Local AI-agent memory server and Python library · Emerging active project with substantial adoption signals, but supplied metadata and documentation are dated in 2026 and cannot be independently reconciled in this analysis context.

mnemosyne-oss/mnemosyne

MIT-licensed, local-first Python memory layer for AI agents that stores memories in SQLite and exposes remember, recall, and forget capabilities to Claude Code through a documented stdio MCP server; it also offers a Python SDK, CLI, optional local embeddings and consolidation, temporal triples, memory banks, import/export, and optional encrypted cross-instance sync.

AI agent memoryClaude Code integrationLocal-first infrastructureSemantic retrievalKnowledge graphsDeveloper toolingData synchronizationMulti-agent systems
Routing score
80.0
Readiness
Usable for local Claude Code memory with caveats; production sync, remote models, sensitive-memory retention, and benchmark claims require additional validation.
License
MIT
Maintenance
active
Components
20
Revision
0

Selection

Select when

  • You need persistent cross-session memory in Claude Code through MCP.
  • You prefer local SQLite storage over a hosted memory platform.
  • You want one memory layer shared by Claude Code and other MCP or Python agents.
  • You need explicit remember, recall, forget, export, and import operations.
  • You want hybrid FTS5 and optional vector retrieval without an external vector database.
  • You need scoped memories, expiry, banks, or temporal triples.
  • You are willing to operate a self-hosted sync endpoint for cross-device memory.
  • You require optional client-side encryption for sync payload content.
  • You want a deeper first-party memory-provider integration with Hermes Agent.

Boundaries

Avoid when

  • You require a native Claude Code plugin with lifecycle hooks or automatic capture evidenced for Claude Code.
  • You cannot allow an agent-accessible store to retain sensitive conversation or project content.
  • You need independently audited encryption, authorization, sandboxing, or privacy guarantees.
  • You require independently reproducible state-of-the-art memory benchmarks.
  • You need zero operational dependencies while also requiring vector search, MCP, local LLMs, or sync.
  • You cannot review the privacy effects of remote embedding or LLM endpoints.
  • You need a managed multi-tenant service with documented enterprise controls.
  • You require proven high-quality multilingual retrieval without model selection and evaluation.
  • You need guaranteed sub-millisecond retrieval at large corpus sizes.
  • You cannot manage backups and encryption keys for local or synchronized SQLite data.

Strengths

Capabilities

Runs a documented stdio MCP server that Claude Code can launch from its MCP configuration.Exposes three documented basic MCP tools for remembering, recalling, and forgetting memories.Provides a Python SDK with remember and recall entry points.Stores memory in SQLite with FTS5 and optional vector-search support.Combines vector similarity, full-text rank, and importance in documented hybrid retrieval.Supports working and episodic memory tiers with explicit consolidation.Supports temporal recall weighting and configurable recency decay.Supports memory importance, source, scope, expiry, and optional entity extraction.Provides temporal knowledge-graph triples with as-of querying.Provides named memory banks for domain isolation.Offers CLI operations for storage, recall, statistics, consolidation, export, and import.Supports local embedding extras and configurable OpenAI-compatible remote embedding endpoints.Offers stdio, SSE, and Streamable HTTP MCP transports.Provides optional bidirectional, push-only, or pull-only delta synchronization.Supports optional client-side sync encryption with Fernet or PyNaCl SecretBox.Supports API-key and JWT authentication for synchronization.Documents self-hosted sync deployment using Docker Compose, Caddy, or Fly.io.Provides native or adapted integrations for Hermes, OpenClaw, OpenWebUI, Pi, Zero, Codex, Cursor, and Windsurf.Includes a Hermes memory-provider package with hooks, prompt injection, automatic capture, tools, and lifecycle handling.Claims no telemetry and no outbound data by default unless remote models, sync, or other networked features are enabled.

Risk profile

Risks and limitations

  • Uncertainty: only selected first-party files were supplied, so MCP schemas, retrieval code, encryption implementation, authorization, migrations, tests, packaging, and Claude Code integration behavior were not directly inspected.
  • Uncertainty: repository and benchmark dates extend into 2026 relative to this analysis context, so recency, release status, adoption counts, and version claims cannot be independently reconciled.
  • Uncertainty: the dedicated Claude Code integration guide was referenced but not supplied, leaving config-file location, minimum host version, and tested compatibility unverified.
  • The files report different tool counts across basic MCP, full MCP, legacy Hermes, and current Hermes surfaces, so exact availability is version and integration dependent.
  • The README withdraws a prior LongMemEval result for lack of methodology, while its comparison table still lists a different LongMem figure, creating unresolved benchmark-documentation inconsistency.
  • The documentation calls the system sub-millisecond, but the supplied pure-retrieval table reports 35–493 ms, so latency claims require workload-specific validation.
  • The published BEAM result is explicitly point-in-time, has not been rerun on the current tree, and uses a different judge from a compared competitor.
  • The reported pure-retrieval Recall@10 is 20%, which the project itself describes as low.
  • Remote embeddings and LLM-driven extraction can transmit stored memories and recall queries to third-party endpoints.
  • Local SQLite files can contain sensitive preferences, credentials, conversations, and project details; local at-rest protection and host access controls require careful configuration.
  • Sync without client-side encryption exposes memory content according to the operator's infrastructure, while loss of an encryption key can make encrypted memories unrecoverable.
  • No independent security audit, privacy assessment, Claude Code compatibility matrix, benchmark reproduction, or production reliability study was supplied.

The default documented Claude Code path launches a local stdio MCP process over a local SQLite store. Network exposure appears optional through SSE or Streamable HTTP MCP, remote embeddings or LLMs, and sync. Non-loopback HTTP requires allowed-host and, for browsers, allowed-origin configuration according to the README. Sync supports API keys, JWTs, TLS guidance, and optional authenticated client-side payload encryption. The evidence does not establish audited implementation quality, local database encryption defaults, per-tool authorization, or safe handling of agent-supplied sensitive content.

Component inventory

20 documented components

Claude Code MCP extension

Mnemosyne MCP server

Local SQLite-backed memory tools exposed to Claude Code and other MCP clients over stdio, SSE, or Streamable HTTP.

README.md
command-line interface

Mnemosyne CLI

Stores, recalls, consolidates, inspects, exports, and imports local memories.

README.md
Python module

Mnemosyne Python SDK

Direct API for adding and retrieving memories from Python agents and applications.

README.md
Python module

BEAM memory API

Advanced direct access to working memory, episodic consolidation, and recall.

README.md
Python module

Temporal TripleStore

Bank-scoped temporal knowledge graph for adding and querying versioned triples.

README.md
Python module

Memory banks

Creates isolated per-domain memory stores and selects a bank for Mnemosyne operations.

README.md
sync service and workflow

Mnemosyne Sync

Bidirectional, push-only, or pull-only delta sync with authentication and optional client-side encryption.

README.md
deployment workflow

Self-hosted sync deployment

Docker Compose/Caddy and Fly.io configurations for hosting the Mnemosyne sync endpoint.

deploy/sync/README.md
Hermes plugin

Mnemosyne Hermes provider

First-class Hermes memory provider with lifecycle capture, prefetch, tool registration, consolidation, and local persistence.

integrations/hermes/README.md
Hermes directory plugin

Hermes catalog plugin

Catalog wrapper that installs the released provider and embedding-enabled core into Hermes.

integrations/hermes-catalog/README.md
Hermes skill

Mnemosyne memory override skill

Behavioral guardrail steering Hermes agents away from the legacy memory tool for durable facts.

integrations/hermes/README.md
Hermes command namespace

Hermes Mnemosyne commands

Hermes-facing status, consolidation, inspection, clearing, export, and import operations.

hermes_memory_provider/README.md
agent plugin

Zero plugin

Adds five shell-backed memory tools, an after-tool file-edit capture hook, and model guidance to Zero.

integrations/zero/README.md
editor extension

VS Code extension

Sidebar and commands for browsing, searching, storing, and refreshing Mnemosyne memories.

integrations/vscode-mnemosyne/README.md
Obsidian plugin

Obsidian Sync plugin

Exports memories as Markdown notes with frontmatter, merge protection, filtering, and scheduled sync.

integrations/obsidian-mnemosyne/README.md
agent tool bridge

OpenWebUI tool integration

Exposes Mnemosyne to OpenWebUI as a native tool; documentation also identifies a deeper chat-capture integration.

docs/integrations/README.md
agent plugin

OpenClaw provider

Native Mnemosyne memory provider integration for OpenClaw.

README.md
agent extension

Pi extension and skill

Packaged Pi integration combining an extension with memory guidance.

docs/integrations/README.md
integration workflow

Generic MCP client integrations

Documented stdio MCP setup for Cursor, Codex CLI, Windsurf, Hermes, and other MCP clients.

docs/integrations/README.md

Technical profile

Requirements and configuration

Repository
mnemosyne-oss/mnemosyne
License
MIT
PrimaryLanguage
Python
PythonRequirement
Python 3.10+
Distribution
PyPI packages mnemosyne-memory and mnemosyne-hermes are documented.
Storage
SQLite with FTS5 and optional sqlite-vec or compressed vectors.
Retrieval
Documented weighted hybrid scoring uses vector similarity, FTS5 rank, and importance.
ClaudeCodeInterface
Local MCP server launched with the mnemosyne executable and mcp argument.
McpTransports
stdio, SSE, and Streamable HTTP are documented.
BasicMcpSurface
The integrations index documents remember, recall, and forget tools.
Sdk
Python remember and recall APIs plus lower-level BEAM and triple-store interfaces.
MemoryModel
Working memory, episodic memory, and a temporal triple store are documented.
EmbeddingModes
Optional local embedding packages or OpenAI-compatible remote embedding APIs.
DefaultEmbeddingModel
BAAI/bge-small-en-v1.5 is documented as the default and English-optimized.
DataLocation
The README documents ~/.hermes/mnemosyne/data as the default data directory.
Sync
Delta-based bidirectional, push-only, or pull-only synchronization between instances.
SyncEncryption
Optional Fernet or PyNaCl SecretBox client-side encryption is documented.
Deployment
Local package installation plus optional Docker Compose, Caddy, bare-metal, or Fly.io sync hosting.
Telemetry
The README states zero telemetry.
RepositoryStatus
Public, non-archived repository with MIT metadata and 3,299 stars in the supplied snapshot.

Classification

How it enters the stack

Tool SurfaceInfrastructure

Evidence: Claude Code launches Mnemosyne as a separately installed local MCP server and receives memory tools; SQLite persistence and optional sync operate alongside the host. It is not evidenced as a native Claude plugin or hook. Basic setup is a Python package plus MCP configuration, while optional embeddings and sync increase effort.

Runs Alongside · low setup effort · high confidence · automated

Evidence and risk

Primary sources

first_party_fileREADME.mdhttps://github.com/mnemosyne-oss/mnemosyne/blob/main/README.md
first_party_filedocs/README.mdhttps://github.com/mnemosyne-oss/mnemosyne/blob/main/docs/README.md
first_party_filedeploy/sync/README.mdhttps://github.com/mnemosyne-oss/mnemosyne/blob/main/deploy/sync/README.md
first_party_filemnemosyne/install.pyhttps://github.com/mnemosyne-oss/mnemosyne/blob/main/mnemosyne/install.py
first_party_filedocs/getting-started.mdhttps://github.com/mnemosyne-oss/mnemosyne/blob/main/docs/getting-started.md

Routing context

Conflicts, complements, and synergies

integrates_with

gh_nousresearch_hermes_agent

Mnemosyne is Hermes-first and provides a dedicated Hermes memory-provider package, plugin catalog wrapper, tools, lifecycle hooks, prompt injection, capture, recall, and consolidation.

high confidence
integrates_with

gh_openai_codex

The repository documents Codex CLI connection through the local MCP server and a .codex/mcp.json configuration path.

high confidence