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Bothread
Pricing
- Model
- Free
Summary
Two AI coding agents editing the same file at the same time is a silent disaster — no warning, no merge conflict, just one agent's work erasing the other's. Bothread exists to stop that from happening.
Bothread runs locally on your machine and coordinates MCP-compatible agents — Claude Code, Cursor, Gemini CLI, Codex, and others — on a single codebase through a shared room where agents claim files before editing them. You watch the live thread of agent activity and can pause, redirect, mute, or remove any agent mid-task. An optional approval gate lets you sign off before any action flagged as risky proceeds. The coordination layer calls no models itself and requires no API keys — it only manages the agents you are already running. The ceiling appears when your project requires agent tooling that does not speak MCP: any agent outside that protocol sits outside Bothread's coordination entirely.
Bottom line: Bothread is the right call when you are running two or more MCP-compatible agents on one codebase and want local-only coordination with no cloud dependency — it breaks down the moment any agent in your stack does not support MCP.
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Pros
Sign in to edit- File-claiming before any edit happens, so two agents cannot overwrite each other's work on the same file — the class of silent conflict that makes multi-agent setups unreliable on a shared codebase is eliminated by design.
- Optional approval gate for risky actions, which means you stay in the loop on consequential changes without having to watch every line the agents produce.
- No API keys and no cloud calls — Bothread coordinates agents you already run, so adding it does not change your cost structure or introduce a new data-transmission surface.
- Fully self-hosted on 127.0.0.1 with MIT source available, so teams with strict data-residency requirements can audit the coordination layer and verify nothing leaves the machine.
- Live thread visibility with per-agent pause, redirect, mute, and remove controls, which means you can intervene on one agent mid-task without stopping the rest of the room.
Cons
Sign in to edit- Any agent that does not speak MCP cannot join a Bothread room — it operates outside the file-claiming and conflict-prevention system entirely. Teams whose preferred agent lacks MCP support face a hard choice: swap the agent for an MCP-compatible alternative or accept that coordination is incomplete and one agent can still overwrite another's work.
- Bothread has no API surface of its own, so there is no programmatic way to integrate the coordination layer into a CI pipeline, a deployment script, or any automated workflow that sits outside the agents themselves. Teams that want coordination events surfaced in external tooling have no supported path to do that.
- The tool coordinates agents but does not task them — it has no built-in mechanism for splitting a feature into subtasks and routing each to the right agent automatically. Teams that need that kind of work decomposition will reach for a separate orchestration layer, at which point they are maintaining two systems.
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About
- Platforms
- Local machine (127.0.0.1)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-20T20:18:10.951Z
Best For
Who it's for
- Developers using multiple AI coding agents simultaneously
- Users requiring local-only operation and full control
- Teams or individuals managing agent workflows on one project
- MCP-compatible agent setups needing coordination
What it does well
- Multi-agent collaboration on a single codebase
- Coordinated file editing with conflict prevention
- Live agent communication and oversight during coding tasks
- Local orchestration of existing MCP agents without cloud services
Integrations
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Frequently Asked Questions
- Is Bothread free?
- Yes — Bothread is fully free to use. There is no paid tier.
- Is Bothread open source?
- Yes. Bothread is open source.
- Can I self-host Bothread?
- Yes. Bothread supports self-hosting on your own infrastructure.
- What platforms does Bothread support?
- Bothread is available on: Local machine (127.0.0.1).
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Curated lists that include this category
Running more than one AI coding agent on the same project without coordination is asking for overwritten files and invisible conflicts. Bothread addresses this by giving MCP-compatible agents — Claude Code, Cursor, Antigravity, Gemini CLI, Codex — a shared room on your local machine where they negotiate file access before any edit happens. An agent claims a file, does its work, and releases it; no other agent touches that file in the meantime. You watch every action in a live thread and hold controls to pause, redirect, mute, or remove any agent without stopping the others.
The differentiating detail is what Bothread does not do: it calls no models, stores nothing in the cloud, and requires no API keys or account. It runs on 127.0.0.1 and coordinates agents you already have configured. This means the tool adds zero marginal cost and zero new data exposure — the security and cost profile of your existing agents does not change. For teams with strict data-residency requirements or individuals who simply do not want another cloud dependency, that architecture is the whole pitch.
Bothread fits best when your agent stack is already MCP-native and you need a lightweight coordination layer to prevent the race conditions that appear once two agents are working the same project simultaneously. It breaks down when any agent you depend on does not support MCP — that agent cannot join the shared room and sits outside the conflict-prevention system entirely. At that point, teams either replace the non-MCP agent with a compatible alternative or accept that coordination is only partial. There is no plugin path or adapter layer described in the docs for bridging non-MCP tools.
Setup follows the MCP server pattern: add the Bothread server to each agent’s configuration, and the agent gains the ability to join a room. The project is MIT-licensed and hosted on GitHub, so the coordination logic is auditable. The getting-started page is at bothread.vercel.app/start.