Octomind MCP Server

<div align="center"> <a href="https://octomind.run"> <img src="assets/logo.svg" width="560" alt="Octomind — open-source coding agent harness" /> </a> <h3>Open-source coding agent harness. One Rust binary, any model, every surface.</h3> <p>Interactive, stdin, daemon, WebSocket, or ACP — one binary, one config.<br />A supervising model plans and checks the work.<br />Policy lives in TOML guardrails, not approval prompts.</p>

Website · Docs · Tap Registry · Benchmarks · Issues

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Octomind is an open-source coding agent harness. It gives any LLM the tools to work in a repository — read and edit files, run shells, search code, delegate to sub-agents — through MCP, and runs the result as an interactive CLI, a stdin pipe, a daemon, a WebSocket server, or an ACP agent in your editor. Most agents stop and ask a human before each risky step, which works at a keyboard and blocks everything in CI. Octomind writes that policy down as TOML guardrails and adds a supervising model that plans and checks the work, so the same session can run with nobody watching. On 25 tasks taken from merged pull requests it solved 24; OpenCode solved 19 with the same model at about twice the cost (benchmarks).

How It Works

One diagram, the whole turn: what is assembled before the model sees the request, the tool loop, how a turn is allowed to end, and what carries over to the next session.

<picture> <source media="(prefers-color-scheme: dark)" srcset="assets/how-it-works-dark.svg"> <img src="assets/how-it-works-light.svg" width="100%" alt="Octomind turn flow: request, context assembly (AGENTS.md and skills, lesson recall, supervisor notes, compression), model, tool loop (guards, MCP tools, hooks, condense, detectors), verify gate, validators, response; lessons extracted after /done feed later sessions and, with evolution enabled, skill and guardrail candidates on trial"> </picture>

• What the model sees is built once per user message, not per tool round: AGENTS.md, the skills whose rules match the prompt (standard SKILL.md folders), the tool bundles those skills need (they start their own MCP servers on demand), and the few past lessons that match. Skills · Learning
• Compression folds older turns into a summary that cites content-addressed blocks; the raw messages stay in a per-session archive and the recall tool brings any block back verbatim as a normal tool result, so history is never rewritten and the prompt cache survives. Compression
• Guards, hooks, validators are deterministic and live in .agents/guardrails.toml plus scripts; they run before any model opinion. The optional intent check ([supervisor.authorizer], off by default) adds one supervisor-model look after the guards and blocks a call only when it independently conflicts with what you asked for. Guardrails
• The supervisor is a second model ([supervisor.model]): it keeps the plan and reminds the model of the goal each turn, cuts oversized tool results down to the lines the task needs (copied, never retyped), and refuses a self-reported "done" until the evidence checks out. The loop check is a fixed heuristic: when the model repeats itself or stalls it gets one nudge, never a takeover. Supervisor
• The decision model ([decision]) is optional: a classifier that answers typed yes/no questions with a probability instead of prose. When set, the supervisor sends its cheap questions there — which lessons to recall, which skill to activate, which lines to keep, whether a completion claim holds — and keeps chat calls for writing. Calibrated decisions
• Learning extracts lessons after /done or at exit, in your own words, into files under learning/<project>/<role>/; later sessions recall the ones that match. With [supervisor.learning.evolution] enabled, recurring lessons become skill and guardrail candidates that run in shadow first, then an A/B trial against a control arm, and go active only when they beat it. Learning

Why Octomind?

Built for the work that keeps going after you leave:

• Policy as code, not approval popups. Deterministic guardrails — pre-call guards, post-result hooks, post-turn validators — live in TOML and scripts, so they work in CI, daemons, and scheduled runs.
• Measured, not claimed. All four agents ran stock on the same tasks with the same judge. Per-case table, artifacts, and a reproduction guide are in the benchmarks.
• Ready-made specialists. octomind run developer pulls a packaged agent (prompt, model, MCP servers, tool permissions) from a tap. It starts developer:chief, the orchestrator of the developer domain (a domain is the part of a tag before the :; its orchestrator is the agent named chief). octomind run developer:general starts one exact agent, the generalist that does the work itself. Publish your own with one repo.
• Sessions that stay sharp. Adaptive compression keeps long tasks moving without forgetting the task or your last few messages.
• Learns the project as you work. Corrections and rules you state become lessons, quoted from your own words and scoped to the project and role, and come back in later sessions. Turn on evolution and recurring lessons compile into skill and guardrail candidates, trialled against a control and kept only when they help.
• Any model, any budget. Use Octomind Cloud or your own key (OpenRouter, Anthropic, OpenAI, DeepSeek, Ollama, and more), pick a model per role, and cap spend per request and per session. Over budget, a terminal session asks y/N; a non-interactive run stops with exit code 75.
• One config. Models, tools, roles, guardrails, budgets: all TOML, no framework code.

# Interactive
octomind run developer

# Non-interactive — CI, scripts, automation
echo "Explain the auth module" | octomind run developer --format plain

# Daemon — keep it alive, talk to it from another terminal
echo "watch the build" | octomind run --name watcher --daemon --format jsonl
octomind send --name watcher "run the test suite"

Quick Start

1. Install (macOS, Linux):

curl -fsSL https://raw.githubusercontent.com/muvon/octomind/master/install.sh | bash

On Windows, in PowerShell:

irm https://raw.githubusercontent.com/muvon/octomind/master/install.ps1 | iex

On macOS, you can also install with Homebrew:

brew install muvon/tap/octomind

2. Choose where models come from. Start octomind once in a terminal:

octomind

Before it writes a config, the first interactive run asks where to get models:

• Octomind hub (recommended): one browser sign-in, auto picks a model for each job, paid with credits you buy.
• Your own provider: OpenRouter, OpenAI, Anthropic, DeepSeek, Ollama, and every other provider octomind supports. Octomind asks for the key with hidden input, saves it to its own .env (readable only by you), and lists the provider's models to pick from. One model can serve the main agent, the supervisor, and compression, or you can pick each.
• Skip: keep the shipped defaults (octohub:auto).

Run octomind configure to change this later. Scripts, piped input, and --format runs never see the screen; they use the default config.

3. Run a specialist in your project:

cd ~/your/project
octomind run developer

You're now in a session with the developer domain's lead agent. It reads your code and runs commands itself, and hands code changes to the domain's specialists. The interface is a plain line prompt with markdown rendering — no TUI to learn, so it works over SSH, in tmux, and in CI logs. The first time you run a specialist, it may install its tools and ask for credentials.

developer:chief comes from the default tap muvon/tap, not your local config. Plain octomind run starts the config's default role, assistant:chief, the global assistant. A name without a :, such as octomind run developer, starts that domain's orchestrator (developer:chief), unless your config defines a role with that name; the local role wins.

Try the recorded bug-fix demo: two failing tests, an agent fix, and an independent test rerun.

Benchmarks

Measured on octobench against 25 tasks harvested from merged pull requests across Python, PHP, Rust, C++, and JavaScript. Each agent works in the pre-fix repo; held-out tests from the merged fix decide pass or fail. These are the published July 30, 2026 results, not a measurement of this checkout.

	solved	judge Σ / 2500	cost	wall time
octomind + glm-5.2	24/25	2264	$63.43	3.6h
claude code + claude-opus-5	23/25	2262	$81.79	6.7h
codex + gpt-5.6-sol	21/25	2127	$14.86	1.0h
opencode + glm-5.2	19/25	2093	$129.54	3.3h

• The harness matters. opencode used the same model and endpoint; octomind solved 24 vs 19 at roughly half the cost. All four agents ran stock invocations with no tuning, so this measures the whole harness, not individual features.
• Worst-case pricing, still ahead. glm-5.2 ran without prompt caching while Opus billed ~97% of context re-reads at cache rates — and octomind still led on solves, cost, and wall time.
• Reproducible. Per-case table, artifacts, and reproduction guide: BENCHMARK.md. Background: blog post.

Guardrails: Policy as Code

Drop a .agents/guardrails.toml in your repo. Octomind enforces it deterministically, with no code to compile and no plugin to install.

# Pre-call deny — block a class of calls before they execute
[[guard]]
match   = "shell(command=^rm\\s+-rf?)"
message = "rm -rf blocked."

# Conditional rule — only fires after the agent ran git status this session
[[guard]]
match   = "shell(command=git push)"
when    = ["+shell(command=git status)"]
message = "Review changes before pushing."

Layer	When it runs	What it does
Guards	Before a tool call	Deny by capability, argument regex, and session history. A match returns a denial instead of executing the call.
Hooks	After a tool result	A non-zero exit injects stdout into the agent's inbox — lint and format failures become automatic corrections without restarting the turn.
Validators	After a turn	Scripts the agent reads on its next turn. This replaces "approve this change?" prompts in autonomous loops.

If you know Claude Code hooks: guards are PreToolUse denies declared in TOML, hooks are PostToolUse, validators are Stop — no scripts required for the common cases.

Hook and validator script examples: Guardrails guide.

One Harness, Five Surfaces

The same harness, exposed wherever your workflow needs it.

Surface	Use it for	Docs
Interactive CLI	Daily work, any domain	Sessions
Non-interactive (--format plain / jsonl)	CI, scripts, scheduled jobs	CLI reference
Daemon + send	Background agents, continuous monitoring	Daemon & hooks
WebSocket (octomind server)	IDE plugins, dashboards, integrations	WebSocket server
ACP (octomind acp)	Agent inside Zed, JetBrains, VS Code, Neovim, or any ACP editor	ACP protocol

Need machine-readable results? Pass --schema schema.json to constrain the final response to a JSON Schema (requires a model with structured-output support). See Structured output.

Features

Feature	What you get	Learn more
Specialists & taps	Packaged agents with model, prompt, MCP servers, and permissions; installs their tool dependencies on first use	Tap system
Roles	Per-role model, temperature, system prompt, tools	Roles
Workflows	Multi-step pipelines, each step with its own model and toolset	Workflows
MCP client	stdio and Streamable HTTP servers, with OAuth; enable/disable servers mid-session	MCP tools
Agent Skills	Standard SKILL.md skills from .agents/skills/, ~/.config/agents/skills/, and taps; they activate on demand, so your context stays small	Skills
Local project tools	Executable scripts in .agents/tools/ auto-register as tools	Local tools
Plugins & AGENTS.md	Agent Plugins (plugin.json + skills + mcp.json) from taps, .agents/plugins/, or ~/.config/agents/plugins/; project AGENTS.md loads at session start	Configuration
Supervisor & learning	A second model plans, checks completion, and condenses tool output; lessons persist as files per project and role	Supervisor, Learning
Sandbox	--sandbox locks writes to the working directory via Landlock (Linux) or Seatbelt (macOS), inherited by every shell, MCP server, and sub-agent	Config reference
Token efficiency	Cache-aware cost accounting and adaptive compression	Token efficiency

Specialists

octomind run developer            # developer domain: its lead agent hands larger work to the specialists
octomind run doctor               # doctor domain: health questions, routed to the right specialist
octomind run lawyer               # lawyer domain: legal questions, by jurisdiction
octomind run doctor:blood         # one exact specialist: blood-test interpretation

Running a specialist fetches its manifest from the tap registry, installs any required binaries, resolves credentials, starts the right MCP servers, and then opens the session.

Build and register your own:

octomind tap init yourteam/my-tap   # scaffold, validate, git-init, and register locally
octomind tap yourteam/my-tap        # on another machine, after publishing to GitHub

The octomind- prefix is added automatically: yourteam/my-tap maps to the repository yourteam/octomind-my-tap.

Installation

Method	Command
Install script (recommended)	curl -fsSL https://raw.githubusercontent.com/muvon/octomind/master/install.sh | bash
Install script (Windows PowerShell)	irm https://raw.githubusercontent.com/muvon/octomind/master/install.ps1 | iex
Homebrew (macOS)	brew install muvon/tap/octomind
Scoop (Windows)	scoop bucket add muvon https://github.com/Muvon/scoop-bucket then scoop install muvon/octomind
Cargo (Rust 1.95+)	cargo install octomind
Docker	docker run --rm ghcr.io/muvon/octomind --help
From source	git clone https://github.com/muvon/octomind.git && cd octomind && cargo build --release

The install script detects your OS and architecture (Linux and macOS on x86_64/aarch64) and installs to ~/.local/bin/. Pass --install-dir <DIR> or set OCTOMIND_INSTALL_DIR to override. On Windows (x86_64/aarch64), install.ps1 installs to %LOCALAPPDATA%\octomind\bin and adds it to your user PATH; it honors OCTOMIND_INSTALL_DIR and OCTOMIND_VERSION too. install.sh still works from Git Bash or MSYS2.

Command not found? Add the install directory to your PATH:

export PATH="$HOME/.local/bin:$PATH"

Building from source requires protoc; see Building from source for per-OS prerequisites.

Verify:

octomind --version
octomind config      # generate the default config
octomind run         # start your first session

Shell completions: octomind completion <bash|zsh|fish|elvish|powershell>.

Model access

Run octomind configure any time to switch between the hub and your own provider. It changes only the three model names (main, supervisor, compression) and keeps the rest of your config and its comments.

Octomind Cloud — choose the hub in octomind configure, or run octomind login (device-code sign-in, like gh auth login). Either stores a gateway key locally. The default config already uses octohub:auto. Learn more.

Bring your own key — choose your provider in octomind configure. To set it up by hand instead, set the provider's environment variable (OPENROUTER_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, DEEPSEEK_API_KEY, …) and point the model tables at it. The supervisor and compression profiles also default to octohub:auto, so update all three:

[model]
name = "anthropic:claude-sonnet-5-5"

[supervisor.model]
name = "anthropic:claude-haiku-5-5"

[compression.model]
name = "anthropic:claude-haiku-5-5"

Provider details and per-role overrides: Providers & models.

Configuration

Config lives at ~/.local/share/octomind/config/config.toml on macOS and Linux, and under %LOCALAPPDATA%/octomind/config/ on Windows. Set OCTOMIND_DATA_DIR to override the data root.

octomind config --show       # view current config
octomind config --validate   # validate config

Override any value for a single run with -c:

octomind run -c supervisor.enabled=false

Full reference: Configuration reference · Environment variables.

Session commands

Command	Description
/help	Show all commands
/info	Token usage and costs
/model anthropic:claude-sonnet-5-5	Switch model mid-session
/role developer:general	Switch to a configured role or tap tag
/new Review auth	Start a fresh session (title optional)
/done	Force context compression and start background learning extraction when enabled
/exit	Exit the session

Full list: Session commands.

Telemetry

Octomind reports anonymous usage: which commands, tools, and models are used, plus timings, token counts, and error kinds. It never sends your code, prompts, file paths, tool arguments, or environment values. Opt out in any of three ways:

export DO_NOT_TRACK=1          # the cross-tool standard, honored first
export OCTOMIND_TELEMETRY=0    # for subsequent commands in this shell
# or set `telemetry = false` in config.toml

Exact field list: Telemetry.

Documentation

Start here	Go deeper	Integrate	Reference
Installation	Roles	WebSocket server	CLI
Quickstart	Workflows	ACP protocol	Config
Configuration	Compression	Daemon & hooks	Session commands
Providers & models	Guardrails	Editor integration	Environment variables

Full index: doc/README.md · Hosted: octomind.run/docs.

Contributing

The most impactful contribution isn't code — it's specialist agents. A cardiologist publishing doctor:medications. A tax attorney publishing lawyer:us. A security researcher publishing security:owasp. One TOML file, and everyone with that problem gets an agent that knows the domain.

• How to write a tap agent
• Open issues

Code contributions are welcome too. Set up the hooks and run the gate before you push:

pip install pre-commit && pre-commit install
make dev    # fmt + clippy + test

Commits follow Conventional Commits. Full guide, architecture overview, and code conventions: CONTRIBUTING.md · internals: Architecture.

Community participation follows our Code of Conduct. Report vulnerabilities privately using the security policy.

License

Apache License 2.0 — see LICENSE.

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<div align="center"> <strong>Octomind</strong> by <a href="https://muvon.io">Muvon</a> · <a href="https://octomind.run">Website</a> · <a href="https://octomind.run/docs/">Documentation</a> </div>