Repository Memory

Repository Memory is a small, source-backed memory layer for AI agents. It gives an agent one consistent way to answer questions about project documents, research notes, reports, source-code evidence, and explicitly imported conversation memory.

The important idea is simple:

An answer is a fact only when the runtime can show where it came from.

It ships as one generic Skill with a shared Python runtime:

• a citation-first repository index and CLI;

• a local stdio MCP server for Claude, Codex, OpenClaw, and other hosts;

• an optional MemoryCore adapter for L0-L3 conversation memory;

• an optional OpenClaw lifecycle extension for conservative post-turn capture;

• a metadata-only audit proxy and a guard that can block direct-file bypasses.

The repository itself is the source of truth. Indexes, snapshots, audit logs, conversation data, and credentials stay in user-level data/config/cache directories and are never written back to the source repository by search or sync.

What happens on one question?

flowchart LR
    A[Agent question] --> B[MCP or CLI]
    B --> C[doctor and scope router]
    C --> D[Repository snapshot and structured index]
    D --> E[Verified citation: commit, path, lines]
    C --> F[Optional MemoryCore]
    F --> G[L0 raw conversation]
    F --> H[L1 atomic memory]
    F --> I[L2 scenario candidate or accepted]
    F --> J[L3 profile/core after explicit promotion]
    E --> K[Answer or abstain]
    G --> K
    H --> K
    I --> K
    J --> K
    B --> L[Optional audit and host guard]

scope=repository searches Git-backed evidence only. scope=memory searches the configured conversation-memory plane. scope=all returns two separate groups; it never fuses scores or turns a conversation into a Git citation.

Related MCP server: mem-universe

Install

Requirements: Python 3.10+ and Git. The core runtime uses only the Python standard library. Node.js is needed only for the OpenClaw extension tests or when OpenClaw itself requires it.

git clone https://github.com/LeslieWylie/repository-memory.git
cd repository-memory

# Install the Skill, CLI, MCP registration, and (when OpenClaw is configured)
# the profile-local lifecycle extension.
python3 install.py --all --source-root /path/to/knowledge-repository --json

For a single host:

python3 install.py --target codex --source-root /path/to/knowledge-repository --json
python3 install.py --target claude --source-root /path/to/knowledge-repository --json
python3 install.py --target openclaw --openclaw-config /path/to/openclaw.json \
  --source-root /path/to/knowledge-repository --json

The installer makes a timestamped backup before changing a host config. It does not push, commit, pull, or rewrite the knowledge repository.

First check

After installation, run the bundled executable or the generated user-level command:

repository-memory doctor --json
repository-memory search "the question in the user's own words" \
  --scope repository --json

With OpenClaw, verify the registered server through the host rather than trusting a model-written receipt:

openclaw mcp probe repository-memory

A healthy repository setup reports an indexed commit, a non-stale source, and results containing a valid citation. If the source is missing, stale, dirty, or the citation cannot be checked, the result stays in candidates or the runtime returns abstain=true.

Result rules

Every search response has two layers:

• verified: the runtime resolved the source, commit, path, line range, and excerpt, and no disqualifying status was found;

• candidates: related or incomplete material, including stale, generated, inferred, pending, dirty, or citation-incomplete results.

Agents should answer from verified only. A document-level verified result does not prove every part of a compound claim. Check support.claim_support and use get or explain for the full evidence window before making a claim marked partial or unknown.

The runtime does not require embeddings. When no semantic provider is configured, doctor and search say retrieval_mode=lexical and semantic_available=false; this is a supported fallback, not a hidden semantic claim. No black-box cross-backend RRF is used.

Four memory layers

The optional MemoryCore adapter keeps conversation memory distinct from repository evidence:

An API being reachable is not the same as having useful data. Doctor reports capability, reachability, record counts, pending candidates, and read-back verification separately.

MemoryCore is optional and is not bundled in this repository. Its endpoint, model, provider, and credentials are discovered from user configuration or environment at runtime. Credentials are never committed to Git. If it is not available, repository search still works and explicit session ingest can use the conservative local fallback with clearly reported layer support.

MCP

The server uses local stdio and supports the modern MCP discovery/metadata path first, while retaining a small compatibility handshake for hosts that have not migrated yet. Current tool names are:

memory_doctor
memory_sync
memory_search
memory_get
memory_init       # explicit source setup
memory_ingest     # explicit write

The MCP and CLI call the same runtime and return the same JSON contract. The server is not bound to a port.

OpenClaw capture and guard

The OpenClaw extension is optional. It can:

1. require the repository-memory MCP route for project-fact turns;

2. block the bare built-in memory tool and direct-file fallback when the host supports the relevant lifecycle hooks;

3. audit tool metadata without storing full prompts or answers;

4. capture bounded user/assistant text after a completed turn into L0;

5. leave L2 as a reviewable candidate and never write L3 automatically.

Normal coding tasks remain free to use the host's normal tools. A host without tool lifecycle hooks can still use the Skill/MCP contract, but cannot claim that direct-file access is technically blocked.

Public boundary

This project contains generic runtime code, fixtures, and documentation only. It intentionally does not contain private repositories, organization-specific evaluation sets, credentials, model names, internal hostnames, or user data. Use memory_init/source add to attach the repositories that are appropriate for your own environment.

See docs/quickstart.md, docs/architecture.md, and docs/troubleshooting.md.

License

MIT. See LICENSE.