Skip to main content

Local Codex memory manager with an official Python SDK-based MCP server.

Project description

Breathing Memory

Breathing Memory is a local memory support system for coding agents. It runs as a stdio MCP server through the official Python MCP SDK, stores memory in SQLite, and isolates memory by project so one installation can be reused across repositories without mixing contexts.

Overview

Breathing Memory keeps collaboration context that an agent should remember but a repository should not need to encode everywhere.

  • local stdio MCP server
  • SQLite storage under user app-data, isolated by project
  • fragment-centric public model built around anchor and fragment
  • text-first retrieval today, with a public search surface already aligned for later semantic retrieval work
  • dynamic working / holding maintenance with a compression backend that uses a supported coding agent without polluting normal conversation history

Supported Clients

  • Codex

Installation

The intended long-term user path is:

pip install 'breathing-memory[semantic]'
breathing-memory install-codex

breathing-memory install-codex registers the breathing-memory MCP server with the currently supported client, pins that registration to a stable project identity for the current repository, and creates or updates the managed Breathing Memory block in the current repository's AGENTS.md.

Published package:

  • recommended: pip install 'breathing-memory[semantic]'
  • minimal lexical-only install: pip install breathing-memory
  • contributor setup and unreleased local work: docs/dev-guide.md

Quickstart

Recommended first run:

python3 -m venv .venv
. .venv/bin/activate
pip install 'breathing-memory[semantic]'
breathing-memory doctor
breathing-memory install-codex

Useful commands:

  • breathing-memory doctor: inspect installation, active project identity, DB path selection, Codex registration state, and effective retrieval mode
  • breathing-memory serve: start the stdio MCP server
  • breathing-memory inspect-memory --json: inspect current memory state

How Memory Works

Breathing Memory does not auto-capture the full client conversation by itself. The supported operating path is explicit MCP use by the calling agent.

The basic flow is:

  1. Check memory_recent before persisting immediately repeated agent / user turns
  2. If there is an unremembered final agent answer from the previous turn, save it first with memory_remember(actor="agent")
  3. Save the current user message with memory_remember(actor="user")
  4. Search before an answer with memory_search
  5. Record feedback with memory_feedback when the user clearly confirms or corrects remembered information

Key points:

  • one user utterance becomes one fragment
  • one final user-facing agent answer is normally remembered on the next user turn
  • commentary is not remembered
  • use memory_recent as a caller-side first check before memory_remember when you suspect an immediately repeated save
  • track which retrieved fragments materially informed the final answer and pass them in source_fragment_ids
  • if the final answer materially used remembered fragments, pass those ids in source_fragment_ids
  • use memory_feedback only when the user's evaluation can be attributed safely
  • edits are modeled as forks rather than overwrites
  • duplicate deferred agent capture for the same reply target and content is suppressed
  • user duplicate checks are caller-side and should use memory_recent rather than engine-side suppression
  • archived runtime files such as archived_sessions/*.jsonl are not the primary capture path
  • if no later user turn arrives, the final agent answer may remain unremembered

Current MCP tools:

  • memory_remember
  • memory_search
  • memory_fetch
  • memory_recent
  • memory_feedback
  • memory_stats

memory_search keeps the default response compact. When debugging retrieval, callers can opt in to per-result diagnostics with include_diagnostics=true.

Runtime Notes

Breathing Memory stores data under the user app-data directory resolved by platformdirs, then separates memory by project identity. The exact SQLite path can be inspected with breathing-memory doctor.

For Codex installs, install-codex now pins the MCP registration to a stable project identity derived from the repository at install time, so the live MCP server does not drift with VSCode or Codex internal working directories. doctor prefers that registration-derived identity when it is available, so its reported DB path matches the live MCP target rather than the shell's current directory.

If you already have remembered data under an older unpinned Codex registration, migration is manual by design. Move the SQLite database yourself if you want to keep that history; Breathing Memory does not auto-discover or auto-merge old databases.

The current implementation supports lexical retrieval by default and semantic retrieval through the optional semantic extra. Runtime auto resolves to default when the embedding backend and HNSW support are available, resolves to lite when embeddings are available but HNSW support is unavailable, and resolves to super_lite when semantic retrieval is unavailable. When semantic retrieval encounters live fragments with missing embeddings, Breathing Memory backfills those vectors before continuing. If default search finds a missing or invalid ANN index, it attempts repair first, waits briefly for conflicting rebuild work, and returns a structured status when the caller should decide whether to retry or fall back.

breathing-memory doctor reports both the configured retrieval mode and the effective runtime mode, along with HNSW support and index readiness, so after installing breathing-memory[semantic] you can verify whether auto can target the HNSW-backed path and whether the index is already ready or still needs repair. breathing-memory install-codex also prints the effective retrieval mode in its post-install summary, so the semantic state is visible even before the first MCP conversation.

The current compression backend invokes a supported coding agent without leaving normal conversation history. In the current supported setup, that path uses Codex through codex exec --ephemeral.

Further Reading

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

breathing_memory-0.5.4.tar.gz (59.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

breathing_memory-0.5.4-py3-none-any.whl (41.0 kB view details)

Uploaded Python 3

File details

Details for the file breathing_memory-0.5.4.tar.gz.

File metadata

  • Download URL: breathing_memory-0.5.4.tar.gz
  • Upload date:
  • Size: 59.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for breathing_memory-0.5.4.tar.gz
Algorithm Hash digest
SHA256 f9dbdf207489158e3a4eb684e0f02631fd8da8d95a3431c2258a8b6a73429d6b
MD5 df67862f5e3134d914bc4d4f8e04d2f3
BLAKE2b-256 b206a981a06c10e353bdee469d23473bc2a270247202f8b2a6ac1d3694c3d1cf

See more details on using hashes here.

Provenance

The following attestation bundles were made for breathing_memory-0.5.4.tar.gz:

Publisher: publish.yml on KazinaG/breathing_memory

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file breathing_memory-0.5.4-py3-none-any.whl.

File metadata

File hashes

Hashes for breathing_memory-0.5.4-py3-none-any.whl
Algorithm Hash digest
SHA256 3ace95eace4fa05296ec8b4dce85eea298cea7cee26b800e4c5d0af21812c533
MD5 913412874250718ff915d2c329cfe7c2
BLAKE2b-256 db6076f98df1806bdfa407ecfc2f6ffdad34093ffbe1db81f4866bc5bc489e82

See more details on using hashes here.

Provenance

The following attestation bundles were made for breathing_memory-0.5.4-py3-none-any.whl:

Publisher: publish.yml on KazinaG/breathing_memory

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page