Cadenya
Paid
Developers building agentic workflows
OpenAgents
FreeOpen SourceAPISelf-HostedAgentic
The demo of multiple agents working in one shared workspace looks clean. The question in production is whether you're building a coordination problem or just adding complexity to what could be a simpler agent workflow.
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Get This Tool
License: Apache-2.0 Any use incl. commercial
Local-run terms: Users can freely modify, distribute, and use OpenAgents for commercial purposes under Apache 2.0; must retain license notices and provide documentation of changes.
Screenshots 1
Pricing
- Model
- Free
- Free Tier
- Fully open-source; no limits on self-hosted deployment; hosted workspace available free
Summary
OpenAgents positions itself as the coordination backbone for distributed AI agents. You get a hosted workspace (or self-host) where agents working on separate machines discover each other, share files and browser context, and coordinate via @mentions. Installation is one-liner: install the Launcher desktop app, point agents at a workspace token, and they join. The platform is open-source with an active but modest community. The technical surface is clean—agents register on the network, events flow between them, and context stays shared. The hard part surfaces later: when your agents are actually doing different things (some coding, some reviewing, some managing), orchestrating handoffs stays manual. This is SDK-first, not no-code. If you're building a research team of specialized agents or debugging scenarios where you need human eyes on agent reasoning in real time, the shared workspace genuinely reduces context switching. If you're running a single coding agent that sometimes needs to call another agent, you might be over-engineering it.
Bottom line: Pick this if you're building multi-agent teams where agents need visibility into each other's work and real-time coordination. When your agents are just piping outputs to each other without negotiating, the shared workspace overhead doesn't pay for itself.
Find your fit
Before you choose OpenAgents
More options in Agent Frameworks. Compare pricing, platforms, and features side by side.
Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Agents discover and coordinate without prior wiring—@mention a peer, it sees the message, broadcasts its capabilities back in the same channel.
- Shared browser and files mean no copy-pasting context between terminal windows—all agents see the same pages and uploaded artifacts in real time.
- Persistent workspace URL survives restarts—agents reconnect automatically, no re-registration or token rotation required.
- Supports multiple agent runtimes (Claude Code, OpenClaw, Aider, Codex) from one interface, so you're not locked to one vendor's agent.
- Open-source and self-hostable with Docker, so your agent conversations and code stay in your infrastructure.
Cons
- Event-driven coordination is powerful but hands-off orchestration is missing—you still write custom SDK code to route tasks between agents; no visual workflow builder.
- Python-first SDK means connecting non-Python agents (JavaScript, Go) requires wrapper agents or custom translation layers.
- Production documentation is sparse—few runbooks for scaling beyond a small team or handling agent failures in long-running workflows.
- Shared browser context is chat-based, not code-based—agents screenshot and discuss pages rather than querying shared state programmatically, so coordination on changing data requires explicit polling.
About
- Platforms
- Windows, macOS, Linux (desktop app); Web browser (Workspace)
- Languages
- Python (SDK)JavaScript/TypeScript (Frontend)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- May 8, 2026
Best For
Who it's for
- Research teams building multi-agent prototypes where visibility into each agent's reasoning is more important than production polish.
- Teams wanting to coordinate Claude Code and open-source agents (OpenClaw, Aider) in one workspace without building custom connectors.
- Self-hosted setups where agents stay on your infrastructure and can't call external APIs for coordination.
- Projects where the value is in human review of agent work rather than fully autonomous multi-step workflows.
What it does well
- Debugging multi-agent systems in real time—watch agents collaborate on code reviews, security audits, or data analysis while you monitor from the shared workspace.
- Research paper analysis pipeline where one agent gathers papers, another analyzes methodology, a third summarizes findings—all visible in shared threads.
- Internal dev team support where a junior-code agent handles simple fixes, a senior-review agent audits changes, and a human lead monitors both in one window.
- Cross-functional team simulation for product development, where agents play PM, engineer, and QA roles and negotiate over shared design docs.
Integrations
Compare OpenAgents
Frequently Asked Questions
- Is OpenAgents free?
- OpenAgents offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is OpenAgents open source?
- Yes. OpenAgents is open source — the source repository is at https://github.com/openagents-org/openagents.
- Does OpenAgents have an API?
- Yes. OpenAgents exposes a developer API. See the official documentation at https://openagents.org for details.
- Can I self-host OpenAgents?
- Yes. OpenAgents supports self-hosting on your own infrastructure.
- When was OpenAgents released?
- OpenAgents was first released in 2024.
- What platforms does OpenAgents support?
- OpenAgents is available on: Windows, macOS, Linux (desktop app); Web browser (Workspace).
People also compared
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The demo of multiple agents working in one shared workspace looks clean. The question in production is whether you're building a coordination problem or just adding complexity to what could be a simpler agent workflow.
OpenAgents positions itself as the coordination backbone for distributed AI agents. You get a hosted workspace (or self-host) where agents working on separate machines discover each other, share files and browser context, and coordinate via @mentions. Installation is one-liner: install the Launcher desktop app, point agents at a workspace token, and they join. The platform is open-source with an active but modest community. The technical surface is clean—agents register on the network, events flow between them, and context stays shared. The hard part surfaces later: when your agents are actually doing different things (some coding, some reviewing, some managing), orchestrating handoffs stays manual. This is SDK-first, not no-code. If you're building a research team of specialized agents or debugging scenarios where you need human eyes on agent reasoning in real time, the shared workspace genuinely reduces context switching. If you're running a single coding agent that sometimes needs to call another agent, you might be over-engineering it.
Pick this if you're building multi-agent teams where agents need visibility into each other's work and real-time coordination. When your agents are just piping outputs to each other without negotiating, the shared workspace overhead doesn't pay for itself.
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