url}?api-version={self.api_version}"
def _build_headers(self) -> dict[str, str]:
"""Build headers for Azure OpenAI API with api-key header."""
return {
"Content-Type": "application/json",
"api-key": self.api_key, # Azure OpenAI uses api-key header, not Authorization
"x-session-affinity": uuid.uuid4().hex, # For cache locality
}
@staticmethod
def _supports_temperature(
deployment_name: str,
reasoning_effort: str | None = None,
) -> bool:
"""Return True when temperature is likely supported for this deployment."""
if reasoning_effort:
return False
name = deployment_name.lower()
return not any(token in name for token in ("gpt-5", "o1", "o3", "o4"))
def _prepare_request_payload(
self,
deployment_name: str,
messages: list[dict[str, Any]],
tools: list[dict[str, Any]] | None = None,
max_tokens: int = 4096,
temperature: float = 0.7,
reasoning_effort: str | None = None,
) -> dict[str, Any]:
"""Prepare the request payload with Azure OpenAI 2024-10-21 compliance."""
payload: dict[str, Any] = {
"messages": self._sanitize_request_messages(
self._sanitize_empty_content(messages),
_AZURE_MSG_KEYS,
),
"max_completion_tokens": max(1, max_tokens), # Azure API 2024-10-21 uses max_completion_tokens
}
if self._supports_temperature(deployment_name, reasoning_effort):
payload["temperature"] = temperature
if reasoning_effort:
payload["reasoning_effort"] = reasoning_effort
if tools:
payload["tools"] = tools
payload["tool_choice"] = "auto"
return payload
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:
"""
Send a chat completion request to Azure OpenAI.
Args:
messages: List of message dicts with 'role' and 'content'.
tools: Optional list of tool definitions in OpenAI format.
model: Model identifier (used as deployment name).
max_tokens: Maximum tokens in response (mapped to max_completion_tokens).
temperature: Sampling temperature.
reasoning_effort: Optional reasoning effort parameter.
Returns:
LLMResponse with content and/or tool calls.
"""
deployment_name = model or self.default_model
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
)
try:
async with httpx.AsyncClient(timeout=60.0, verify=True) as client:
response = await client.post(url, headers=headers, json=payload)
if response.status_code != 200:
return LLMResponse(
content=f"Azure OpenAI API Error {response.status_code}: {response.text}",
finish_reason="error",
)
response_data = response.json()
return self._parse_response(response_data)
except Exception as e:
return LLMResponse(
content=f"Error calling Azure OpenAI: {repr(e)}",
finish_reason="error",
)
def _parse_response(self, response: dict[str, Any]) -> LLMResponse:
"""Parse Azure OpenAI response into our standard format."""
try:
choice = response["choices"][0]
message = choice["message"]
tool_calls = []
if message.get("tool_calls"):
for tc in message["tool_calls"]:
# Parse arguments from JSON string if needed
args = tc["function"]["arguments"]
if isinstance(args, str):
args = json_repair.loads(args)
tool_calls.append(
ToolCallRequest(
id=tc["id"],
name=tc["function"]["name"],
arguments=args,
)
)
usage = {}
if response.get("usage"):
usage_data = response["usage"]
usage = {
"prompt_tokens": usage_data.get("prompt_tokens", 0),
"completion_tokens": usage_data.get("completion_tokens", 0),
"total_tokens": usage_data.get("total_tokens", 0),
}
reasoning_content = message.get("reasoning_content") or None
return LLMResponse(
content=message.get("content"),
tool_calls=tool_calls,
finish_reason=choice.get("finish_reason", "stop"),
usage=usage,
reasoning_content=reasoning_content,
)
except (KeyError, IndexError) as e:
return LLMResponse(
content=f"Error parsing Azure OpenAI response: {str(e)}",
finish_reason="error",
)
def get_default_model(self) -> str:
"""Get the default model (also used as default deployment name)."""
return self.default_model

14
nanobot/providers/base.py
View File

@@ -87,6 +87,20 @@ class LLMProvider(ABC):
result.append(msg)
return result
@staticmethod
def _sanitize_request_messages(
messages: list[dict[str, Any]],
allowed_keys: frozenset[str],
) -> list[dict[str, Any]]:
"""Keep only provider-safe message keys and normalize assistant content."""
sanitized = []
for msg in messages:
clean = {k: v for k, v in msg.items() if k in allowed_keys}
if clean.get("role") == "assistant" and "content" not in clean:
clean["content"] = None
sanitized.append(clean)
return sanitized
@abstractmethod
async def chat(
self,

8
nanobot/providers/custom_provider.py
View File

@@ -2,6 +2,7 @@
from __future__ import annotations
import uuid
from typing import Any
import json_repair
@@ -15,7 +16,12 @@ class CustomProvider(LLMProvider):
def __init__(self, api_key: str = "no-key", api_base: str = "http://localhost:8000/v1", default_model: str = "default"):
super().__init__(api_key, api_base)
self.default_model = default_model
self._client = AsyncOpenAI(api_key=api_key, base_url=api_base)
# Keep affinity stable for this provider instance to improve backend cache locality.
self._client = AsyncOpenAI(
api_key=api_key,
base_url=api_base,
default_headers={"x-session-affinity": uuid.uuid4().hex},
)
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,

87
nanobot/providers/litellm_provider.py
View File

@@ -1,5 +1,6 @@
"""LiteLLM provider implementation for multi-provider support."""
import hashlib
import os
import secrets
import string
@@ -8,6 +9,7 @@ from typing import Any
import json_repair
import litellm
from litellm import acompletion
from loguru import logger
from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
from nanobot.providers.registry import find_by_model, find_gateway
@@ -165,17 +167,43 @@ class LiteLLMProvider(LLMProvider):
return _ANTHROPIC_EXTRA_KEYS
return frozenset()
@staticmethod
def _normalize_tool_call_id(tool_call_id: Any) -> Any:
"""Normalize tool_call_id to a provider-safe 9-char alphanumeric form."""
if not isinstance(tool_call_id, str):
return tool_call_id
if len(tool_call_id) == 9 and tool_call_id.isalnum():
return tool_call_id
return hashlib.sha1(tool_call_id.encode()).hexdigest()[:9]
@staticmethod
def _sanitize_messages(messages: list[dict[str, Any]], extra_keys: frozenset[str] = frozenset()) -> list[dict[str, Any]]:
"""Strip non-standard keys and ensure assistant messages have a content key."""
allowed = _ALLOWED_MSG_KEYS | extra_keys
sanitized = []
for msg in messages:
clean = {k: v for k, v in msg.items() if k in allowed}
# Strict providers require "content" even when assistant only has tool_calls
if clean.get("role") == "assistant" and "content" not in clean:
clean["content"] = None
sanitized.append(clean)
sanitized = LLMProvider._sanitize_request_messages(messages, allowed)
id_map: dict[str, str] = {}
def map_id(value: Any) -> Any:
if not isinstance(value, str):
return value
return id_map.setdefault(value, LiteLLMProvider._normalize_tool_call_id(value))
for clean in sanitized:
# Keep assistant tool_calls[].id and tool tool_call_id in sync after
# shortening, otherwise strict providers reject the broken linkage.
if isinstance(clean.get("tool_calls"), list):
normalized_tool_calls = []
for tc in clean["tool_calls"]:
if not isinstance(tc, dict):
normalized_tool_calls.append(tc)
continue
tc_clean = dict(tc)
tc_clean["id"] = map_id(tc_clean.get("id"))
normalized_tool_calls.append(tc_clean)
clean["tool_calls"] = normalized_tool_calls
if "tool_call_id" in clean and clean["tool_call_id"]:
clean["tool_call_id"] = map_id(clean["tool_call_id"])
return sanitized
async def chat(
@@ -255,20 +283,37 @@ class LiteLLMProvider(LLMProvider):
"""Parse LiteLLM response into our standard format."""
choice = response.choices[0]
message = choice.message
content = message.content
finish_reason = choice.finish_reason
# Some providers (e.g. GitHub Copilot) split content and tool_calls
# across multiple choices. Merge them so tool_calls are not lost.
raw_tool_calls = []
for ch in response.choices:
msg = ch.message
if hasattr(msg, "tool_calls") and msg.tool_calls:
raw_tool_calls.extend(msg.tool_calls)
if ch.finish_reason in ("tool_calls", "stop"):
finish_reason = ch.finish_reason
if not content and msg.content:
content = msg.content
if len(response.choices) > 1:
logger.debug("LiteLLM response has {} choices, merged {} tool_calls",
len(response.choices), len(raw_tool_calls))
tool_calls = []
if hasattr(message, "tool_calls") and message.tool_calls:
for tc in message.tool_calls:
# Parse arguments from JSON string if needed
args = tc.function.arguments
if isinstance(args, str):
args = json_repair.loads(args)
for tc in raw_tool_calls:
# Parse arguments from JSON string if needed
args = tc.function.arguments
if isinstance(args, str):
args = json_repair.loads(args)
tool_calls.append(ToolCallRequest(
id=_short_tool_id(),
name=tc.function.name,
arguments=args,
))
tool_calls.append(ToolCallRequest(
id=_short_tool_id(),
name=tc.function.name,
arguments=args,
))
usage = {}
if hasattr(response, "usage") and response.usage:
@@ -280,11 +325,11 @@ class LiteLLMProvider(LLMProvider):
reasoning_content = getattr(message, "reasoning_content", None) or None
thinking_blocks = getattr(message, "thinking_blocks", None) or None
return LLMResponse(
content=message.content,
content=content,
tool_calls=tool_calls,
finish_reason=choice.finish_reason or "stop",
finish_reason=finish_reason or "stop",
usage=usage,
reasoning_content=reasoning_content,
thinking_blocks=thinking_blocks,

3
nanobot/providers/openai_codex_provider.py
View File

@@ -52,6 +52,9 @@ class OpenAICodexProvider(LLMProvider):
"parallel_tool_calls": True,
}
if reasoning_effort:
body["reasoning"] = {"effort": reasoning_effort}
if tools:
body["tools"] = _convert_tools(tools)

118
nanobot/providers/registry.py
View File

@@ -26,33 +26,33 @@ class ProviderSpec:
"""
# identity
name: str # config field name, e.g. "dashscope"
keywords: tuple[str, ...] # model-name keywords for matching (lowercase)
env_key: str # LiteLLM env var, e.g. "DASHSCOPE_API_KEY"
display_name: str = "" # shown in `nanobot status`
name: str # config field name, e.g. "dashscope"
keywords: tuple[str, ...] # model-name keywords for matching (lowercase)
env_key: str # LiteLLM env var, e.g. "DASHSCOPE_API_KEY"
display_name: str = "" # shown in `nanobot status`
# model prefixing
litellm_prefix: str = "" # "dashscope" → model becomes "dashscope/{model}"
skip_prefixes: tuple[str, ...] = () # don't prefix if model already starts with these
litellm_prefix: str = "" # "dashscope" → model becomes "dashscope/{model}"
skip_prefixes: tuple[str, ...] = () # don't prefix if model already starts with these
# extra env vars, e.g. (("ZHIPUAI_API_KEY", "{api_key}"),)
env_extras: tuple[tuple[str, str], ...] = ()
# gateway / local detection
is_gateway: bool = False # routes any model (OpenRouter, AiHubMix)
is_local: bool = False # local deployment (vLLM, Ollama)
detect_by_key_prefix: str = "" # match api_key prefix, e.g. "sk-or-"
detect_by_base_keyword: str = "" # match substring in api_base URL
default_api_base: str = "" # fallback base URL
is_gateway: bool = False # routes any model (OpenRouter, AiHubMix)
is_local: bool = False # local deployment (vLLM, Ollama)
detect_by_key_prefix: str = "" # match api_key prefix, e.g. "sk-or-"
detect_by_base_keyword: str = "" # match substring in api_base URL
default_api_base: str = "" # fallback base URL
# gateway behavior
strip_model_prefix: bool = False # strip "provider/" before re-prefixing
strip_model_prefix: bool = False # strip "provider/" before re-prefixing
# per-model param overrides, e.g. (("kimi-k2.5", {"temperature": 1.0}),)
model_overrides: tuple[tuple[str, dict[str, Any]], ...] = ()
# OAuth-based providers (e.g., OpenAI Codex) don't use API keys
is_oauth: bool = False # if True, uses OAuth flow instead of API key
is_oauth: bool = False # if True, uses OAuth flow instead of API key
# Direct providers bypass LiteLLM entirely (e.g., CustomProvider)
is_direct: bool = False
@@ -70,7 +70,6 @@ class ProviderSpec:
# ---------------------------------------------------------------------------
PROVIDERS: tuple[ProviderSpec, ...] = (
# === Custom (direct OpenAI-compatible endpoint, bypasses LiteLLM) ======
ProviderSpec(
name="custom",
@@ -81,16 +80,24 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
is_direct=True,
),
# === Azure OpenAI (direct API calls with API version 2024-10-21) =====
ProviderSpec(
name="azure_openai",
keywords=("azure", "azure-openai"),
env_key="",
display_name="Azure OpenAI",
litellm_prefix="",
is_direct=True,
),
# === Gateways (detected by api_key / api_base, not model name) =========
# Gateways can route any model, so they win in fallback.
# OpenRouter: global gateway, keys start with "sk-or-"
ProviderSpec(
name="openrouter",
keywords=("openrouter",),
env_key="OPENROUTER_API_KEY",
display_name="OpenRouter",
litellm_prefix="openrouter", # claude-3 → openrouter/claude-3
litellm_prefix="openrouter", # claude-3 → openrouter/claude-3
skip_prefixes=(),
env_extras=(),
is_gateway=True,
@@ -102,16 +109,15 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
model_overrides=(),
supports_prompt_caching=True,
),
# AiHubMix: global gateway, OpenAI-compatible interface.
# strip_model_prefix=True: it doesn't understand "anthropic/claude-3",
# so we strip to bare "claude-3" then re-prefix as "openai/claude-3".
ProviderSpec(
name="aihubmix",
keywords=("aihubmix",),
env_key="OPENAI_API_KEY", # OpenAI-compatible
env_key="OPENAI_API_KEY", # OpenAI-compatible
display_name="AiHubMix",
litellm_prefix="openai", # → openai/{model}
litellm_prefix="openai", # → openai/{model}
skip_prefixes=(),
env_extras=(),
is_gateway=True,
@@ -119,10 +125,9 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
detect_by_key_prefix="",
detect_by_base_keyword="aihubmix",
default_api_base="https://aihubmix.com/v1",
strip_model_prefix=True, # anthropic/claude-3 → claude-3 → openai/claude-3
strip_model_prefix=True, # anthropic/claude-3 → claude-3 → openai/claude-3
model_overrides=(),
),
# SiliconFlow (硅基流动): OpenAI-compatible gateway, model names keep org prefix
ProviderSpec(
name="siliconflow",
@@ -140,7 +145,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# VolcEngine (火山引擎): OpenAI-compatible gateway
ProviderSpec(
name="volcengine",
@@ -158,9 +162,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# === Standard providers (matched by model-name keywords) ===============
# Anthropic: LiteLLM recognizes "claude-*" natively, no prefix needed.
ProviderSpec(
name="anthropic",
@@ -179,7 +181,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
model_overrides=(),
supports_prompt_caching=True,
),
# OpenAI: LiteLLM recognizes "gpt-*" natively, no prefix needed.
ProviderSpec(
name="openai",
@@ -197,14 +198,13 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# OpenAI Codex: uses OAuth, not API key.
ProviderSpec(
name="openai_codex",
keywords=("openai-codex",),
env_key="", # OAuth-based, no API key
env_key="", # OAuth-based, no API key
display_name="OpenAI Codex",
litellm_prefix="", # Not routed through LiteLLM
litellm_prefix="", # Not routed through LiteLLM
skip_prefixes=(),
env_extras=(),
is_gateway=False,
@@ -214,16 +214,15 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
default_api_base="https://chatgpt.com/backend-api",
strip_model_prefix=False,
model_overrides=(),
is_oauth=True, # OAuth-based authentication
is_oauth=True, # OAuth-based authentication
),
# Github Copilot: uses OAuth, not API key.
ProviderSpec(
name="github_copilot",
keywords=("github_copilot", "copilot"),
env_key="", # OAuth-based, no API key
env_key="", # OAuth-based, no API key
display_name="Github Copilot",
litellm_prefix="github_copilot", # github_copilot/model → github_copilot/model
litellm_prefix="github_copilot", # github_copilot/model → github_copilot/model
skip_prefixes=("github_copilot/",),
env_extras=(),
is_gateway=False,
@@ -233,17 +232,16 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
default_api_base="",
strip_model_prefix=False,
model_overrides=(),
is_oauth=True, # OAuth-based authentication
is_oauth=True, # OAuth-based authentication
),
# DeepSeek: needs "deepseek/" prefix for LiteLLM routing.
ProviderSpec(
name="deepseek",
keywords=("deepseek",),
env_key="DEEPSEEK_API_KEY",
display_name="DeepSeek",
litellm_prefix="deepseek", # deepseek-chat → deepseek/deepseek-chat
skip_prefixes=("deepseek/",), # avoid double-prefix
litellm_prefix="deepseek", # deepseek-chat → deepseek/deepseek-chat
skip_prefixes=("deepseek/",), # avoid double-prefix
env_extras=(),
is_gateway=False,
is_local=False,
@@ -253,15 +251,14 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# Gemini: needs "gemini/" prefix for LiteLLM.
ProviderSpec(
name="gemini",
keywords=("gemini",),
env_key="GEMINI_API_KEY",
display_name="Gemini",
litellm_prefix="gemini", # gemini-pro → gemini/gemini-pro
skip_prefixes=("gemini/",), # avoid double-prefix
litellm_prefix="gemini", # gemini-pro → gemini/gemini-pro
skip_prefixes=("gemini/",), # avoid double-prefix
env_extras=(),
is_gateway=False,
is_local=False,
@@ -271,7 +268,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# Zhipu: LiteLLM uses "zai/" prefix.
# Also mirrors key to ZHIPUAI_API_KEY (some LiteLLM paths check that).
# skip_prefixes: don't add "zai/" when already routed via gateway.
@@ -280,11 +276,9 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
keywords=("zhipu", "glm", "zai"),
env_key="ZAI_API_KEY",
display_name="Zhipu AI",
litellm_prefix="zai", # glm-4 → zai/glm-4
litellm_prefix="zai", # glm-4 → zai/glm-4
skip_prefixes=("zhipu/", "zai/", "openrouter/", "hosted_vllm/"),
env_extras=(
("ZHIPUAI_API_KEY", "{api_key}"),
),
env_extras=(("ZHIPUAI_API_KEY", "{api_key}"),),
is_gateway=False,
is_local=False,
detect_by_key_prefix="",
@@ -293,14 +287,13 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# DashScope: Qwen models, needs "dashscope/" prefix.
ProviderSpec(
name="dashscope",
keywords=("qwen", "dashscope"),
env_key="DASHSCOPE_API_KEY",
display_name="DashScope",
litellm_prefix="dashscope", # qwen-max → dashscope/qwen-max
litellm_prefix="dashscope", # qwen-max → dashscope/qwen-max
skip_prefixes=("dashscope/", "openrouter/"),
env_extras=(),
is_gateway=False,
@@ -311,7 +304,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# Moonshot: Kimi models, needs "moonshot/" prefix.
# LiteLLM requires MOONSHOT_API_BASE env var to find the endpoint.
# Kimi K2.5 API enforces temperature >= 1.0.
@@ -320,22 +312,17 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
keywords=("moonshot", "kimi"),
env_key="MOONSHOT_API_KEY",
display_name="Moonshot",
litellm_prefix="moonshot", # kimi-k2.5 → moonshot/kimi-k2.5
litellm_prefix="moonshot", # kimi-k2.5 → moonshot/kimi-k2.5
skip_prefixes=("moonshot/", "openrouter/"),
env_extras=(
("MOONSHOT_API_BASE", "{api_base}"),
),
env_extras=(("MOONSHOT_API_BASE", "{api_base}"),),
is_gateway=False,
is_local=False,
detect_by_key_prefix="",
detect_by_base_keyword="",
default_api_base="https://api.moonshot.ai/v1", # intl; use api.moonshot.cn for China
default_api_base="https://api.moonshot.ai/v1", # intl; use api.moonshot.cn for China
strip_model_prefix=False,
model_overrides=(
("kimi-k2.5", {"temperature": 1.0}),
),
model_overrides=(("kimi-k2.5", {"temperature": 1.0}),),
),
# MiniMax: needs "minimax/" prefix for LiteLLM routing.
# Uses OpenAI-compatible API at api.minimax.io/v1.
ProviderSpec(
@@ -343,7 +330,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
keywords=("minimax",),
env_key="MINIMAX_API_KEY",
display_name="MiniMax",
litellm_prefix="minimax", # MiniMax-M2.1 → minimax/MiniMax-M2.1
litellm_prefix="minimax", # MiniMax-M2.1 → minimax/MiniMax-M2.1
skip_prefixes=("minimax/", "openrouter/"),
env_extras=(),
is_gateway=False,
@@ -354,9 +341,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
strip_model_prefix=False,
model_overrides=(),
),
# === Local deployment (matched by config key, NOT by api_base) =========
# vLLM / any OpenAI-compatible local server.
# Detected when config key is "vllm" (provider_name="vllm").
ProviderSpec(
@@ -364,20 +349,18 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
keywords=("vllm",),
env_key="HOSTED_VLLM_API_KEY",
display_name="vLLM/Local",
litellm_prefix="hosted_vllm", # Llama-3-8B → hosted_vllm/Llama-3-8B
litellm_prefix="hosted_vllm", # Llama-3-8B → hosted_vllm/Llama-3-8B
skip_prefixes=(),
env_extras=(),
is_gateway=False,
is_local=True,
detect_by_key_prefix="",
detect_by_base_keyword="",
default_api_base="", # user must provide in config
default_api_base="", # user must provide in config
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.
ProviderSpec(
@@ -385,8 +368,8 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
keywords=("groq",),
env_key="GROQ_API_KEY",
display_name="Groq",
litellm_prefix="groq", # llama3-8b-8192 → groq/llama3-8b-8192
skip_prefixes=("groq/",), # avoid double-prefix
litellm_prefix="groq", # llama3-8b-8192 → groq/llama3-8b-8192
skip_prefixes=("groq/",), # avoid double-prefix
env_extras=(),
is_gateway=False,
is_local=False,
@@ -403,6 +386,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
# Lookup helpers
# ---------------------------------------------------------------------------
def find_by_model(model: str) -> ProviderSpec | None:
"""Match a standard provider by model-name keyword (case-insensitive).
Skips gateways/local — those are matched by api_key/api_base instead."""
@@ -418,7 +402,9 @@ def find_by_model(model: str) -> ProviderSpec | None:
return spec
for spec in std_specs:
if any(kw in model_lower or kw.replace("-", "_") in model_normalized for kw in spec.keywords):
if any(
kw in model_lower or kw.replace("-", "_") in model_normalized for kw in spec.keywords
):
return spec
return None

45
nanobot/utils/helpers.py
View File

@@ -5,6 +5,19 @@ from datetime import datetime
from pathlib import Path
def detect_image_mime(data: bytes) -> str | None:
"""Detect image MIME type from magic bytes, ignoring file extension."""
if data[:8] == b"\x89PNG\r\n\x1a\n":
return "image/png"
if data[:3] == b"\xff\xd8\xff":
return "image/jpeg"
if data[:6] in (b"GIF87a", b"GIF89a"):
return "image/gif"
if data[:4] == b"RIFF" and data[8:12] == b"WEBP":
return "image/webp"
return None
def ensure_dir(path: Path) -> Path:
"""Ensure directory exists, return it."""
path.mkdir(parents=True, exist_ok=True)
@@ -34,6 +47,38 @@ def safe_filename(name: str) -> str:
return _UNSAFE_CHARS.sub("_", name).strip()
def split_message(content: str, max_len: int = 2000) -> list[str]:
"""
Split content into chunks within max_len, preferring line breaks.
Args:
content: The text content to split.
max_len: Maximum length per chunk (default 2000 for Discord compatibility).
Returns:
List of message chunks, each within max_len.
"""
if not content:
return []
if len(content) <= max_len:
return [content]
chunks: list[str] = []
while content:
if len(content) <= max_len:
chunks.append(content)
break
cut = content[:max_len]
# Try to break at newline first, then space, then hard break
pos = cut.rfind('\n')
if pos <= 0:
pos = cut.rfind(' ')
if pos <= 0:
pos = max_len
chunks.append(content[:pos])
content = content[pos:].lstrip()
return chunks
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

6
pyproject.toml
View File

@@ -30,7 +30,7 @@ dependencies = [
"rich>=14.0.0,<15.0.0",
"croniter>=6.0.0,<7.0.0",
"dingtalk-stream>=0.24.0,<1.0.0",
"python-telegram-bot[socks]>=22.0,<23.0",
"python-telegram-bot[socks]>=22.6,<23.0",
"lark-oapi>=1.5.0,<2.0.0",
"socksio>=1.0.0,<2.0.0",
"python-socketio>=5.16.0,<6.0.0",
@@ -42,6 +42,7 @@ dependencies = [
"prompt-toolkit>=3.0.50,<4.0.0",
"mcp>=1.26.0,<2.0.0",
"json-repair>=0.57.0,<1.0.0",
"chardet>=3.0.2,<6.0.0",
"openai>=2.8.0",
]
@@ -55,6 +56,9 @@ dev = [
"pytest>=9.0.0,<10.0.0",
"pytest-asyncio>=1.3.0,<2.0.0",
"ruff>=0.1.0",
"matrix-nio[e2e]>=0.25.2",
"mistune>=3.0.0,<4.0.0",
"nh3>=0.2.17,<1.0.0",
]
[project.scripts]

399
tests/test_azure_openai_provider.py Normal file
View File

@@ -0,0 +1,399 @@
"""Test Azure OpenAI provider implementation (updated for model-based deployment names)."""
from unittest.mock import AsyncMock, Mock, patch
import pytest
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
from nanobot.providers.base import LLMResponse
def test_azure_openai_provider_init():
"""Test AzureOpenAIProvider initialization without deployment_name."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o-deployment",
)
assert provider.api_key == "test-key"
assert provider.api_base == "https://test-resource.openai.azure.com/"
assert provider.default_model == "gpt-4o-deployment"
assert provider.api_version == "2024-10-21"
def test_azure_openai_provider_init_validation():
"""Test AzureOpenAIProvider initialization validation."""
# Missing api_key
with pytest.raises(ValueError, match="Azure OpenAI api_key is required"):
AzureOpenAIProvider(api_key="", api_base="https://test.com")
# Missing api_base
with pytest.raises(ValueError, match="Azure OpenAI api_base is required"):
AzureOpenAIProvider(api_key="test", api_base="")
def test_build_chat_url():
"""Test Azure OpenAI URL building with different deployment names."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
# Test various deployment names
test_cases = [
("gpt-4o-deployment", "https://test-resource.openai.azure.com/openai/deployments/gpt-4o-deployment/chat/completions?api-version=2024-10-21"),
("gpt-35-turbo", "https://test-resource.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-10-21"),
("custom-model", "https://test-resource.openai.azure.com/openai/deployments/custom-model/chat/completions?api-version=2024-10-21"),
]
for deployment_name, expected_url in test_cases:
url = provider._build_chat_url(deployment_name)
assert url == expected_url
def test_build_chat_url_api_base_without_slash():
"""Test URL building when api_base doesn't end with slash."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com", # No trailing slash
default_model="gpt-4o",
)
url = provider._build_chat_url("test-deployment")
expected = "https://test-resource.openai.azure.com/openai/deployments/test-deployment/chat/completions?api-version=2024-10-21"
assert url == expected
def test_build_headers():
"""Test Azure OpenAI header building with api-key authentication."""
provider = AzureOpenAIProvider(
api_key="test-api-key-123",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
headers = provider._build_headers()
assert headers["Content-Type"] == "application/json"
assert headers["api-key"] == "test-api-key-123" # Azure OpenAI specific header
assert "x-session-affinity" in headers
def test_prepare_request_payload():
"""Test request payload preparation with Azure OpenAI 2024-10-21 compliance."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
messages = [{"role": "user", "content": "Hello"}]
payload = provider._prepare_request_payload("gpt-4o", messages, max_tokens=1500, temperature=0.8)
assert payload["messages"] == messages
assert payload["max_completion_tokens"] == 1500 # Azure API 2024-10-21 uses max_completion_tokens
assert payload["temperature"] == 0.8
assert "tools" not in payload
# Test with tools
tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
payload_with_tools = provider._prepare_request_payload("gpt-4o", messages, tools=tools)
assert payload_with_tools["tools"] == tools
assert payload_with_tools["tool_choice"] == "auto"
# Test with reasoning_effort
payload_with_reasoning = provider._prepare_request_payload(
"gpt-5-chat", messages, reasoning_effort="medium"
)
assert payload_with_reasoning["reasoning_effort"] == "medium"
assert "temperature" not in payload_with_reasoning
def test_prepare_request_payload_sanitizes_messages():
"""Test Azure payload strips non-standard message keys before sending."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
messages = [
{
"role": "assistant",
"tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
"reasoning_content": "hidden chain-of-thought",
},
{
"role": "tool",
"tool_call_id": "call_123",
"name": "x",
"content": "ok",
"extra_field": "should be removed",
},
]
payload = provider._prepare_request_payload("gpt-4o", messages)
assert payload["messages"] == [
{
"role": "assistant",
"content": None,
"tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
},
{
"role": "tool",
"tool_call_id": "call_123",
"name": "x",
"content": "ok",
},
]
@pytest.mark.asyncio
async def test_chat_success():
"""Test successful chat request using model as deployment name."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o-deployment",
)
# Mock response data
mock_response_data = {
"choices": [{
"message": {
"content": "Hello! How can I help you today?",
"role": "assistant"
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 12,
"completion_tokens": 18,
"total_tokens": 30
}
}
with patch("httpx.AsyncClient") as mock_client:
mock_response = AsyncMock()
mock_response.status_code = 200
mock_response.json = Mock(return_value=mock_response_data)
mock_context = AsyncMock()
mock_context.post = AsyncMock(return_value=mock_response)
mock_client.return_value.__aenter__.return_value = mock_context
# Test with specific model (deployment name)
messages = [{"role": "user", "content": "Hello"}]
result = await provider.chat(messages, model="custom-deployment")
assert isinstance(result, LLMResponse)
assert result.content == "Hello! How can I help you today?"
assert result.finish_reason == "stop"
assert result.usage["prompt_tokens"] == 12
assert result.usage["completion_tokens"] == 18
assert result.usage["total_tokens"] == 30
# Verify URL was built with the provided model as deployment name
call_args = mock_context.post.call_args
expected_url = "https://test-resource.openai.azure.com/openai/deployments/custom-deployment/chat/completions?api-version=2024-10-21"
assert call_args[0][0] == expected_url
@pytest.mark.asyncio
async def test_chat_uses_default_model_when_no_model_provided():
"""Test that chat uses default_model when no model is specified."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="default-deployment",
)
mock_response_data = {
"choices": [{
"message": {"content": "Response", "role": "assistant"},
"finish_reason": "stop"
}],
"usage": {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
}
with patch("httpx.AsyncClient") as mock_client:
mock_response = AsyncMock()
mock_response.status_code = 200
mock_response.json = Mock(return_value=mock_response_data)
mock_context = AsyncMock()
mock_context.post = AsyncMock(return_value=mock_response)
mock_client.return_value.__aenter__.return_value = mock_context
messages = [{"role": "user", "content": "Test"}]
await provider.chat(messages) # No model specified
# Verify URL was built with default model as deployment name
call_args = mock_context.post.call_args
expected_url = "https://test-resource.openai.azure.com/openai/deployments/default-deployment/chat/completions?api-version=2024-10-21"
assert call_args[0][0] == expected_url
@pytest.mark.asyncio
async def test_chat_with_tool_calls():
"""Test chat request with tool calls in response."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
# Mock response with tool calls
mock_response_data = {
"choices": [{
"message": {
"content": None,
"role": "assistant",
"tool_calls": [{
"id": "call_12345",
"function": {
"name": "get_weather",
"arguments": '{"location": "San Francisco"}'
}
}]
},
"finish_reason": "tool_calls"
}],
"usage": {
"prompt_tokens": 20,
"completion_tokens": 15,
"total_tokens": 35
}
}
with patch("httpx.AsyncClient") as mock_client:
mock_response = AsyncMock()
mock_response.status_code = 200
mock_response.json = Mock(return_value=mock_response_data)
mock_context = AsyncMock()
mock_context.post = AsyncMock(return_value=mock_response)
mock_client.return_value.__aenter__.return_value = mock_context
messages = [{"role": "user", "content": "What's the weather?"}]
tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
result = await provider.chat(messages, tools=tools, model="weather-model")
assert isinstance(result, LLMResponse)
assert result.content is None
assert result.finish_reason == "tool_calls"
assert len(result.tool_calls) == 1
assert result.tool_calls[0].name == "get_weather"
assert result.tool_calls[0].arguments == {"location": "San Francisco"}
@pytest.mark.asyncio
async def test_chat_api_error():
"""Test chat request API error handling."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
with patch("httpx.AsyncClient") as mock_client:
mock_response = AsyncMock()
mock_response.status_code = 401
mock_response.text = "Invalid authentication credentials"
mock_context = AsyncMock()
mock_context.post = AsyncMock(return_value=mock_response)
mock_client.return_value.__aenter__.return_value = mock_context
messages = [{"role": "user", "content": "Hello"}]
result = await provider.chat(messages)
assert isinstance(result, LLMResponse)
assert "Azure OpenAI API Error 401" in result.content
assert "Invalid authentication credentials" in result.content
assert result.finish_reason == "error"
@pytest.mark.asyncio
async def test_chat_connection_error():
"""Test chat request connection error handling."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
with patch("httpx.AsyncClient") as mock_client:
mock_context = AsyncMock()
mock_context.post = AsyncMock(side_effect=Exception("Connection failed"))
mock_client.return_value.__aenter__.return_value = mock_context
messages = [{"role": "user", "content": "Hello"}]
result = await provider.chat(messages)
assert isinstance(result, LLMResponse)
assert "Error calling Azure OpenAI: Exception('Connection failed')" in result.content
assert result.finish_reason == "error"
def test_parse_response_malformed():
"""Test response parsing with malformed data."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o",
)
# Test with missing choices
malformed_response = {"usage": {"prompt_tokens": 10}}
result = provider._parse_response(malformed_response)
assert isinstance(result, LLMResponse)
assert "Error parsing Azure OpenAI response" in result.content
assert result.finish_reason == "error"
def test_get_default_model():
"""Test get_default_model method."""
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="my-custom-deployment",
)
assert provider.get_default_model() == "my-custom-deployment"
if __name__ == "__main__":
# Run basic tests
print("Running basic Azure OpenAI provider tests...")
# Test initialization
provider = AzureOpenAIProvider(
api_key="test-key",
api_base="https://test-resource.openai.azure.com",
default_model="gpt-4o-deployment",
)
print("✅ Provider initialization successful")
# Test URL building
url = provider._build_chat_url("my-deployment")
expected = "https://test-resource.openai.azure.com/openai/deployments/my-deployment/chat/completions?api-version=2024-10-21"
assert url == expected
print("✅ URL building works correctly")
# Test headers
headers = provider._build_headers()
assert headers["api-key"] == "test-key"
assert headers["Content-Type"] == "application/json"
print("✅ Header building works correctly")
# Test payload preparation
messages = [{"role": "user", "content": "Test"}]
payload = provider._prepare_request_payload("gpt-4o-deployment", messages, max_tokens=1000)
assert payload["max_completion_tokens"] == 1000 # Azure 2024-10-21 format
print("✅ Payload preparation works correctly")
print("✅ All basic tests passed! Updated test file is working correctly.")

19
tests/test_context_prompt_cache.py
View File

@@ -40,7 +40,7 @@ def test_system_prompt_stays_stable_when_clock_changes(tmp_path, monkeypatch) ->
def test_runtime_context_is_separate_untrusted_user_message(tmp_path) -> None:
"""Runtime metadata should be a separate user message before the actual user message."""
"""Runtime metadata should be merged with the user message."""
workspace = _make_workspace(tmp_path)
builder = ContextBuilder(workspace)
@@ -54,13 +54,12 @@ def test_runtime_context_is_separate_untrusted_user_message(tmp_path) -> None:
assert messages[0]["role"] == "system"
assert "## Current Session" not in messages[0]["content"]
assert messages[-2]["role"] == "user"
runtime_content = messages[-2]["content"]
assert isinstance(runtime_content, str)
assert ContextBuilder._RUNTIME_CONTEXT_TAG in runtime_content
assert "Current Time:" in runtime_content
assert "Channel: cli" in runtime_content
assert "Chat ID: direct" in runtime_content
# Runtime context is now merged with user message into a single message
assert messages[-1]["role"] == "user"
assert messages[-1]["content"] == "Return exactly: OK"
user_content = messages[-1]["content"]
assert isinstance(user_content, str)
assert ContextBuilder._RUNTIME_CONTEXT_TAG in user_content
assert "Current Time:" in user_content
assert "Channel: cli" in user_content
assert "Chat ID: direct" in user_content
assert "Return exactly: OK" in user_content

29
tests/test_cron_commands.py
View File

@@ -1,29 +0,0 @@
from typer.testing import CliRunner
from nanobot.cli.commands import app
runner = CliRunner()
def test_cron_add_rejects_invalid_timezone(monkeypatch, tmp_path) -> None:
monkeypatch.setattr("nanobot.config.loader.get_data_dir", lambda: tmp_path)
result = runner.invoke(
app,
[
"cron",
"add",
"--name",
"demo",
"--message",
"hello",
"--cron",
"0 9 * * *",
"--tz",
"America/Vancovuer",
],
)
assert result.exit_code == 1
assert "Error: unknown timezone 'America/Vancovuer'" in result.stdout
assert not (tmp_path / "cron" / "jobs.json").exists()

2
tests/test_cron_service.py
View File

@@ -48,6 +48,8 @@ async def test_running_service_honors_external_disable(tmp_path) -> None:
)
await service.start()
try:
# Wait slightly to ensure file mtime is definitively different
await asyncio.sleep(0.05)
external = CronService(store_path)
updated = external.enable_job(job.id, enabled=False)
assert updated is not None

27
tests/test_feishu_post_content.py
View File

@@ -1,4 +1,4 @@
from nanobot.channels.feishu import _extract_post_content
from nanobot.channels.feishu import FeishuChannel, _extract_post_content
def test_extract_post_content_supports_post_wrapper_shape() -> None:
@@ -38,3 +38,28 @@ def test_extract_post_content_keeps_direct_shape_behavior() -> None:
assert text == "Daily report"
assert image_keys == ["img_a", "img_b"]
def test_register_optional_event_keeps_builder_when_method_missing() -> None:
class Builder:
pass
builder = Builder()
same = FeishuChannel._register_optional_event(builder, "missing", object())
assert same is builder
def test_register_optional_event_calls_supported_method() -> None:
called = []
class Builder:
def register_event(self, handler):
called.append(handler)
return self
builder = Builder()
handler = object()
same = FeishuChannel._register_optional_event(builder, "register_event", handler)
assert same is builder
assert called == [handler]

104
tests/test_feishu_table_split.py Normal file
View File

@@ -0,0 +1,104 @@
"""Tests for FeishuChannel._split_elements_by_table_limit.
Feishu cards reject messages that contain more than one table element
(API error 11310: card table number over limit). The helper splits a flat
list of card elements into groups so that each group contains at most one
table, allowing nanobot to send multiple cards instead of failing.
"""
from nanobot.channels.feishu import FeishuChannel
def _md(text: str) -> dict:
return {"tag": "markdown", "content": text}
def _table() -> dict:
return {
"tag": "table",
"columns": [{"tag": "column", "name": "c0", "display_name": "A", "width": "auto"}],
"rows": [{"c0": "v"}],
"page_size": 2,
}
split = FeishuChannel._split_elements_by_table_limit
def test_empty_list_returns_single_empty_group() -> None:
assert split([]) == [[]]
def test_no_tables_returns_single_group() -> None:
els = [_md("hello"), _md("world")]
result = split(els)
assert result == [els]
def test_single_table_stays_in_one_group() -> None:
els = [_md("intro"), _table(), _md("outro")]
result = split(els)
assert len(result) == 1
assert result[0] == els
def test_two_tables_split_into_two_groups() -> None:
# Use different row values so the two tables are not equal
t1 = {
"tag": "table",
"columns": [{"tag": "column", "name": "c0", "display_name": "A", "width": "auto"}],
"rows": [{"c0": "table-one"}],
"page_size": 2,
}
t2 = {
"tag": "table",
"columns": [{"tag": "column", "name": "c0", "display_name": "B", "width": "auto"}],
"rows": [{"c0": "table-two"}],
"page_size": 2,
}
els = [_md("before"), t1, _md("between"), t2, _md("after")]
result = split(els)
assert len(result) == 2
# First group: text before table-1 + table-1
assert t1 in result[0]
assert t2 not in result[0]
# Second group: text between tables + table-2 + text after
assert t2 in result[1]
assert t1 not in result[1]
def test_three_tables_split_into_three_groups() -> None:
tables = [
{"tag": "table", "columns": [], "rows": [{"c0": f"t{i}"}], "page_size": 1}
for i in range(3)
]
els = tables[:]
result = split(els)
assert len(result) == 3
for i, group in enumerate(result):
assert tables[i] in group
def test_leading_markdown_stays_with_first_table() -> None:
intro = _md("intro")
t = _table()
result = split([intro, t])
assert len(result) == 1
assert result[0] == [intro, t]
def test_trailing_markdown_after_second_table() -> None:
t1, t2 = _table(), _table()
tail = _md("end")
result = split([t1, t2, tail])
assert len(result) == 2
assert result[1] == [t2, tail]
def test_non_table_elements_before_first_table_kept_in_first_group() -> None:
head = _md("head")
t1, t2 = _table(), _table()
result = split([head, t1, t2])
# head + t1 in group 0; t2 in group 1
assert result[0] == [head, t1]
assert result[1] == [t2]

20
tests/test_matrix_channel.py
View File

@@ -159,6 +159,7 @@ class _FakeAsyncClient:
def _make_config(**kwargs) -> MatrixConfig:
kwargs.setdefault("allow_from", ["*"])
return MatrixConfig(
enabled=True,
homeserver="https://matrix.org",
@@ -274,7 +275,7 @@ async def test_stop_stops_sync_forever_before_close(monkeypatch) -> None:
@pytest.mark.asyncio
async def test_room_invite_joins_when_allow_list_is_empty() -> None:
async def test_room_invite_ignores_when_allow_list_is_empty() -> None:
channel = MatrixChannel(_make_config(allow_from=[]), MessageBus())
client = _FakeAsyncClient("", "", "", None)
channel.client = client
@@ -284,9 +285,22 @@ async def test_room_invite_joins_when_allow_list_is_empty() -> None:
await channel._on_room_invite(room, event)
assert client.join_calls == ["!room:matrix.org"]
assert client.join_calls == []
@pytest.mark.asyncio
async def test_room_invite_joins_when_sender_allowed() -> None:
channel = MatrixChannel(_make_config(allow_from=["@alice:matrix.org"]), MessageBus())
client = _FakeAsyncClient("", "", "", None)
channel.client = client
room = SimpleNamespace(room_id="!room:matrix.org")
event = SimpleNamespace(sender="@alice:matrix.org")
await channel._on_room_invite(room, event)
assert client.join_calls == ["!room:matrix.org"]
@pytest.mark.asyncio
async def test_room_invite_respects_allow_list_when_configured() -> None:
channel = MatrixChannel(_make_config(allow_from=["@bob:matrix.org"]), MessageBus())
@@ -1163,6 +1177,8 @@ async def test_send_progress_keeps_typing_keepalive_running() -> None:
assert "!room:matrix.org" in channel._typing_tasks
assert client.typing_calls[-1] == ("!room:matrix.org", True, TYPING_NOTICE_TIMEOUT_MS)
await channel.stop()
@pytest.mark.asyncio
async def test_send_clears_typing_when_send_fails() -> None:

75
tests/test_memory_consolidation_types.py
View File

@@ -145,3 +145,78 @@ class TestMemoryConsolidationTypeHandling:
assert result is True
provider.chat.assert_not_called()
@pytest.mark.asyncio
async def test_list_arguments_extracts_first_dict(self, tmp_path: Path) -> None:
"""Some providers return arguments as a list - extract first element if it's a dict."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
# Simulate arguments being a list containing a dict
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=[{
"history_entry": "[2026-01-01] User discussed testing.",
"memory_update": "# Memory\nUser likes testing.",
}],
)
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
assert result is True
assert "User discussed testing." in store.history_file.read_text()
assert "User likes testing." in store.memory_file.read_text()
@pytest.mark.asyncio
async def test_list_arguments_empty_list_returns_false(self, tmp_path: Path) -> None:
"""Empty list arguments should return False."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=[],
)
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
assert result is False
@pytest.mark.asyncio
async def test_list_arguments_non_dict_content_returns_false(self, tmp_path: Path) -> None:
"""List with non-dict content should return False."""
store = MemoryStore(tmp_path)
provider = AsyncMock()
response = LLMResponse(
content=None,
tool_calls=[
ToolCallRequest(
id="call_1",
name="save_memory",
arguments=["string", "content"],
)
],
)
provider.chat = AsyncMock(return_value=response)
session = _make_session(message_count=60)
result = await store.consolidate(session, provider, "test-model", memory_window=50)
assert result is False

29
tests/test_message_tool_suppress.py
View File

@@ -86,6 +86,35 @@ class TestMessageToolSuppressLogic:
assert result is not None
assert "Hello" in result.content
async def test_progress_hides_internal_reasoning(self, tmp_path: Path) -> None:
loop = _make_loop(tmp_path)
tool_call = ToolCallRequest(id="call1", name="read_file", arguments={"path": "foo.txt"})
calls = iter([
LLMResponse(
content="Visible<think>hidden</think>",
tool_calls=[tool_call],
reasoning_content="secret reasoning",
thinking_blocks=[{"signature": "sig", "thought": "secret thought"}],
),
LLMResponse(content="Done", tool_calls=[]),
])
loop.provider.chat = AsyncMock(side_effect=lambda *a, **kw: next(calls))
loop.tools.get_definitions = MagicMock(return_value=[])
loop.tools.execute = AsyncMock(return_value="ok")
progress: list[tuple[str, bool]] = []
async def on_progress(content: str, *, tool_hint: bool = False) -> None:
progress.append((content, tool_hint))
final_content, _, _ = await loop._run_agent_loop([], on_progress=on_progress)
assert final_content == "Done"
assert progress == [
("Visible", False),
('read_file("foo.txt")', True),
]
class TestMessageToolTurnTracking:

162
tests/test_telegram_channel.py Normal file
View File

@@ -0,0 +1,162 @@
from types import SimpleNamespace
import pytest
from nanobot.bus.events import OutboundMessage
from nanobot.bus.queue import MessageBus
from nanobot.channels.telegram import TelegramChannel
from nanobot.config.schema import TelegramConfig
class _FakeHTTPXRequest:
instances: list["_FakeHTTPXRequest"] = []
def __init__(self, **kwargs) -> None:
self.kwargs = kwargs
self.__class__.instances.append(self)
class _FakeUpdater:
def __init__(self, on_start_polling) -> None:
self._on_start_polling = on_start_polling
async def start_polling(self, **kwargs) -> None:
self._on_start_polling()
class _FakeBot:
def __init__(self) -> None:
self.sent_messages: list[dict] = []
async def get_me(self):
return SimpleNamespace(username="nanobot_test")
async def set_my_commands(self, commands) -> None:
self.commands = commands
async def send_message(self, **kwargs) -> None:
self.sent_messages.append(kwargs)
class _FakeApp:
def __init__(self, on_start_polling) -> None:
self.bot = _FakeBot()
self.updater = _FakeUpdater(on_start_polling)
self.handlers = []
self.error_handlers = []
def add_error_handler(self, handler) -> None:
self.error_handlers.append(handler)
def add_handler(self, handler) -> None:
self.handlers.append(handler)
async def initialize(self) -> None:
pass
async def start(self) -> None:
pass
class _FakeBuilder:
def __init__(self, app: _FakeApp) -> None:
self.app = app
self.token_value = None
self.request_value = None
self.get_updates_request_value = None
def token(self, token: str):
self.token_value = token
return self
def request(self, request):
self.request_value = request
return self
def get_updates_request(self, request):
self.get_updates_request_value = request
return self
def proxy(self, _proxy):
raise AssertionError("builder.proxy should not be called when request is set")
def get_updates_proxy(self, _proxy):
raise AssertionError("builder.get_updates_proxy should not be called when request is set")
def build(self):
return self.app
@pytest.mark.asyncio
async def test_start_uses_request_proxy_without_builder_proxy(monkeypatch) -> None:
config = TelegramConfig(
enabled=True,
token="123:abc",
allow_from=["*"],
proxy="http://127.0.0.1:7890",
)
bus = MessageBus()
channel = TelegramChannel(config, bus)
app = _FakeApp(lambda: setattr(channel, "_running", False))
builder = _FakeBuilder(app)
monkeypatch.setattr("nanobot.channels.telegram.HTTPXRequest", _FakeHTTPXRequest)
monkeypatch.setattr(
"nanobot.channels.telegram.Application",
SimpleNamespace(builder=lambda: builder),
)
await channel.start()
assert len(_FakeHTTPXRequest.instances) == 1
assert _FakeHTTPXRequest.instances[0].kwargs["proxy"] == config.proxy
assert builder.request_value is _FakeHTTPXRequest.instances[0]
assert builder.get_updates_request_value is _FakeHTTPXRequest.instances[0]
def test_derive_topic_session_key_uses_thread_id() -> None:
message = SimpleNamespace(
chat=SimpleNamespace(type="supergroup"),
chat_id=-100123,
message_thread_id=42,
)
assert TelegramChannel._derive_topic_session_key(message) == "telegram:-100123:topic:42"
@pytest.mark.asyncio
async def test_send_progress_keeps_message_in_topic() -> None:
config = TelegramConfig(enabled=True, token="123:abc", allow_from=["*"])
channel = TelegramChannel(config, MessageBus())
channel._app = _FakeApp(lambda: None)
await channel.send(
OutboundMessage(
channel="telegram",
chat_id="123",
content="hello",
metadata={"_progress": True, "message_thread_id": 42},
)
)
assert channel._app.bot.sent_messages[0]["message_thread_id"] == 42
@pytest.mark.asyncio
async def test_send_reply_infers_topic_from_message_id_cache() -> None:
config = TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], reply_to_message=True)
channel = TelegramChannel(config, MessageBus())
channel._app = _FakeApp(lambda: None)
channel._message_threads[("123", 10)] = 42
await channel.send(
OutboundMessage(
channel="telegram",
chat_id="123",
content="hello",
metadata={"message_id": 10},
)
)
assert channel._app.bot.sent_messages[0]["message_thread_id"] == 42
assert channel._app.bot.sent_messages[0]["reply_parameters"].message_id == 10

231
tests/test_tool_validation.py
View File

@@ -106,3 +106,234 @@ def test_exec_extract_absolute_paths_captures_posix_absolute_paths() -> None:
paths = ExecTool._extract_absolute_paths(cmd)
assert "/tmp/data.txt" in paths
assert "/tmp/out.txt" in paths
# --- cast_params tests ---
class CastTestTool(Tool):
"""Minimal tool for testing cast_params."""
def __init__(self, schema: dict[str, Any]) -> None:
self._schema = schema
@property
def name(self) -> str:
return "cast_test"
@property
def description(self) -> str:
return "test tool for casting"
@property
def parameters(self) -> dict[str, Any]:
return self._schema
async def execute(self, **kwargs: Any) -> str:
return "ok"
def test_cast_params_string_to_int() -> None:
tool = CastTestTool(
{
"type": "object",
"properties": {"count": {"type": "integer"}},
}
)
result = tool.cast_params({"count": "42"})
assert result["count"] == 42
assert isinstance(result["count"], int)
def test_cast_params_string_to_number() -> None:
tool = CastTestTool(
{
"type": "object",
"properties": {"rate": {"type": "number"}},
}
)
result = tool.cast_params({"rate": "3.14"})
assert result["rate"] == 3.14
assert isinstance(result["rate"], float)
def test_cast_params_string_to_bool() -> None:
tool = CastTestTool(
{
"type": "object",
"properties": {"enabled": {"type": "boolean"}},
}
)
assert tool.cast_params({"enabled": "true"})["enabled"] is True
assert tool.cast_params({"enabled": "false"})["enabled"] is False
assert tool.cast_params({"enabled": "1"})["enabled"] is True
def test_cast_params_array_items() -> None:
tool = CastTestTool(
{
"type": "object",
"properties": {
"nums": {"type": "array", "items": {"type": "integer"}},
},
}
)
result = tool.cast_params({"nums": ["1", "2", "3"]})
assert result["nums"] == [1, 2, 3]
def test_cast_params_nested_object() -> None:
tool = CastTestTool(
{
"type": "object",
"properties": {
"config": {
"type": "object",
"properties": {
"port": {"type": "integer"},
"debug": {"type": "boolean"},
},
},
},
}
)
result = tool.cast_params({"config": {"port": "8080", "debug": "true"}})
assert result["config"]["port"] == 8080
assert result["config"]["debug"] is True
def test_cast_params_bool_not_cast_to_int() -> None:
"""Booleans should not be silently cast to integers."""
tool = CastTestTool(
{
"type": "object",
"properties": {"count": {"type": "integer"}},
}
)
result = tool.cast_params({"count": True})
assert result["count"] is True
errors = tool.validate_params(result)
assert any("count should be integer" in e for e in errors)
def test_cast_params_preserves_empty_string() -> None:
"""Empty strings should be preserved for string type."""
tool = CastTestTool(
{
"type": "object",
"properties": {"name": {"type": "string"}},
}
)
result = tool.cast_params({"name": ""})
assert result["name"] == ""
def test_cast_params_bool_string_false() -> None:
"""Test that 'false', '0', 'no' strings convert to False."""
tool = CastTestTool(
{
"type": "object",
"properties": {"flag": {"type": "boolean"}},
}
)
assert tool.cast_params({"flag": "false"})["flag"] is False
assert tool.cast_params({"flag": "False"})["flag"] is False
assert tool.cast_params({"flag": "0"})["flag"] is False
assert tool.cast_params({"flag": "no"})["flag"] is False
assert tool.cast_params({"flag": "NO"})["flag"] is False
def test_cast_params_bool_string_invalid() -> None:
"""Invalid boolean strings should not be cast."""
tool = CastTestTool(
{
"type": "object",
"properties": {"flag": {"type": "boolean"}},
}
)
# Invalid strings should be preserved (validation will catch them)
result = tool.cast_params({"flag": "random"})
assert result["flag"] == "random"
result = tool.cast_params({"flag": "maybe"})
assert result["flag"] == "maybe"
def test_cast_params_invalid_string_to_int() -> None:
"""Invalid strings should not be cast to integer."""
tool = CastTestTool(
{
"type": "object",
"properties": {"count": {"type": "integer"}},
}
)
result = tool.cast_params({"count": "abc"})
assert result["count"] == "abc" # Original value preserved
result = tool.cast_params({"count": "12.5.7"})
assert result["count"] == "12.5.7"
def test_cast_params_invalid_string_to_number() -> None:
"""Invalid strings should not be cast to number."""
tool = CastTestTool(
{
"type": "object",
"properties": {"rate": {"type": "number"}},
}
)
result = tool.cast_params({"rate": "not_a_number"})
assert result["rate"] == "not_a_number"
def test_validate_params_bool_not_accepted_as_number() -> None:
"""Booleans should not pass number validation."""
tool = CastTestTool(
{
"type": "object",
"properties": {"rate": {"type": "number"}},
}
)
errors = tool.validate_params({"rate": False})
assert any("rate should be number" in e for e in errors)
def test_cast_params_none_values() -> None:
"""Test None handling for different types."""
tool = CastTestTool(
{
"type": "object",
"properties": {
"name": {"type": "string"},
"count": {"type": "integer"},
"items": {"type": "array"},
"config": {"type": "object"},
},
}
)
result = tool.cast_params(
{
"name": None,
"count": None,
"items": None,
"config": None,
}
)
# None should be preserved for all types
assert result["name"] is None
assert result["count"] is None
assert result["items"] is None
assert result["config"] is None
def test_cast_params_single_value_not_auto_wrapped_to_array() -> None:
"""Single values should NOT be automatically wrapped into arrays."""
tool = CastTestTool(
{
"type": "object",
"properties": {"items": {"type": "array"}},
}
)
# Non-array values should be preserved (validation will catch them)
result = tool.cast_params({"items": 5})
assert result["items"] == 5 # Not wrapped to [5]
result = tool.cast_params({"items": "text"})
assert result["items"] == "text" # Not wrapped to ["text"]