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SwarmClaw

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Open-source self-hosted AI agent runtime and multi-agent framework for autonomous agent swarms. Agent memory, MCP tools, schedules, delegation, and 23+ LLM providers (Claude, GPT,

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OSS

SwarmClaw

Added 1 June 2026

#agent-framework #agent-memory #agent-runtime #agent-swarm #agents #ai #ai-agent-framework #ai-agents

Overview

SwarmClaw is an open-source, self-hosted runtime and framework for building and running autonomous multi-agent swarms. It provides agent memory, MCP tools, scheduling, delegation, and supports 23+ LLM providers including Claude, GPT, Gemini, OpenRouter, and Ollama.

Best for

Best for
Developers who need a self-hosted, multi-agent framework with broad LLM support and want to avoid vendor lock-in.

Use cases

  • Deploying autonomous agent swarms for complex task orchestration
  • Building multi-agent systems with memory and tool integration
  • Replacing Claude Code or LangChain in self-hosted environments

Notes

SwarmClaw is an open-source, self-hosted runtime and framework for building and running autonomous multi-agent swarms. It provides agent memory, MCP tools, scheduling, delegation, and supports 23+ LLM providers including Claude, GPT, Gemini, OpenRouter, and Ollama.

539 stars on GitHub. Last updated 2026-05-26. Licensed MIT.

Use cases

  • Deploying autonomous agent swarms for complex task orchestration
  • Building multi-agent systems with memory and tool integration
  • Replacing Claude Code or LangChain in self-hosted environments

Pros

  • Fully self-hosted, giving full control over data and infrastructure
  • Broad LLM provider support reduces vendor lock-in
  • Includes built-in agent memory, scheduling, and delegation

Cons

  • Small community (539 stars) means less support and fewer integrations
  • Self-hosting requires operational overhead and infrastructure management
  • Documentation and ecosystem maturity may lag behind larger frameworks

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Fully self-hosted, giving full control over data and infrastructure
  • Broad LLM provider support reduces vendor lock-in
  • Includes built-in agent memory, scheduling, and delegation

Cons

  • Small community (539 stars) means less support and fewer integrations
  • Self-hosting requires operational overhead and infrastructure management
  • Documentation and ecosystem maturity may lag behind larger frameworks