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Merge branch 'main' into pr-926

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Re-bin
2026-02-21 08:21:17 +00:00
parent c4bee640b8 4dca2872bf
commit 012a5e78e5
4 changed files with 148 additions and 225 deletions
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2
README.md
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@@ -16,7 +16,7 @@
⚡️ Delivers core agent functionality in just **~4,000** lines of code — **99% smaller** than Clawdbot's 430k+ lines.
📏 Real-time line count: **3,827 lines** (run `bash core_agent_lines.sh` to verify anytime)
📏 Real-time line count: **3,806 lines** (run `bash core_agent_lines.sh` to verify anytime)
## 📢 News

1
nanobot/agent/context.py
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@@ -106,6 +106,7 @@ Only use the 'message' tool when you need to send a message to a specific chat c
For normal conversation, just respond with text - do not call the message tool.
Always be helpful, accurate, and concise. Before calling tools, briefly tell the user what you're about to do (one short sentence in the user's language).
If you need to use tools, call them directly — never send a preliminary message like "Let me check" without actually calling a tool.
When remembering something important, write to {workspace_path}/memory/MEMORY.md
To recall past events, grep {workspace_path}/memory/HISTORY.md"""

262
nanobot/agent/loop.py
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@@ -9,7 +9,6 @@ from contextlib import AsyncExitStack
from pathlib import Path
from typing import TYPE_CHECKING, Awaitable, Callable
import json_repair
from loguru import logger
from nanobot.agent.context import ContextBuilder
@@ -100,33 +99,18 @@ class AgentLoop:
def _register_default_tools(self) -> None:
"""Register the default set of tools."""
# File tools (workspace for relative paths, restrict if configured)
allowed_dir = self.workspace if self.restrict_to_workspace else None
self.tools.register(ReadFileTool(workspace=self.workspace, allowed_dir=allowed_dir))
self.tools.register(WriteFileTool(workspace=self.workspace, allowed_dir=allowed_dir))
self.tools.register(EditFileTool(workspace=self.workspace, allowed_dir=allowed_dir))
self.tools.register(ListDirTool(workspace=self.workspace, allowed_dir=allowed_dir))
# Shell tool
for cls in (ReadFileTool, WriteFileTool, EditFileTool, ListDirTool):
self.tools.register(cls(workspace=self.workspace, allowed_dir=allowed_dir))
self.tools.register(ExecTool(
working_dir=str(self.workspace),
timeout=self.exec_config.timeout,
restrict_to_workspace=self.restrict_to_workspace,
))
# Web tools
self.tools.register(WebSearchTool(api_key=self.brave_api_key))
self.tools.register(WebFetchTool())
# Message tool
message_tool = MessageTool(send_callback=self.bus.publish_outbound)
self.tools.register(message_tool)
# Spawn tool (for subagents)
spawn_tool = SpawnTool(manager=self.subagents)
self.tools.register(spawn_tool)
# Cron tool (for scheduling)
self.tools.register(MessageTool(send_callback=self.bus.publish_outbound))
self.tools.register(SpawnTool(manager=self.subagents))
if self.cron_service:
self.tools.register(CronTool(self.cron_service))
@@ -188,21 +172,11 @@ class AgentLoop:
initial_messages: list[dict],
on_progress: Callable[[str], Awaitable[None]] | None = None,
) -> tuple[str | None, list[str]]:
"""
Run the agent iteration loop.
Args:
initial_messages: Starting messages for the LLM conversation.
on_progress: Optional callback to push intermediate content to the user.
Returns:
Tuple of (final_content, list_of_tools_used).
"""
"""Run the agent iteration loop. Returns (final_content, tools_used)."""
messages = initial_messages
iteration = 0
final_content = None
tools_used: list[str] = []
text_only_retried = False
while iteration < self.max_iterations:
iteration += 1
@@ -248,13 +222,6 @@ class AgentLoop:
)
else:
final_content = self._strip_think(response.content)
# Some models send an interim text response before tool calls.
# Give them one retry; don't forward the text to avoid duplicates.
if not tools_used and not text_only_retried and final_content:
text_only_retried = True
logger.debug("Interim text response (no tools used yet), retrying: {}", final_content[:80])
final_content = None
continue
break
return final_content, tools_used
@@ -312,20 +279,25 @@ class AgentLoop:
session_key: str | None = None,
on_progress: Callable[[str], Awaitable[None]] | None = None,
) -> OutboundMessage | None:
"""
Process a single inbound message.
Args:
msg: The inbound message to process.
session_key: Override session key (used by process_direct).
on_progress: Optional callback for intermediate output (defaults to bus publish).
Returns:
The response message, or None if no response needed.
"""
# System messages route back via chat_id ("channel:chat_id")
"""Process a single inbound message and return the response."""
# System messages: parse origin from chat_id ("channel:chat_id")
if msg.channel == "system":
return await self._process_system_message(msg)
channel, chat_id = (msg.chat_id.split(":", 1) if ":" in msg.chat_id
else ("cli", msg.chat_id))
logger.info("Processing system message from {}", msg.sender_id)
key = f"{channel}:{chat_id}"
session = self.sessions.get_or_create(key)
self._set_tool_context(channel, chat_id, msg.metadata.get("message_id"))
messages = self.context.build_messages(
history=session.get_history(max_messages=self.memory_window),
current_message=msg.content, channel=channel, chat_id=chat_id,
)
final_content, _ = await self._run_agent_loop(messages)
session.add_message("user", f"[System: {msg.sender_id}] {msg.content}")
session.add_message("assistant", final_content or "Background task completed.")
self.sessions.save(session)
return OutboundMessage(channel=channel, chat_id=chat_id,
content=final_content or "Background task completed.")
preview = msg.content[:80] + "..." if len(msg.content) > 80 else msg.content
logger.info("Processing message from {}:{}: {}", msg.channel, msg.sender_id, preview)
@@ -333,19 +305,18 @@ class AgentLoop:
key = session_key or msg.session_key
session = self.sessions.get_or_create(key)
# Handle slash commands
# Slash commands
cmd = msg.content.strip().lower()
if cmd == "/new":
# Capture messages before clearing (avoid race condition with background task)
messages_to_archive = session.messages.copy()
session.clear()
self.sessions.save(session)
self.sessions.invalidate(session.key)
async def _consolidate_and_cleanup():
temp_session = Session(key=session.key)
temp_session.messages = messages_to_archive
await self._consolidate_memory(temp_session, archive_all=True)
temp = Session(key=session.key)
temp.messages = messages_to_archive
await self._consolidate_memory(temp, archive_all=True)
asyncio.create_task(_consolidate_and_cleanup())
return OutboundMessage(channel=msg.channel, chat_id=msg.chat_id,
@@ -374,16 +345,14 @@ class AgentLoop:
history=session.get_history(max_messages=self.memory_window),
current_message=msg.content,
media=msg.media if msg.media else None,
channel=msg.channel,
chat_id=msg.chat_id,
channel=msg.channel, chat_id=msg.chat_id,
)
async def _bus_progress(content: str) -> None:
meta = dict(msg.metadata or {})
meta["_progress"] = True
await self.bus.publish_outbound(OutboundMessage(
channel=msg.channel, chat_id=msg.chat_id, content=content,
metadata=meta,
channel=msg.channel, chat_id=msg.chat_id, content=content, metadata=meta,
))
final_content, tools_used = await self._run_agent_loop(
@@ -406,153 +375,16 @@ class AgentLoop:
return None
return OutboundMessage(
channel=msg.channel,
chat_id=msg.chat_id,
content=final_content,
metadata=msg.metadata or {}, # Pass through for channel-specific needs (e.g. Slack thread_ts)
)
async def _process_system_message(self, msg: InboundMessage) -> OutboundMessage | None:
"""
Process a system message (e.g., subagent announce).
The chat_id field contains "original_channel:original_chat_id" to route
the response back to the correct destination.
"""
logger.info("Processing system message from {}", msg.sender_id)
# Parse origin from chat_id (format: "channel:chat_id")
if ":" in msg.chat_id:
parts = msg.chat_id.split(":", 1)
origin_channel = parts[0]
origin_chat_id = parts[1]
else:
# Fallback
origin_channel = "cli"
origin_chat_id = msg.chat_id
session_key = f"{origin_channel}:{origin_chat_id}"
session = self.sessions.get_or_create(session_key)
self._set_tool_context(origin_channel, origin_chat_id, msg.metadata.get("message_id"))
initial_messages = self.context.build_messages(
history=session.get_history(max_messages=self.memory_window),
current_message=msg.content,
channel=origin_channel,
chat_id=origin_chat_id,
)
final_content, _ = await self._run_agent_loop(initial_messages)
if final_content is None:
final_content = "Background task completed."
session.add_message("user", f"[System: {msg.sender_id}] {msg.content}")
session.add_message("assistant", final_content)
self.sessions.save(session)
return OutboundMessage(
channel=origin_channel,
chat_id=origin_chat_id,
content=final_content
channel=msg.channel, chat_id=msg.chat_id, content=final_content,
metadata=msg.metadata or {},
)
async def _consolidate_memory(self, session, archive_all: bool = False) -> None:
"""Consolidate old messages into MEMORY.md + HISTORY.md.
Args:
archive_all: If True, clear all messages and reset session (for /new command).
If False, only write to files without modifying session.
"""
memory = MemoryStore(self.workspace)
if archive_all:
old_messages = session.messages
keep_count = 0
logger.info("Memory consolidation (archive_all): {} total messages archived", len(session.messages))
else:
keep_count = self.memory_window // 2
if len(session.messages) <= keep_count:
logger.debug("Session {}: No consolidation needed (messages={}, keep={})", session.key, len(session.messages), keep_count)
return
messages_to_process = len(session.messages) - session.last_consolidated
if messages_to_process <= 0:
logger.debug("Session {}: No new messages to consolidate (last_consolidated={}, total={})", session.key, session.last_consolidated, len(session.messages))
return
old_messages = session.messages[session.last_consolidated:-keep_count]
if not old_messages:
return
logger.info("Memory consolidation started: {} total, {} new to consolidate, {} keep", len(session.messages), len(old_messages), keep_count)
lines = []
for m in old_messages:
if not m.get("content"):
continue
tools = f" [tools: {', '.join(m['tools_used'])}]" if m.get("tools_used") else ""
lines.append(f"[{m.get('timestamp', '?')[:16]}] {m['role'].upper()}{tools}: {m['content']}")
conversation = "\n".join(lines)
current_memory = memory.read_long_term()
prompt = f"""You are a memory consolidation agent. Process this conversation and return a JSON object with exactly two keys:
1. "history_entry": A paragraph (2-5 sentences) summarizing the key events/decisions/topics. Start with a timestamp like [YYYY-MM-DD HH:MM]. Include enough detail to be useful when found by grep search later.
2. "memory_update": The updated long-term memory content. Add any new facts: user location, preferences, personal info, habits, project context, technical decisions, tools/services used. If nothing new, return the existing content unchanged.
## Current Long-term Memory
{current_memory or "(empty)"}
## Conversation to Process
{conversation}
**IMPORTANT**: Both values MUST be strings, not objects or arrays.
Example:
{{
"history_entry": "[2026-02-14 22:50] User asked about...",
"memory_update": "- Host: HARRYBOOK-T14P\n- Name: Nado"
}}
Respond with ONLY valid JSON, no markdown fences."""
try:
response = await self.provider.chat(
messages=[
{"role": "system", "content": "You are a memory consolidation agent. Respond only with valid JSON."},
{"role": "user", "content": prompt},
],
model=self.model,
)
text = (response.content or "").strip()
if not text:
logger.warning("Memory consolidation: LLM returned empty response, skipping")
return
if text.startswith("```"):
text = text.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
result = json_repair.loads(text)
if not isinstance(result, dict):
logger.warning("Memory consolidation: unexpected response type, skipping. Response: {}", text[:200])
return
if entry := result.get("history_entry"):
# Defensive: ensure entry is a string (LLM may return dict)
if not isinstance(entry, str):
entry = json.dumps(entry, ensure_ascii=False)
memory.append_history(entry)
if update := result.get("memory_update"):
# Defensive: ensure update is a string
if not isinstance(update, str):
update = json.dumps(update, ensure_ascii=False)
if update != current_memory:
memory.write_long_term(update)
if archive_all:
session.last_consolidated = 0
else:
session.last_consolidated = len(session.messages) - keep_count
logger.info("Memory consolidation done: {} messages, last_consolidated={}", len(session.messages), session.last_consolidated)
except Exception as e:
logger.error("Memory consolidation failed: {}", e)
"""Delegate to MemoryStore.consolidate()."""
await MemoryStore(self.workspace).consolidate(
session, self.provider, self.model,
archive_all=archive_all, memory_window=self.memory_window,
)
async def process_direct(
self,
@@ -562,26 +394,8 @@ Respond with ONLY valid JSON, no markdown fences."""
chat_id: str = "direct",
on_progress: Callable[[str], Awaitable[None]] | None = None,
) -> str:
"""
Process a message directly (for CLI or cron usage).
Args:
content: The message content.
session_key: Session identifier (overrides channel:chat_id for session lookup).
channel: Source channel (for tool context routing).
chat_id: Source chat ID (for tool context routing).
on_progress: Optional callback for intermediate output.
Returns:
The agent's response.
"""
"""Process a message directly (for CLI or cron usage)."""
await self._connect_mcp()
msg = InboundMessage(
channel=channel,
sender_id="user",
chat_id=chat_id,
content=content
)
msg = InboundMessage(channel=channel, sender_id="user", chat_id=chat_id, content=content)
response = await self._process_message(msg, session_key=session_key, on_progress=on_progress)
return response.content if response else ""

108
nanobot/agent/memory.py
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@@ -1,9 +1,46 @@
"""Memory system for persistent agent memory."""
from __future__ import annotations
import json
from pathlib import Path
from typing import TYPE_CHECKING
from loguru import logger
from nanobot.utils.helpers import ensure_dir
if TYPE_CHECKING:
from nanobot.providers.base import LLMProvider
from nanobot.session.manager import Session
_SAVE_MEMORY_TOOL = [
{
"type": "function",
"function": {
"name": "save_memory",
"description": "Save the memory consolidation result to persistent storage.",
"parameters": {
"type": "object",
"properties": {
"history_entry": {
"type": "string",
"description": "A paragraph (2-5 sentences) summarizing key events/decisions/topics. "
"Start with [YYYY-MM-DD HH:MM]. Include detail useful for grep search.",
},
"memory_update": {
"type": "string",
"description": "Full updated long-term memory as markdown. Include all existing "
"facts plus new ones. Return unchanged if nothing new.",
},
},
"required": ["history_entry", "memory_update"],
},
},
}
]
class MemoryStore:
"""Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
@@ -28,3 +65,74 @@ class MemoryStore:
def get_memory_context(self) -> str:
long_term = self.read_long_term()
return f"## Long-term Memory\n{long_term}" if long_term else ""
async def consolidate(
self,
session: Session,
provider: LLMProvider,
model: str,
*,
archive_all: bool = False,
memory_window: int = 50,
) -> None:
"""Consolidate old messages into MEMORY.md + HISTORY.md via LLM tool call."""
if archive_all:
old_messages = session.messages
keep_count = 0
logger.info("Memory consolidation (archive_all): {} messages", len(session.messages))
else:
keep_count = memory_window // 2
if len(session.messages) <= keep_count:
return
if len(session.messages) - session.last_consolidated <= 0:
return
old_messages = session.messages[session.last_consolidated:-keep_count]
if not old_messages:
return
logger.info("Memory consolidation: {} to consolidate, {} keep", len(old_messages), keep_count)
lines = []
for m in old_messages:
if not m.get("content"):
continue
tools = f" [tools: {', '.join(m['tools_used'])}]" if m.get("tools_used") else ""
lines.append(f"[{m.get('timestamp', '?')[:16]}] {m['role'].upper()}{tools}: {m['content']}")
current_memory = self.read_long_term()
prompt = f"""Process this conversation and call the save_memory tool with your consolidation.
## Current Long-term Memory
{current_memory or "(empty)"}
## Conversation to Process
{chr(10).join(lines)}"""
try:
response = await provider.chat(
messages=[
{"role": "system", "content": "You are a memory consolidation agent. Call the save_memory tool with your consolidation of the conversation."},
{"role": "user", "content": prompt},
],
tools=_SAVE_MEMORY_TOOL,
model=model,
)
if not response.has_tool_calls:
logger.warning("Memory consolidation: LLM did not call save_memory, skipping")
return
args = response.tool_calls[0].arguments
if entry := args.get("history_entry"):
if not isinstance(entry, str):
entry = json.dumps(entry, ensure_ascii=False)
self.append_history(entry)
if update := args.get("memory_update"):
if not isinstance(update, str):
update = json.dumps(update, ensure_ascii=False)
if update != current_memory:
self.write_long_term(update)
session.last_consolidated = 0 if archive_all else len(session.messages) - keep_count
logger.info("Memory consolidation done: {} messages, last_consolidated={}", len(session.messages), session.last_consolidated)
except Exception as e:
logger.error("Memory consolidation failed: {}", e)