nextlevelbuilder/goclaw

GoClaw - GoClaw is OpenClaw rebuilt in Go — with multi-tenant isolation, 5-layer security, and native concurrency. Deploy AI agent teams at scale without compromising on safety.

What it solves

GoClaw is a multi-tenant AI agent platform that provides a production-ready gateway for deploying and managing multiple AI agents. It solves the complexity of integrating various LLM providers, managing long-term memory across sessions, and orchestrating collaboration between different agents across multiple messaging channels like Telegram, Discord, and Slack.

How it works

Built in Go as a single binary, the platform uses a multi-tenant PostgreSQL database with pgvector for semantic search. It implements an 8-stage agent pipeline (context, history, prompt, think, act, observe, memory, summarize) and a 3-tier memory system (working, episodic, and semantic) to maintain context. Agents can be organized into teams with shared task boards and delegation capabilities. The system also features a "Knowledge Vault" for document registry and hybrid search.

Who it’s for

Developers and organizations needing a scalable, secure, multi-tenant infrastructure to deploy AI agents that can interact with users via common messaging apps, integrate with 20+ LLM providers, and maintain complex state and memory.

Highlights

  • Multi-Provider Support: Integrates with 20+ LLM providers including Anthropic, OpenAI, Groq, and DeepSeek.
  • Multi-Channel Deployment: Connects agents to Telegram, Discord, Slack, Zalo, Feishu, and WhatsApp.
  • 3-Tier Memory: Progressive loading of conversation, session summaries, and a knowledge graph.
  • Agent Orchestration: Supports sync, async, and bidirectional delegation between agents within teams.
  • Self-Evolution: A guardrailed pipeline that allows agents to refine their own communication style based on metrics.
  • Production-Ready: Includes RBAC, AES-256-GCM encryption, rate limiting, and prompt injection detection.
  • Lightweight: Distributed as a single static Go binary (~25MB) with a desktop "Lite" version using SQLite.