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feat: redesign memory system — two-layer architecture with grep-based retrieval

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Re-bin
2026-02-12 15:02:52 +00:00
parent a05e58cf79
commit 94c21fc235
9 changed files with 141 additions and 117 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: **3,578 lines** (run `bash core_agent_lines.sh` to verify anytime)
📏 Real-time line count: **3,562 lines** (run `bash core_agent_lines.sh` to verify anytime)
## 📢 News

7
nanobot/agent/context.py
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@@ -97,8 +97,8 @@ You are nanobot, a helpful AI assistant. You have access to tools that allow you
## Workspace
Your workspace is at: {workspace_path}
- Memory files: {workspace_path}/memory/MEMORY.md
- Daily notes: {workspace_path}/memory/YYYY-MM-DD.md
- Long-term memory: {workspace_path}/memory/MEMORY.md
- History log: {workspace_path}/memory/HISTORY.md (grep-searchable)
- Custom skills: {workspace_path}/skills/{{skill-name}}/SKILL.md
IMPORTANT: When responding to direct questions or conversations, reply directly with your text response.
@@ -106,7 +106,8 @@ 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. When using tools, think step by step: what you know, what you need, and why you chose this tool.
When remembering something, write to {workspace_path}/memory/MEMORY.md"""
When remembering something important, write to {workspace_path}/memory/MEMORY.md
To recall past events, grep {workspace_path}/memory/HISTORY.md"""
def _load_bootstrap_files(self) -> str:
"""Load all bootstrap files from workspace."""

88
nanobot/agent/loop.py
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@@ -18,6 +18,7 @@ from nanobot.agent.tools.web import WebSearchTool, WebFetchTool
from nanobot.agent.tools.message import MessageTool
from nanobot.agent.tools.spawn import SpawnTool
from nanobot.agent.tools.cron import CronTool
from nanobot.agent.memory import MemoryStore
from nanobot.agent.subagent import SubagentManager
from nanobot.session.manager import SessionManager
@@ -41,6 +42,7 @@ class AgentLoop:
workspace: Path,
model: str | None = None,
max_iterations: int = 20,
memory_window: int = 50,
brave_api_key: str | None = None,
exec_config: "ExecToolConfig | None" = None,
cron_service: "CronService | None" = None,
@@ -54,6 +56,7 @@ class AgentLoop:
self.workspace = workspace
self.model = model or provider.get_default_model()
self.max_iterations = max_iterations
self.memory_window = memory_window
self.brave_api_key = brave_api_key
self.exec_config = exec_config or ExecToolConfig()
self.cron_service = cron_service
@@ -141,12 +144,13 @@ class AgentLoop:
self._running = False
logger.info("Agent loop stopping")
async def _process_message(self, msg: InboundMessage) -> OutboundMessage | None:
async def _process_message(self, msg: InboundMessage, session_key: str | 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).
Returns:
The response message, or None if no response needed.
@@ -160,7 +164,11 @@ class AgentLoop:
logger.info(f"Processing message from {msg.channel}:{msg.sender_id}: {preview}")
# Get or create session
session = self.sessions.get_or_create(msg.session_key)
session = self.sessions.get_or_create(session_key or msg.session_key)
# Consolidate memory before processing if session is too large
if len(session.messages) > self.memory_window:
await self._consolidate_memory(session)
# Update tool contexts
message_tool = self.tools.get("message")
@@ -187,6 +195,7 @@ class AgentLoop:
# Agent loop
iteration = 0
final_content = None
tools_used: list[str] = []
while iteration < self.max_iterations:
iteration += 1
@@ -219,6 +228,7 @@ class AgentLoop:
# Execute tools
for tool_call in response.tool_calls:
tools_used.append(tool_call.name)
args_str = json.dumps(tool_call.arguments, ensure_ascii=False)
logger.info(f"Tool call: {tool_call.name}({args_str[:200]})")
result = await self.tools.execute(tool_call.name, tool_call.arguments)
@@ -239,9 +249,10 @@ class AgentLoop:
preview = final_content[:120] + "..." if len(final_content) > 120 else final_content
logger.info(f"Response to {msg.channel}:{msg.sender_id}: {preview}")
# Save to session
# Save to session (include tool names so consolidation sees what happened)
session.add_message("user", msg.content)
session.add_message("assistant", final_content)
session.add_message("assistant", final_content,
tools_used=tools_used if tools_used else None)
self.sessions.save(session)
return OutboundMessage(
@@ -352,6 +363,67 @@ class AgentLoop:
content=final_content
)
async def _consolidate_memory(self, session) -> None:
"""Consolidate old messages into MEMORY.md + HISTORY.md, then trim session."""
memory = MemoryStore(self.workspace)
keep_count = min(10, max(2, self.memory_window // 2))
old_messages = session.messages[:-keep_count] # Everything except recent ones
if not old_messages:
return
logger.info(f"Memory consolidation started: {len(session.messages)} messages, archiving {len(old_messages)}, keeping {keep_count}")
# Format messages for LLM (include tool names when available)
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}
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,
)
import json as _json
text = (response.content or "").strip()
# Strip markdown fences that LLMs often add despite instructions
if text.startswith("```"):
text = text.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
result = _json.loads(text)
if entry := result.get("history_entry"):
memory.append_history(entry)
if update := result.get("memory_update"):
if update != current_memory:
memory.write_long_term(update)
# Trim session to recent messages
session.messages = session.messages[-keep_count:]
self.sessions.save(session)
logger.info(f"Memory consolidation done, session trimmed to {len(session.messages)} messages")
except Exception as e:
logger.error(f"Memory consolidation failed: {e}")
async def process_direct(
self,
content: str,
@@ -364,9 +436,9 @@ class AgentLoop:
Args:
content: The message content.
session_key: Session identifier.
channel: Source channel (for context).
chat_id: Source chat ID (for context).
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).
Returns:
The agent's response.
@@ -378,5 +450,5 @@ class AgentLoop:
content=content
)
response = await self._process_message(msg)
response = await self._process_message(msg, session_key=session_key)
return response.content if response else ""

93
nanobot/agent/memory.py
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@@ -1,109 +1,30 @@
"""Memory system for persistent agent memory."""
from pathlib import Path
from datetime import datetime
from nanobot.utils.helpers import ensure_dir, today_date
from nanobot.utils.helpers import ensure_dir
class MemoryStore:
"""
Memory system for the agent.
Supports daily notes (memory/YYYY-MM-DD.md) and long-term memory (MEMORY.md).
"""
"""Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
def __init__(self, workspace: Path):
self.workspace = workspace
self.memory_dir = ensure_dir(workspace / "memory")
self.memory_file = self.memory_dir / "MEMORY.md"
def get_today_file(self) -> Path:
"""Get path to today's memory file."""
return self.memory_dir / f"{today_date()}.md"
def read_today(self) -> str:
"""Read today's memory notes."""
today_file = self.get_today_file()
if today_file.exists():
return today_file.read_text(encoding="utf-8")
return ""
def append_today(self, content: str) -> None:
"""Append content to today's memory notes."""
today_file = self.get_today_file()
if today_file.exists():
existing = today_file.read_text(encoding="utf-8")
content = existing + "\n" + content
else:
# Add header for new day
header = f"# {today_date()}\n\n"
content = header + content
today_file.write_text(content, encoding="utf-8")
self.history_file = self.memory_dir / "HISTORY.md"
def read_long_term(self) -> str:
"""Read long-term memory (MEMORY.md)."""
if self.memory_file.exists():
return self.memory_file.read_text(encoding="utf-8")
return ""
def write_long_term(self, content: str) -> None:
"""Write to long-term memory (MEMORY.md)."""
self.memory_file.write_text(content, encoding="utf-8")
def get_recent_memories(self, days: int = 7) -> str:
"""
Get memories from the last N days.
Args:
days: Number of days to look back.
Returns:
Combined memory content.
"""
from datetime import timedelta
memories = []
today = datetime.now().date()
for i in range(days):
date = today - timedelta(days=i)
date_str = date.strftime("%Y-%m-%d")
file_path = self.memory_dir / f"{date_str}.md"
if file_path.exists():
content = file_path.read_text(encoding="utf-8")
memories.append(content)
return "\n\n---\n\n".join(memories)
def list_memory_files(self) -> list[Path]:
"""List all memory files sorted by date (newest first)."""
if not self.memory_dir.exists():
return []
files = list(self.memory_dir.glob("????-??-??.md"))
return sorted(files, reverse=True)
def append_history(self, entry: str) -> None:
with open(self.history_file, "a", encoding="utf-8") as f:
f.write(entry.rstrip() + "\n\n")
def get_memory_context(self) -> str:
"""
Get memory context for the agent.
Returns:
Formatted memory context including long-term and recent memories.
"""
parts = []
# Long-term memory
long_term = self.read_long_term()
if long_term:
parts.append("## Long-term Memory\n" + long_term)
# Today's notes
today = self.read_today()
if today:
parts.append("## Today's Notes\n" + today)
return "\n\n".join(parts) if parts else ""
return f"## Long-term Memory\n{long_term}" if long_term else ""

11
nanobot/cli/commands.py
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@@ -200,7 +200,7 @@ You are a helpful AI assistant. Be concise, accurate, and friendly.
- Always explain what you're doing before taking actions
- Ask for clarification when the request is ambiguous
- Use tools to help accomplish tasks
- Remember important information in your memory files
- Remember important information in memory/MEMORY.md; past events are logged in memory/HISTORY.md
""",
"SOUL.md": """# Soul
@@ -259,6 +259,11 @@ This file stores important information that should persist across sessions.
""")
console.print(" [dim]Created memory/MEMORY.md[/dim]")
history_file = memory_dir / "HISTORY.md"
if not history_file.exists():
history_file.write_text("")
console.print(" [dim]Created memory/HISTORY.md[/dim]")
# Create skills directory for custom user skills
skills_dir = workspace / "skills"
skills_dir.mkdir(exist_ok=True)
@@ -324,6 +329,7 @@ def gateway(
workspace=config.workspace_path,
model=config.agents.defaults.model,
max_iterations=config.agents.defaults.max_tool_iterations,
memory_window=config.agents.defaults.memory_window,
brave_api_key=config.tools.web.search.api_key or None,
exec_config=config.tools.exec,
cron_service=cron,
@@ -428,6 +434,9 @@ def agent(
bus=bus,
provider=provider,
workspace=config.workspace_path,
model=config.agents.defaults.model,
max_iterations=config.agents.defaults.max_tool_iterations,
memory_window=config.agents.defaults.memory_window,
brave_api_key=config.tools.web.search.api_key or None,
exec_config=config.tools.exec,
restrict_to_workspace=config.tools.restrict_to_workspace,

1
nanobot/config/schema.py
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@@ -161,6 +161,7 @@ class AgentDefaults(BaseModel):
max_tokens: int = 8192
temperature: float = 0.7
max_tool_iterations: int = 20
memory_window: int = 50
class AgentsConfig(BaseModel):

31
nanobot/skills/memory/SKILL.md Normal file
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@@ -0,0 +1,31 @@
---
name: memory
description: Two-layer memory system with grep-based recall.
always: true
---
# Memory
## Structure
- `memory/MEMORY.md` — Long-term facts (preferences, project context, relationships). Always loaded into your context.
- `memory/HISTORY.md` — Append-only event log. NOT loaded into context. Search it with grep.
## Search Past Events
```bash
grep -i "keyword" memory/HISTORY.md
```
Use the `exec` tool to run grep. Combine patterns: `grep -iE "meeting|deadline" memory/HISTORY.md`
## When to Update MEMORY.md
Write important facts immediately using `edit_file` or `write_file`:
- User preferences ("I prefer dark mode")
- Project context ("The API uses OAuth2")
- Relationships ("Alice is the project lead")
## Auto-consolidation
Old conversations are automatically summarized and appended to HISTORY.md when the session grows large. Long-term facts are extracted to MEMORY.md. You don't need to manage this.

11
nanobot/utils/helpers.py
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@@ -37,23 +37,12 @@ def get_sessions_path() -> Path:
return ensure_dir(get_data_path() / "sessions")
def get_memory_path(workspace: Path | None = None) -> Path:
"""Get the memory directory within the workspace."""
ws = workspace or get_workspace_path()
return ensure_dir(ws / "memory")
def get_skills_path(workspace: Path | None = None) -> Path:
"""Get the skills directory within the workspace."""
ws = workspace or get_workspace_path()
return ensure_dir(ws / "skills")
def today_date() -> str:
"""Get today's date in YYYY-MM-DD format."""
return datetime.now().strftime("%Y-%m-%d")
def timestamp() -> str:
"""Get current timestamp in ISO format."""
return datetime.now().isoformat()

4
workspace/AGENTS.md
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@@ -20,8 +20,8 @@ You have access to:
## Memory
- Use `memory/` directory for daily notes
- Use `MEMORY.md` for long-term information
- `memory/MEMORY.md` — long-term facts (preferences, context, relationships)
- `memory/HISTORY.md` — append-only event log, search with grep to recall past events
## Scheduled Reminders