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Mitos

Mitos MCP server records architectural decisions in Markdown and exposes graph-backed precedent search to AI agents.

Install Mitos

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Overview

Mitos MCP server gives AI assistants a per-project memory of architectural decisions, rejected alternatives, and later superseding or amending decisions. It stores human-readable Markdown as the source of truth and derives a typed SQLite graph plus local Qdrant search index for agents. MCP tools cover precedent lookup, decision recording, and semantic or exact-handle queries, with CLI equivalents. Reach for it when repeated AI-assisted design work makes past reasoning difficult to find or keep consistent.

Use cases

  • Surface prior architectural decisions before choosing an approach
  • Record rejected alternatives and the reasoning behind a design
  • Query decisions by meaning, handle, or rejected option
  • Audit a project’s decision corpus for contradictions

Key features

  • Per-project decision graphs
  • Markdown source of truth with archived records
  • Semantic recall through local Qdrant
  • Gemini-backed embeddings
  • Decision supersession and amendment relationships
  • CLI counterparts for MCP operations

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

6/6 checks healthy over the last 34d

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars4Stargazers on the source repository.
Last commit9d agoMost recent push to the default branch.

How Mitos works

What Mitos does

Mitos MCP server maintains an architectural decision record for each project. A decision includes the chosen approach, alternatives that were rejected, and relationships to earlier decisions, such as superseding or amending them. This preserves the reasoning behind a design instead of treating each new assistant conversation as an isolated interaction.

The project’s Markdown files remain the authoritative record. Decisions are written to decisions.md, while committed entries can be rotated into quarterly files under decisions/archive/. These files are readable and searchable with ordinary text tools. The graph and search data are derived from the Markdown and can be rebuilt.

This places Mitos in the Knowledge & Memory category for workflows where AI coding agents need to check precedent before proposing or committing an architectural change.

How it works

Mitos MCP server runs per project. Each project has its own decision graph and Qdrant collections, while one machine-level installation and MCP registration can serve multiple registered projects. Commands and agent calls identify the target with a project name, an absolute path, or . from the project root; there is no implicit default project.

The main workflow has three operations:

  • surface_decisions finds relevant prior decisions before a new choice is made and includes previously rejected alternatives.
  • record_decision stores a settled choice, its rejected paths, and typed relationships to existing records.
  • query_decisions searches by meaning, exact handle, or rejected option.

Semantic recall uses a local Qdrant instance and Gemini embeddings. If the index or embedding service is unavailable, recording continues and searches fall back to text matching over the Markdown, with degraded output reported. mitos check audits the corpus for contradictory decisions. An optional commit hook checks decision changes before allowing a commit to proceed.

Setup and configuration

Install the CLI with pipx install mitos-adr. Start the project’s Qdrant container on port 7333, register the MCP process with the client, and run mitos init from each project root. Use mitos set-key --global to configure a Gemini API key; this is the tested embedding provider and is required. An Anthropic key is strongly recommended for the conflict-audit layer.

Run mitos status . to see missing setup steps and confirm readiness. mitos projects lists registered projects, while mitos unregister <name> removes a registration without deleting its workspace. The CLI provides counterparts for the MCP operations, including surface, record, query, rebuild, and check.

Tools and capabilities

The Mitos MCP server provides decision surfacing, decision recording, and decision querying for agents. It also supports a durable Markdown record, typed decision relationships, semantic retrieval through Qdrant, exact-handle lookup, rejected-option searches, graph rebuilding, and contradiction audits. The CLI and MCP interfaces expose the same core workflow.

Limitations and notes

Mitos is marked Alpha and is actively developed. Python 3.13 or newer is indicated. Gemini is the currently tested embedding provider; multi-provider support is described as a future item. The live test suites make real Gemini and Anthropic API calls and require Qdrant, so API usage and local service availability matter for those features.

The architecture intentionally does not edit a decision’s core axiom in place. Later decisions express correction, amendment, or supersession, while commentary can be edited with mitos amend-commentary. The Markdown corpus should therefore be treated as the durable source from which derived stores can be regenerated.

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Frequently Asked Questions about Mitos

Mitos is an MCP server that connects AI agents to a project’s Markdown decision records, SQLite-backed typed graph, and local Qdrant search index. Its main tools surface prior decisions, record new decisions with rejected alternatives, and query decisions by meaning, exact handle, or rejected option.

Install it with `pipx install mitos-adr`, start the provided Qdrant container on port 7333, register `mitos serve` with your MCP client, and run `mitos init` in each project. Configure a Gemini key with `mitos set-key --global`, then verify with `mitos status .`.

Yes, Cursor is named as a supported setup path for Mitos. Register the standard MCP server with Cursor after installing the CLI and starting Qdrant, then initialize each project with `mitos init`.

Mitos requires a Gemini API key for its tested embedding provider. An Anthropic key is strongly recommended for the conflict-audit layer.

Mitos preserves them by recording later decisions as superseding, correcting, or amending earlier ones instead of editing a decision’s core axiom. Commentary can be corrected separately, while the Markdown record retains the history of why the project changed direction.

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