nanobot-render AGENTS.md

This file provides guidance to AI coding agents working with this repository.

Project Overview

nanobot is a lightweight, open-source AI agent framework written in Python with a React/TypeScript WebUI. It centers around a small agent loop that receives messages from chat channels, invokes an LLM provider, executes tools, and manages session memory.

Development Commands

# Python: run single test / lint
pytest tests/test_openai_api.py::test_function -v
ruff check nanobot/

# WebUI: dev server (proxies API/WS to gateway :8765), build, test
# Build outputs to ../nanobot/web/dist (bundled into the Python wheel)
cd webui && bun run dev      # or NANOBOT_API_URL=... bun run dev
cd webui && bun run build
cd webui && bun run test

# Gateway
nanobot gateway

High-Level Architecture

Core Data Flow

Messages flow through an async MessageBus (nanobot/bus/queue.py) that decouples chat channels from the agent core:

1. Channels (nanobot/channels/) receive messages from external platforms and publish InboundMessage events to the bus.
2. AgentLoop (nanobot/agent/loop.py) consumes inbound messages, builds context, and coordinates the turn.
3. AgentRunner (nanobot/agent/runner.py) handles the actual LLM conversation loop: send messages to the provider, receive tool calls, execute tools, and stream responses.
4. Responses are published as OutboundMessage events back to the appropriate channel.

Key Subsystems

• Agent Loop (nanobot/agent/loop.py, runner.py): The core processing engine. AgentLoop manages session keys, hooks, and context building. AgentRunner executes the multi-turn LLM conversation with tool execution.
• LLM Providers (nanobot/providers/): Provider implementations (Anthropic, OpenAI-compatible, OpenAI Responses API, Azure, Bedrock, GitHub Copilot, OpenAI Codex, etc.) built on a common base (base.py). Includes image generation (image_generation.py) and audio transcription (transcription.py). factory.py and registry.py handle instantiation and model discovery.
• Channels (nanobot/channels/): Platform integrations (Telegram, Discord, Slack, Feishu, Matrix, WhatsApp, QQ, WeChat, WeCom, DingTalk, Email, MoChat, MS Teams, WebSocket). manager.py discovers and coordinates them. Channels are auto-discovered via pkgutil scan + entry-point plugins.
• Tools (nanobot/agent/tools/): Agent capabilities exposed to the LLM: filesystem (read/write/edit/list), shell execution (with sandbox backends), web search/fetch, MCP servers, cron, notebook editing, subagent spawning, long-running tasks / sustained goals (long_task.py), image generation, and self-modification. Tools are auto-discovered via pkgutil scan + entry-point plugins.
• Memory (nanobot/agent/memory.py): Session history persistence with Dream two-phase memory consolidation. Uses atomic writes with fsync for durability.
• Session Management (nanobot/session/): Per-session history, context compaction, TTL-based auto-compaction (manager.py), and sustained goal state tracking (goal_state.py).
• Config (nanobot/config/schema.py, loader.py): Pydantic-based configuration loaded from ~/.nanobot/config.json. Supports camelCase aliases for JSON compatibility.
• WebUI (webui/): Vite-based React SPA that talks to the gateway over a WebSocket multiplex protocol. The dev server proxies /api, /webui, /auth, and WebSocket traffic to the gateway.
• API Server (nanobot/api/server.py): OpenAI-compatible HTTP API (/v1/chat/completions, /v1/models) for programmatic access.
• Command Router (nanobot/command/): Slash command routing and built-in command handlers.
• Heartbeat (nanobot/templates/HEARTBEAT.md): Periodic task list checked via cron jobs (legacy dedicated service removed).
• Pairing (nanobot/pairing/): DM sender approval store with persistent pairing codes per channel.
• Skills (nanobot/skills/): Built-in skill definitions (long-goal, cron, github, image-generation, etc.) loaded into agent context.
• Security (nanobot/security/): PTH file guard and other security measures activated at CLI entry.