langclaw 0.2.0

Production-ready multi-channel AI agent framework built on langchain 1.0, langgraph, and deepagents

Langclaw

Multi-channel AI agent framework, the LangChain way

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Repository: github.com/tisu19021997/langclaw

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Langclaw is a Python framework for building production-grade, multi-channel AI agent systems — with RBAC, scheduled tasks, persistent memory, subagent delegation, and a pluggable tool ecosystem — on top of LangChain, LangGraph, and deepagents.

FastAPI gave web developers a declarative, decorator-driven way to build APIs. Langclaw brings that same feeling to multi-channel agentic systems. Define tools, roles, subagents, and channels on a single app object — langclaw handles the wiring, middleware, message routing, and state persistence so you can focus on what your agent actually does.

Why Use Langclaw

1. Framework, not a fork: uv add langclaw and build on top of it — like Flask/FastAPI for agentic systems. No repo cloning, no boilerplate.
2. Multi-channel from day one: Telegram, Discord, Slack, WebSocket out of the box. Add custom channels with a single app.add_channel() call.
3. Declarative RBAC: app.role("analyst", tools=["*"]) — one line to define who can use what. Permissions are enforced as middleware before the LLM sees anything.
4. Subagent delegation: Register specialist subagents that run in isolated contexts. The main agent delegates via a built-in task tool; results flow back cleanly or stream directly to the channel.
5. Scheduled jobs: Users can ask the agent to schedule recurring tasks. Cron jobs publish to the same message bus and flow through the same pipeline as user messages.
6. Pluggable everything: Message bus (asyncio / RabbitMQ / Kafka), checkpointer (SQLite / Postgres), LLM providers — swap backends via config, not code changes.
7. Middleware pipeline: Content filtering, PII redaction, rate limiting, and RBAC run as composable middleware before every LLM call.
8. Built on LangChain + LangGraph: Not a wrapper — langclaw compiles down to a real LangGraph CompiledStateGraph. Bring any LangChain tool, model, or integration.

Hello World

from langclaw import Langclaw

app = Langclaw()

@app.tool()
async def greet(name: str) -> str:
    """Say hello to someone."""
    return f"Hello, {name}!"

if __name__ == "__main__":
    app.run()

That's it. Langclaw wires up the message bus, checkpointer, channels (from your .env), and middleware — then starts listening.

Real-World Example

Here's a research assistant with custom tools, subagent delegation, RBAC, lifecycle hooks, and a slash command — all on one app object:

from langclaw import Langclaw
from langclaw.gateway.commands import CommandContext

app = Langclaw(
    system_prompt=(
        "## Research Assistant\n"
        "You are a financial research assistant.\n"
        "Check stock prices before answering. For complex questions, "
        "delegate to the deep-researcher subagent."
    ),
)

# -- Custom tool: stock price lookup ------------------------------------------

@app.tool()
async def get_stock_price(ticker: str) -> dict:
    """Fetch the latest quote for a US stock ticker."""
    ...  # httpx call to Yahoo Finance
    return {"ticker": ticker, "price": 182.52, "change_pct": "+1.23%"}

# -- Subagent: deep research in isolated context ------------------------------

app.subagent(
    "deep-researcher",
    description="Multi-step research using web search and synthesis",
    system_prompt="You are a thorough researcher. Search, synthesise, cite.",
    tools=["web_search", "web_fetch"],
    output="channel",  # stream results directly to the user
)

# -- RBAC: who can use what ---------------------------------------------------

app.role("analyst", tools=["*"])
app.role("free", tools=["web_search"])

# -- Command: bypasses the LLM entirely --------------------------------------

@app.command("watchlist", description="show watchlist prices (no AI)")
async def watchlist_cmd(ctx: CommandContext) -> str:
    return "AAPL: $182.52 | MSFT: $441.20 | NVDA: $135.80"

# -- Lifecycle hooks ----------------------------------------------------------

@app.on_startup
async def setup():
    ...  # open DB connections, HTTP clients, etc.

@app.on_shutdown
async def teardown():
    ...  # clean up resources

if __name__ == "__main__":
    app.run()

See examples/ for complete, runnable versions.

Message Flow

Every message — whether from a user or a cron job — follows the same path:

Channel (Telegram / Discord / Slack / WebSocket)
    │
    ├── /command ──▶ CommandRouter ──▶ instant response (no LLM)
    │
    └── message ──▶ InboundMessage ──▶ Message Bus
                                          │
                                    GatewayManager
                                          │
                                    SessionManager ──▶ Checkpointer
                                          │
                                    Middleware Pipeline
                                    (RBAC → Rate Limit → Content Filter → PII)
                                          │
                                    LangGraph Agent ──▶ Tools / Subagents
                                          │
                                    OutboundMessage ──▶ Channel

Cron jobs publish InboundMessage to the same bus, flowing through the identical pipeline. Commands bypass everything — they're fast system operations handled before the bus.

Installation

uv add langclaw

With channel and backend extras:

uv add "langclaw[telegram,postgres,rabbitmq]"

# Or install everything:
uv add "langclaw[all]"

Available extras: telegram, discord, slack, websocket, postgres, rabbitmq, kafka, mcp, search, gmail.

Quick Start

1. Install the framework and a channel plugin (e.g., Telegram):

   uv add "langclaw[telegram]"
   

2. Set your environment variables in a .env file:

   LANGCLAW__PROVIDERS__OPENAI__API_KEY=sk-...
   LANGCLAW__CHANNELS__TELEGRAM__BOT_TOKEN=123456:ABC-DEF...
   

3. Run your agent (Choose one option):

   Option 1: Using the CLI (Recommended for new projects)

   langclaw init
   langclaw gateway
   

   Option 2: Programmatically (For custom setups) Create an app.py file:

   from langclaw import Langclaw
   
   app = Langclaw(
       system_prompt="You are a friendly assistant. Keep answers short and helpful."
   )
   
   @app.tool()
   async def reverse_text(text: str) -> str:
       """Reverse the given text. Useful for word games and puzzles."""
       return text[::-1]
   
   if __name__ == "__main__":
       app.run()
   

   Then run it:

   python app.py
   

Packages

Package	Purpose
app.py	Langclaw class — the developer's primary interface (decorators, lifecycle, wiring)
agents/	LangGraph agent construction, tool wiring, subagent delegation
gateway/	Channel orchestration (GatewayManager), command routing, message dispatch
bus/	Message bus abstraction — asyncio (dev), RabbitMQ / Kafka (prod)
middleware/	Request pipeline: RBAC, rate limit, content filter, PII redaction
config/	Pydantic Settings with LANGCLAW__ env prefix (nested __ delimiter)
cron/	Scheduled jobs via APScheduler v4
session/	Maps (channel, user, context) to LangGraph thread IDs
checkpointer/	Conversation state persistence — SQLite (dev), Postgres (prod)
providers/	LLM model resolution via init_chat_model
cli/	Typer CLI: langclaw init, langclaw gateway, langclaw agent, langclaw cron, langclaw status

Roadmap

Shipped

• Subagent delegation — app.subagent() registers child agents with isolated context and per-subagent model/tool sets
• Channel-routed subagents — subagents can publish results directly to the originating channel (output="channel")
• Guardrails middleware — ContentFilterMiddleware (keyword/regex) and PIIMiddleware (redaction) in the built-in stack
• Heartbeat / proactive wake-up — event-driven condition checks that fire messages through the agent pipeline

Planned

• Multi-agent routing — named agents with distinct models, routed by channel or user intent
• More channels — Slack, WhatsApp, REST API gateway
• Plugin ecosystem — langclaw-* tool packs installable via uv add
• Observability — OpenTelemetry tracing for the full message flow
• Test coverage — comprehensive tests across all modules

Contributing

git clone https://github.com/tisu19021997/langclaw.git
cd langclaw
uv sync --group dev
uv run pytest tests/ -v
uv run ruff check . && uv run ruff format .

License

MIT — see LICENSE for details.