Project Memory

File-based, git-friendly project memory for coding agents — a JSON memory store that lives inside your repository, served to agents over the Model Context Protocol, with matching agent skills for disciplined recall and curation.

Coding agents (Claude Code, Codex, and others) forget everything between sessions. This tool gives each repository a small, reviewable knowledge base of hard-won, project-specific lessons — recurring bugs, misleading symptoms, hidden conventions, build quirks — so future sessions don't re-derive them from scratch.

Design

• Plain JSON files in your repo. One file per memory under .project-memory/active/. No database, no embeddings, no external service. Memories diff, merge, and get code-reviewed like any other file, and they travel with the repository.
• Label graph instead of vector search. Memories carry canonical prefix:kebab-case labels from a registry you control. Agents retrieve by label cluster (area:auth AND kind:bug), then use description/triggers for cheap relevance checks — deterministic and inspectable.
• Typed relationships. Memories cross-link with related (with a required reason), supersedes, and superseded_by. Links are enforced to be bidirectional, and a neighborhood query walks the graph with bounded depth.
• Lifecycle statuses, not deletion. active / stale / superseded / wrong — disproven memories become warnings instead of silently disappearing.
• Strict validation. A JSON Schema plus a built-in validator that checks the whole store: field shapes, label registry membership, filename/id agreement, relationship bidirectionality, and index freshness. Every mutation is transactional — validated, and rolled back on failure.
• Zero runtime dependencies. Pure Python standard library.

Installation

Not yet on PyPI — install straight from GitHub:

pip install git+https://github.com/1101AlexZab1011/project-memory-mcp
# or: pipx install git+https://github.com/1101AlexZab1011/project-memory-mcp
# or: uv tool install git+https://github.com/1101AlexZab1011/project-memory-mcp

Or clone and run without installing:

git clone https://github.com/1101AlexZab1011/project-memory-mcp
cd project-memory-mcp
python -m project_memory_mcp --help

Requires Python 3.10+.

Quick start

1. Initialize a store in your project:

cd /path/to/your/project
project-memory-mcp init

This scaffolds:

.project-memory/
  README.md            store rules for humans and agents
  labels.json          canonical label registry (starter kind:/context: labels)
  memory.schema.json   JSON Schema for memory files
  INDEX.json           generated search index
  active/              one JSON file per memory

Commit the whole folder.

2. Register the MCP server with your agent.

Claude Code — add to your project's .mcp.json:

{
  "mcpServers": {
    "project-memory": {
      "type": "stdio",
      "command": "project-memory-mcp",
      "args": ["serve"]
    }
  }
}

Codex — add to ~/.codex/config.toml:

[mcp_servers.project-memory]
command = "project-memory-mcp"
args = ["serve"]

The server finds the store by walking up from its working directory to the nearest .project-memory/; pass --root /path/to/project to pin it explicitly.

3. (Optional but recommended) Install the agent skills:

project-memory-mcp install-skills --claude   # -> .claude/skills/  (Claude Code)
project-memory-mcp install-skills --codex    # -> .agents/skills/  (Codex)
project-memory-mcp install-skills --dest some/other/skills/dir

Three skills teach the agent when and how to use the store well:

Skill	Purpose
project-memory-recall	Retrieve only relevant lessons before/during a task, cheaply (labels first, full files last).
project-memory-remember	After a task, decide what is durable enough to store, deduplicate, cross-link, and validate.
project-memory-forget	Safely delete a memory and clean up every reference to it.

Re-run install-skills after upgrading the package to refresh the copies.

MCP tools

Tool	Description
list_labels	Canonical labels grouped by prefix.
search_memories	Search the lightweight index by label query, status, and optional text.
get_memory	Full JSON for one memory id.
get_memory_neighborhood	Bounded relationship graph around a memory (depth, max_nodes).
create_memory	Create a memory; syncs bidirectional links, regenerates the index, validates.
update_memory	Deep-merge a patch into a memory; same sync + validation.
add_label	Register a new canonical label.
delete_memory	Delete after exact-id confirmation; removes dangling references.

Label queries accept either structured form — {"all": ["area:auth"], "any": ["kind:bug", "kind:workflow"], "not": ["context:testing"]} — or an expression string: area:auth AND (kind:bug OR kind:workflow) AND NOT context:testing.

CLI

project-memory-mcp init            [--root DIR] [--force]
project-memory-mcp validate        [--root DIR] [--fix-index]
project-memory-mcp serve           [--root DIR]
project-memory-mcp install-skills  [--root DIR] [--claude] [--codex] [--dest DIR]

validate checks the whole store and exits non-zero on any problem; --fix-index regenerates INDEX.json from the memory files (refusing if the store itself is invalid). Use it in CI or a pre-commit hook to keep hand-edited memories honest.

Memory format

{
  "schema_version": 1,
  "id": "cache-invalidation-race",
  "status": "active",
  "description": "Session cache invalidation races the auth refresh; symptoms look like random logouts.",
  "tags": ["cache", "auth"],
  "labels": ["area:auth", "kind:bug", "context:runtime"],
  "scope": {
    "project": "my-project",
    "area": "auth",
    "files": ["src/auth/session.ts"],
    "applies_to": ["session refresh flow"]
  },
  "triggers": ["random logouts", "session expired immediately after login"],
  "remembered_facts": [
    "The cache TTL and the refresh token TTL are configured in two different places."
  ],
  "solution_pattern": [
    "Invalidate the session cache inside the refresh transaction, not after it."
  ],
  "pitfalls": [
    "Reproducing locally needs two concurrent tabs; a single tab never hits the race."
  ],
  "evidence": {
    "created_from_task": "Debugging intermittent logout reports",
    "last_validated": "2026-07-07"
  },
  "relationships": {
    "related": [
      { "id": "token-refresh-clock-skew", "reason": "Both affect the session refresh flow." }
    ],
    "supersedes": [],
    "superseded_by": []
  }
}

Statuses: active (use normally), stale (verify against current code), superseded (replaced — see superseded_by), wrong (kept as a warning).

Label conventions (starter registry ships kind: and context: labels; add your own):

• kind: — type of lesson: kind:bug, kind:workflow, kind:architecture, kind:convention
• context: — situation: context:build, context:runtime, context:testing, context:tooling, context:deployment
• area: — your project's subsystems: area:auth, area:renderer, …
• signal: — recurring concrete symptoms: signal:port-conflict, signal:file-lock, …

What belongs in the store

Store lessons that are project-specific, non-obvious, likely to recur, and cheaper to know upfront than rediscover. Do not store generic programming knowledge, one-off fixes, transcripts, speculation — or secrets, credentials, and personal data (the store is plain text committed to your repository).

Development

git clone https://github.com/1101AlexZab1011/project-memory-mcp
cd project-memory-mcp
python -m unittest discover -s tests -v

No dependencies to install; tests use only the standard library.

License

MIT