Compound Memory

io.github.chinwev0.4.3Updated Oct 7, 2026

Local-first shared memory for multiple AI agents that compounds with use; Markdown + git, MCP + CLI.

Overview

AI-generated overview

Gives AI assistants a local, shared Markdown memory store they can write to, search, link, and reinforce across sessions and hosts.

What it does
Exposes five MCP tools — memory_write, memory_search, memory_get, memory_link, and memory_feedback — as the read/write boundary over a local store of frontmatter-annotated Markdown files. Memories carry confidence and use counts; confirmed use raises confidence, feedback from a different host adds an independent bonus, and neglected memories decay into a revivable archive. A CLI covers initialization, stats, decay, revive, distillation, review queue, and git history, with optional semantic recall via sqlite-vec and BGE embeddings that falls back to lexical search.
When to use it
Useful when several agents or sessions should share durable facts, preferences, project conventions, and pitfalls instead of rediscovering them each time. Best for users who want memory stored as human-readable local files with a git audit trail rather than in an opaque hosted database.
Requirements
Local process; Python 3.11 or newer, installed from PyPI or a cloned repository with uv. The store root defaults to ~/.agents/memory and can be overridden with COMPOUND_MEMORY_ROOT; COMPOUND_MEMORY_AGENT_ID is strongly recommended so caller identity is resolved from the process environment. No authentication is declared. Optional vector search needs sqlite-vec and BGE embeddings.
Before you install
The server writes to and auto-commits local files on every write, and CLI operations can archive, revive, or permanently remove memories. Setting COMPOUND_MEMORY_AGENT_ID is recommended so a model cannot misreport or forge its identity. Optional semantic recall downloads and runs embedding models locally. Nothing is sent off the machine by default.

Installation

In SourceWeft

  1. Open Compound Memory in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

compound-memory

[CI] [PyPI] [Python] [License: MIT]

English | 简体中文

Local-first shared memory for multiple AI agents — plain Markdown files that compound in value as they are used. Memory lives on your disk as frontmatter-annotated Markdown, gets stronger with every confirmed use, decays into a revivable archive when neglected, and auto-commits to a local git history on every write.

Why

Every agent session starts from zero: preferences get re-asked, project conventions get re-discovered, the same pitfall gets hit twice. compound-memory gives all your agents one shared store:

  • Local-first — nothing leaves your machine; memories are human-readable Markdown files, not rows in an opaque database.
  • MCP-native — exactly 5 tools (memory_write / memory_search / memory_get / memory_link / memory_feedback) as the single read-write boundary; works with any MCP host (Claude Code, ZCode, WorkBuddy, …), plus a full CLI for operations.
  • Compounding — confirmed usage raises confidence, related memories are recalled as neighbors, validation from a different host counts as independent evidence, and distillation merges many raw memories into fewer, denser ones.
  • Multi-agent by design — a _shared namespace everyone reads, plus agent-* private namespaces each host owns; cross-host validation is tracked per host.
  • Optional semantic recall — vector search via sqlite-vec + BGE embeddings, with automatic graceful fallback to pure lexical search when unavailable.

Quick Start

For AI agents

Paste this one-liner into your coding agent (Claude Code, Cursor, ZCode, …) and let it do the rest:

text
Set up compound-memory (https://github.com/chinwe/compound-memory) — a local-first multi-agent shared memory (MCP server + CLI) — on this machine: install it (`uv tool install compound-memory`, or clone the repo and `uv sync --extra dev`), initialize the store (`compound-memory init`, defaults to ~/.agents/memory), register its stdio MCP server in this host's MCP config — command `compound-memory-server` (PyPI install) or `uvx --from compound-memory compound-memory-server`, env `COMPOUND_MEMORY_ROOT=~/.agents/memory` and `COMPOUND_MEMORY_AGENT_ID=agent-<your-host-id>` — then verify by calling `memory_search` and expecting a `{"hits": [...]}` response; if the host needs a restart to load MCP servers, tell me. Host-specific configs and the usage protocol: docs/agent-integration.md in the repo.

For humans

1. Install

Python ≥ 3.11. Either route works:

bash
# Route A: clone the repo (uv-managed; same path the MCP config uses)git clone https://github.com/chinwe/compound-memory.gitcd compound-memory && uv sync --extra dev
# Route B: install from PyPI (no clone needed)uv tool install compound-memory   # or: pip install compound-memory
2. Initialize your store

Defaults to ~/.agents/memory; override with the COMPOUND_MEMORY_ROOT env var.

bash
uv run compound-memory init
3. Wire it into your MCP host (recommended)

This lets your everyday agents read/write the shared store automatically:

json
{  "mcpServers": {    "compound-memory": {      "type": "stdio",      "command": "uv",      "args": ["run", "--directory", "<repo>", "compound-memory-server"],      "env": {        "COMPOUND_MEMORY_ROOT": "~/.agents/memory",        "COMPOUND_MEMORY_AGENT_ID": "agent-<your-host-id>"      }    }  }}

Installed from PyPI? Swap command/args for uvx + ["--from", "compound-memory", "compound-memory-server"] — no repo clone needed. Setting COMPOUND_MEMORY_AGENT_ID is strongly recommended: the store then resolves caller identity from the process env, so a model misreporting its identity (or forging someone else's source) is rejected loudly.

Verify: ask your agent to call memory_search (any keyword) — a {"hits": [...]} response means you're connected. Or run uv run compound-memory stats from the CLI.

4. Next step

Inject the usage protocol from skills/compound-memory/SKILL.md into your host (the search → feedback → distill loop), per docs/agent-integration.md §6.

Demo

[compound-memory CLI demo: init → write → search → feedback → stats]

One full loop: write → search → feedback (with cross-host first-validation bonus) → store health. Real output from v0.4.0, long payloads trimmed:

bash
uv run compound-memory init
json
{ "ok": true, "root": "~/.agents/memory" }

Two different hosts each write one stable fact (new memories start at confidence 0.5, uses 0):

bash
uv run compound-memory write \  "Deploy serverless functions on this platform times out at 10s — keep handlers under that budget" \  fact agent-claude --key vercel-timeout
json
{  "id": "20261007_86adf1",  "ns": "_shared",  "type": "fact",  "source": "agent-claude",  "content": "Deploy serverless functions on this platform times out at 10s — keep handlers under that budget",  "confidence": 0.5,  "uses": 0,  "key": "vercel-timeout",  "validated_by": []  ...}
bash
uv run compound-memory write \  "User prefers concise replies with tables and code examples" \  fact agent-zcode --key user-style

Search ranks by score (--explain attaches per-hit ranking components for debugging):

bash
uv run compound-memory search "serverless timeout"
json
[  {    "id": "20261007_86adf1", "score": 1.0292, "similarity": 1.0,    "type": "fact", "source": "agent-claude",    "content": "Deploy serverless functions on this platform times out at 10s — keep handlers under that budget",    "neighbors": []  },  {    "id": "20261007_6a0c0c", "score": 0.5211, "similarity": 0.4919,    "type": "fact", "source": "agent-zcode",    "content": "User prefers concise replies with tables and code examples",    "neighbors": []  }]

A different host used this memory and reported it back — uses +1, conf +0.1; and since the reporter agent-workbuddy ≠ source agent-claude, the first cross-host validation adds another +0.15:

bash
uv run compound-memory feedback 20261007_86adf1 agent-workbuddy
json
{  "id": "20261007_86adf1",  "confidence": 0.75,  "uses": 1,  "last_used": "2026-10-07",  "validated_by": ["agent-workbuddy"],  "evidence": {    "success_count": 1, "failure_count": 0, "contradiction_count": 0,    "last_verified": "2026-10-07",    "recent": [{ "date": "2026-10-07", "agent": "agent-workbuddy", "outcome": "success" }]  }  ...}

Store health at a glance (fixed-bucket histograms, liveness, distillation yield):

bash
uv run compound-memory stats
json
{  "total": 2, "archived": 0, "active": 2,  "avg_confidence": 0.625,  "by_type": { "fact": 2 },  "by_ns": { "_shared": 2 },  "review_queue_entries": 0,  "uses_histogram": { "0": 1, "1-2": 1, "3-5": 0, "6-9": 0, "10+": 0 },  "confidence_histogram": { "<0.3": 0, "0.3-0.6": 1, "0.6-0.8": 1, "0.8-1.0": 0 },  "recent_feedback_7d": 1, "cross_validated": 0,  "distilled_total": 0, "distilled_recent_7d": 0}

Three things to notice:

  • New memories start at confidence 0.5 and move on evidence — feedback carries an outcome: success raises it, failure lowers it (floor 0.05), contradiction freezes it into the review queue, obsolete archives immediately.
  • First validation from a different host earns an independent bonus (once per host per memory), with validated_by / evidence trails — confidence is evidence of correctness, not popularity.
  • Hits embed one-hop neighbors automatically (empty here — no links yet; memory_link creates bidirectional links that get recalled for free).

How compounding works

Interest sourceMechanism
① Usage reinforcementmemory_feedback: uses+1, conf+0.1
② Link valuememory_link creates bidirectional links; memory_get pulls one-hop neighbors; search hits embed up to 3 compact neighbors (active memories only, --no-neighbors to disable)
③ Distillationdistill-plan (CLI, deterministic candidates + dual-signal dedup annotations) → agent judgment → distill-apply atomic commit (product links back to sources; sources archived but revivable)
④ Cross-agent validationFeedback from an agent other than the source adds conf +0.15

Scoring (weights are the W_* constants in src/compound_memory/scoring.py): 0.70·similarity + 0.15·confidence + 0.10·recency(0.5+0.5·e^(−Δt/τ)) + 0.05·type weight. With the vector channel enabled, ranking switches to RRF fusion with an ε=0.04 prior tie-break (see the spec, "index as cache").

The 5 MCP tools

ToolPurposeKey points
memory_writeWrite a memorytype: episode/fact/insight/skill/decision; source: your agent id; give fact/insight/decision a stable key; optional valid_from/valid_until (ISO dates) and project scope
memory_searchRetrieveReturns {"hits": [...]} ranked by score; embeds up to 3 one-hop neighbors; dual-channel by default (_shared + caller's own private ns); optional project (fail-closed) and explain
memory_getFetch by idAlways contains a found key; pulls one-hop neighbors; private-ns targets require reader
memory_linkLink two memoriesBidirectional; both sides must be in the same ns; private-ns links require owner identity
memory_feedbackReport "this memory was actually used"Default outcome=success: uses+1, conf+0.1; first cross-host validation +0.15; also failure / contradiction / obsolete / unknown. Mandatory after adopting a hit — that's the loop that makes the store compound

Tool descriptions embed the protocol rules themselves, so agents keep the loop intact even without host-side rules injected. Full parameter reference: docs/agent-integration.md.

CLI

bash
uv sync --extra dev              # first clone: build .venv (later `uv run` reuses it)
uv run compound-memory init               # initialize an empty storeuv run compound-memory write "Vercel Serverless has a 10s timeout" episode agent-workbuddyuv run compound-memory search "Vercel timeout"   # hits embed one-hop neighbors (limit 3, --no-neighbors to disable)uv run compound-memory feedback <id> agent-claudeuv run compound-memory decay          # run from cronuv run compound-memory revive <id>    # revive an archived memoryuv run compound-memory distill-plan   # distillation candidates: merge_with (same-key strong) + possible_dup_of (BM25 weak) + promotion_candidate (high-activity episodes)uv run compound-memory distill-apply "the merged insight" insight agent-workbuddy --sources <id1>,<id2>  # atomic: product (links, origin=distillation) + source archival, one commituv run compound-memory stats            # health: uses/confidence buckets + liveness + distillation yielduv run compound-memory rebuild-index  # rebuild the search cache anytimeuv run compound-memory review-queue   # conflict queue (CLI-only entry)uv run compound-memory git-log        # audit trail

More operations: explain <id> (confidence composition + evidence detail for one memory), forget <id> --agent <id> (terminal removal, ADR-0009), review-resolve (adjudicate conflicts), extract <transcript|dir> (deterministic session-transcript mining).

Architecture

Agent (MCP client / CLI)  └─ memory_write | memory_search | memory_get | memory_link | memory_feedback       └─ MemoryStore (~/.agents/memory)            ├─ namespaces/_shared/{episode,fact,insight,skill}/*.md   shared area            ├─ namespaces/agent-*/...                                  private areas            ├─ archive/...                                             decayed archive (revivable)            ├─ index/tokens.json                                       rebuildable search cache            ├─ review-queue.md                                         fact/insight conflict queue            └─ .git/                                                   auto-commit on every write

Scheduled distillation prep (launchd / cron / systemd)

Per ADR 0001, the deterministic prep runs on a schedule while judgment (summarizing / merging) stays with the calling agent. Every day at 09:00 the candidate list lands in <root>/distill/last-plan.json. Pick one scheduler — launchd (macOS standard, catches up after sleep), systemd user timer (Persistent=true, same catch-up), or cron (most portable, no catch-up) — all three drive the same platform-neutral scripts/distill-prepare.sh. Ready-made templates with copy-paste instructions: scripts/com.compound-memory.distill-prepare.plist.tmpl (launchd), scripts/compound-memory-distill-prepare.{service,timer}.example (systemd), and the Chinese README for cron. The script runs set -eu: any failure exits non-zero (visible via launchctl list / systemctl --user list-timers / cron mail, log at distill/prepare.log). distill/ is a runtime artifact directory (auto-gitignored) — no commit noise; only distill-apply after agent judgment lands one atomic commit.

Documentation

Development

bash
uv run pytest tests/ -q     # full suite (MCP tool boundary + distillation + lifecycle/index/CLI + input defense)uv run mypy src/compound_memory/

Test seams: the MCP tool boundary via in-process mcp.Client(server) (no subprocess) plus unit tests for core modules (scoring / index / store ops). CI runs tests, type checks, and a pure-wheel install smoke across Python 3.11/3.12/3.13.

Release

PyPI versions are immutable and the tag must match pyproject.toml's version (the release workflow verifies this and fails loudly). Releases go through GitHub Actions + PyPI Trusted Publisher (OIDC, no token): push a tag like v0.1.0 and release.yml builds and publishes automatically.


MCP Registry name: mcp-name: io.github.chinwe/compound-memory

Source: README.md at commit 852735b

Tools

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Version history

1
  1. v0.4.3LatestOct 7, 2026