A multi-tenant AI agent gateway that connects LLMs to messaging channels, tools, and teams.
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.
| Category | What You Get |
|---|---|
| Multi-Tenant v3 | Per-user isolation for context, sessions, memory, and traces; per-edition rate limits |
| 8-Stage Agent Pipeline | context → history → prompt → think → act → observe → memory → summarize (v3, always-on) |
| 22 Provider Types | OpenAI, Anthropic, Google, Groq, DeepSeek, Mistral, xAI, and more (15 LLM APIs + local models + ACP CLI agents + media) |
| ACP Provider | Agentic Claude Protocol — runs Claude Code, Codex, Gemini CLI as agents via JSON-RPC 2.0 stdio subprocess |
| Hooks System | 7 lifecycle events (SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, SubagentStart/Stop) — sync/async, SSRF-hardened HTTP handlers, audit logging |
| Audio / TTS Manager | Unified audio manager with 4 TTS providers: ElevenLabs (streaming), OpenAI, Edge TTS, MiniMax; voice LRU cache (1 000 tenants, 1 h TTL) |
| Messaging Channels | Telegram, Discord, WhatsApp (native), Zalo, Zalo Personal, Larksuite, Slack, WebSocket |
| 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 | Delegation (sync/async), teams, handoff, evaluate loops, WaitAll via BatchQueue[T] |
| 3-Tier Memory | L0/L1/L2 with consolidation workers (episodic, semantic, dreaming, dedup) |
| Knowledge Vault | Wikilink document mesh, LLM auto-summary + semantic auto-linking, hybrid BM25 + vector search |
| Knowledge Graph | LLM-powered entity/relationship extraction with graph traversal |
| Agent Evolution | Guardrails + suggestion engine; predefined agents refine SOUL.md / CAPABILITIES.md and grow skills |
| Mode Prompt System | Switchable prompt modes (full / task / minimal / none) with per-agent overrides |
| MCP Support | Connect to Model Context Protocol servers (stdio/SSE/HTTP) |
| Skills System | SKILL.md-based knowledge base with hybrid search; publishing, grants, evolution-driven drafts |
| 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; v3 cache-boundary markers |
| Web Dashboard | Visual management for agents, providers, channels, vault, traces |
| Security | Rate limiting, SSRF protection, credential scrubbing, RBAC, session IDOR hardening |
| Dual-DB | PostgreSQL (full) or SQLite desktop variant via unified store Dialect |
| Single Binary | ~25 MB, <1s startup, runs on a $5 VPS |
- 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
GoClaw runs on PostgreSQL (full multi-tenant production) or SQLite (single-user desktop). Both paths support encrypted credentials, per-user isolated workspaces, and persistent memory — giving you full isolation, complete activity logs, and smart search across all conversations. SQLite omits pgvector-only features (vault semantic auto-linking falls back to lexical).
graph LR
U[User] --> C[Channel<br/>Telegram / Discord / WS]
C --> G[GoClaw Gateway]
G --> PL[8-Stage Pipeline<br/>context → history → prompt →<br/>think → act → observe → memory → summarize]
PL --> P[LLM Provider<br/>OpenAI / Anthropic / ...]
PL --> T[Tools<br/>Search / Code / Memory / Vault / ...]
PL --> D[Database<br/>Sessions / Memory / Vault / Traces]
- A user sends a message through a channel (Telegram, WebSocket, etc.)
- The gateway routes it to the right agent based on channel bindings
- The 8-stage pipeline runs: it assembles context, pulls history, builds the prompt, thinks (LLM call), acts (tool calls), observes results, updates memory, and summarizes
- Tools can search the web, run code, query memory, knowledge graph, or knowledge vault
- The agent can delegate tasks to subagents (with
BatchQueue[T]for parallel waits), hand off conversations, or run evaluate loops for quality-gated output - Background consolidation workers promote episodic facts into semantic memory; the vault enrich worker auto-summarizes and semantically links new documents
- The response flows back through the channel to the user
- Installation — Get GoClaw running on your machine
- Quick Start — Your first agent in 5 minutes
- How GoClaw Works — Deep dive into the architecture