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What Is GoClaw

A multi-tenant AI agent gateway that connects LLMs to messaging channels, tools, and teams.

Overview

GoClaw is an open-source AI agent gateway written in Go. It lets you run AI agents that can chat on Telegram, Discord, WhatsApp, and other channels — while sharing tools, memory, and context across a team. Think of it as the bridge between your LLM providers and the real world.

Key Features

Category What You Get
Multi-Tenant Per-user isolation for context, sessions, memory, and traces
22 Provider Types OpenAI, Anthropic, Google, Groq, DeepSeek, Mistral, xAI, and more (15 LLM APIs + local models + CLI agents + media)
7 Channels Telegram, Discord, WhatsApp, Zalo, Zalo Personal, Larksuite, Slack
32 Built-in Tools File system, web search, browser, code execution, memory, and more
64+ WebSocket RPC Methods Real-time control — chat, agent management, traces, and more via /ws
Agent Orchestration 4 patterns — delegation (sync/async), teams, handoff, evaluate loops
Knowledge Graph LLM-powered entity/relationship extraction with graph traversal
MCP Support Connect to Model Context Protocol servers (stdio/SSE/HTTP)
Skills System SKILL.md-based knowledge base with hybrid search (BM25 + vector)
Quality Gates Hook-based output validation with configurable feedback loops
Extended Thinking Per-provider reasoning modes (Anthropic, OpenAI, DashScope)
Prompt Caching Up to ~90% cost reduction on repeated prefixes
Web Dashboard Visual management for agents, providers, channels, and traces
Memory Long-term memory with hybrid search (vector + full-text)
Security Rate limiting, SSRF protection, credential scrubbing, RBAC
Single Binary ~25 MB, <1s startup, runs on a $5 VPS

Who Is It For?

  • Developers building AI-powered chatbots and assistants
  • Teams that need shared AI agents with role-based access
  • Enterprises requiring multi-tenant isolation and audit trails

Operating Mode

GoClaw requires a PostgreSQL backend with encrypted credentials, multi-user support (each user gets their own isolated workspace), and persistent memory. This gives you full isolation between users, complete activity logs, and smart search across all conversations.

How It Works

graph LR
    U[User] --> C[Channel<br/>Telegram / Discord / WS]
    C --> G[GoClaw Gateway]
    G --> A[Agent Loop<br/>Think → Act → Observe]
    A --> P[LLM Provider<br/>OpenAI / Anthropic / ...]
    A --> T[Tools<br/>Search / Code / Memory / ...]
    A --> D[Database<br/>Sessions / Context / Traces]
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  1. A user sends a message through a channel (Telegram, WebSocket, etc.)
  2. The gateway routes it to the right agent based on channel bindings
  3. The agent loop sends the conversation to an LLM provider
  4. The LLM may call tools (search the web, run code, query memory, search knowledge graph)
  5. The agent can delegate tasks to other agents, hand off conversations, or run evaluate loops for quality-gated output
  6. The response flows back through the channel to the user

What's Next