Best Open Source Multi-Agent Systems

oh-my-claudecode turns Claude Code into a multi-agent team coordinator. You describe a job in plain language and it dispatches specialized agents that plan, build, review, and test in parallel, passing work between themselves without you driving each step. MIT-licensed, installs through the Claude Code marketplace or as an npm CLI. No server to run. It lives inside your Claude Code session and extends the built-in agent tooling. The canonical mode is now explicit team orchestration via /team syntax, which replaced the older swarm keyword. It also routes compatible work across other CLIs you have installed (Codex, Gemini, Grok, Cursor) to save Anthropic tokens, and extracts reusable skills as it goes. Solo developers get a structured way to run long, multi-step tasks without micromanaging each agent. Small teams get a shared pattern for agent pipelines instead of everyone rolling their own. There is no paid tier and no cloud version. You bring your own Claude Max, Pro, or API key. The catch: it moves extremely fast, and that speed shows. Releases land constantly and keywords get deprecated mid-stream (swarm and autoresearch already went), so a setup that worked last month may need adjusting. Multi-agent pipelines also burn tokens faster than a single prompt, and if you don't trust the autopilot, you will review more code than you write.

Completely free and open source. Optional Gemini/Codex integrations save tokens.

qm is Y Combinator's own open source answer to running a fleet of AI agents across a team. It's a "multiplayer agent harness for work": every person gets an isolated workspace with personal and shared scopes, you drive it from Slack or a web UI, and it can run background and scheduled agent jobs. This is YC Labs code, not a fan project trading on the name, the release is announced on ycombinator.com and served from qm.ycombinator.com. The interesting part is the multiplayer framing. Instead of each developer babysitting one agent in a terminal, qm treats agents as shared, always-on coworkers with their own scopes and cron schedules. For a team already leaning on coding agents, it's a free way to run them coordinated instead of siloed. Solo use works too, but the design clearly targets groups. The catch is that it's brand new. The commit history is short and the stars arrived fast, which means the code is early and production-readiness is unproven. The pedigree is real and the idea is sharp, but pilot it on non-critical work before you wire it into how your team actually ships.

Free and open source, straight from Y Combinator. Brand new, so treat it as early.

Omnigent puts one layer over all your AI coding agents so you can swap between them without rewriting anything. Claude Code, Codex, Pi, your own custom agents, it runs them through a single harness with shared policy controls, so the agent choice becomes a config detail instead of a rewrite. The standout feature is a security sandbox that hides your credentials and locks down filesystem and network access while an agent runs. Setup is light for what it does: install the CLI through uv or Homebrew, and you get terminal and web UIs plus runners that execute locally or on Modal or Daytona. You bring your own model credentials. The governance and sandboxing are the real reason to look, they address the thing that makes people nervous about letting agents run loose on their machine. Developers juggling multiple coding agents, or anyone uneasy about an agent seeing their secrets, are the target. If you've settled on one agent and trust it, this is overhead you don't need. The value scales with how much you care about isolation and the ability to switch tools without lock-in. The catch: it's alpha. The README says so, and that means rough edges, breaking changes, and features still landing. It's free and Apache-licensed, but you're adopting an early project to wrap other early projects. Promising, but not something to build a team's critical workflow on yet.

Free and open source (Apache-2.0), still alpha. You pay only for the models you run.

Multica manages a fleet of AI coding agents like they're people you hired. Instead of babysitting one agent in your terminal, you assign issues to agents, group them into squads under a leader, watch their progress live, and schedule recurring jobs. It's runtime-agnostic, so it drives Claude Code, Codex, Copilot CLI, Cursor Agent, Gemini, and others rather than locking you to one. You can self-host it, and for internal use across your own team that's free. The stack is a Go backend, a Next.js frontend, and Postgres with pgvector, so standing it up yourself is real ops work, not a one-click install. Recent versions added the headline pieces: squads for routing work to a stable group of agents, a catalog of agent templates you can spin up in one click, and autopilots for scheduled recurring tasks. It's iterating fast, with near-daily releases. For a solo developer or a small team that wants to run several coding agents in parallel with real visibility into what each one is doing, this is a genuinely useful control layer, and the self-hosted version is free for your own organization. The thing to read carefully is the license. It's a modified Apache 2.0: you can use it internally all you want, but you can't offer Multica as a hosted service to other people or strip its logo without a commercial license, and there's a managed cloud at multica.ai for teams that don't want to self-host. The catch is that this is source-available, not classic open source, and it's young. If you're a developer running it for your own team, none of the restrictions touch you and it's free. If you're thinking about building a product on top of it or reselling it, that's exactly what the commercial clause is there to stop. Know which side of that line you're on before you commit.

Free to self-host for your own team. The modified-Apache license blocks reselling it or offering it as a hosted service without a commercial deal.

AgentSpace is a shared workspace where humans and AI agents work as one team, instead of you driving a single agent in isolation. The idea: give agents defined roles, permissions, and an audit trail, then let them coordinate in channels the way a human team would. Most agent frameworks assume one person and one agent; AgentSpace is built for a group running several agents with governance around them. Apache 2.0 licensed. The interesting piece is AgentRouter, which routes tasks across different agent runtimes, Claude Code, Codex, OpenCode, and others, through one normalized interface, so you are not locked to a single backend. There is a digital employee board that makes agents visible and shareable across an org, a permission and approval system that puts a human checkpoint in front of sensitive actions, and full logging of everything an agent does. You can self-host it or use the hosted platform. This is early, and it is aimed at founder teams and small orgs that want to put agents to work without losing track of what they are doing. The audit trail and approval gates are the real draw; running multiple agents with no oversight gets dangerous fast. Self-host it for full control of the infrastructure, or take the hosted option if you would rather not run it yourself. The catch is maturity. This is a young project moving fast, and multi-agent coordination is still proving itself across the whole industry, not just here. The governance framing is genuinely useful, but expect rough edges, and verify the hosted tier's pricing before you depend on it, since that is not clearly published yet.

Free to self-host under Apache 2.0. A hosted platform exists for teams that don't want the ops, but its pricing isn't clearly published yet, so verify before you rely on it.

Claude-peers-mcp is an MCP server that lets multiple Claude Code instances message each other in real time. If you run parallel Claude sessions (one on the frontend, one on the backend, one writing tests), this creates a direct communication channel between them so they can coordinate without you copy-pasting context between terminals. The way it works: you spin up the MCP server, connect each Claude Code instance to it, and they can send and receive messages from each other ad-hoc, like a group chat between your AI assistants. One session can ask another about an API contract it just wrote, or flag a dependency change that affects the other's work. It removes you as the bottleneck in multi-agent workflows. The catch: very new and tightly coupled to Claude Code's MCP ecosystem. It does not work with other AI agents or coding assistants. Coordinating AI sessions is still experimental territory, so expect rough edges and limited documentation. If you only run one Claude session at a time, you do not need this.

Free. Requires Claude Code (which has its own subscription).

MiroFish-Offline runs multi-agent AI simulations and predictions completely offline using a Neo4j knowledge graph and Ollama for local LLM inference. It's a simulation engine where multiple AI agents interact, predict outcomes, and build up a knowledge base over time. This is niche but powerful for scenarios like market simulation, scenario planning, or research where you can't send data to external APIs. Everything runs locally: the database, the models, the agents. The catch: AGPL-3.0 license (if you modify it and offer it as a service, you must open source your changes). Requires Neo4j and Ollama running locally. That's a real setup commitment. And 'offline multi-agent simulation' is a small but growing niche.

Free for internal use. AGPL means service use requires open-sourcing your modifications.