mnemosyne-memory 3.15.1

The Universal Memory Layer for Any AI Agent — Zero-Dependency, Sub-Millisecond, Fully Private

Mnemosyne

Zero-cloud AI memory that works everywhere. SQLite-backed. One pure-Python dependency.

Mnemosyne is a universal, Hermes-first memory layer that works with any agent framework (Claude Code, Cursor, Codex, OpenWebUI, OpenClaw, or your own custom agent). One pip install, one SQLite database. No external services required.

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Table of Contents

• Works With Everything
• Quick Start
  • Add to your agent
• Benchmarks
• CLI Usage
• Python API
  • BEAM Direct Access
• Architecture
• Why Mnemosyne?
• Security & Privacy Model
• Configuration
  • Environment Variables
• Hermes Plugin
• Mnemosyne Sync
• Contributing
• Sponsors
• Support
• License

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Works With Everything

Platform	Method	Setup
Cursor	MCP	Add to .cursor/mcp.json
Claude Code	MCP	Add to claude.json
OpenAI Codex CLI	MCP	Add to .codex/mcp.json
Windsurf	MCP	Add to .windsurf/mcp_config.json
OpenWebUI	Native @tool	Drop bridge file into data/tools/
Pi	Pi extension + skill	pi install npm:@mnemosyne-oss/pi-mnemosyne
OpenClaw	Native provider	pip install mnemosyne-memory[openclaw]
Hermes Agent	MCP + Plugin	Native -- ships enabled
Hermes Tweet	Companion plugin	Add Hermes Tweet when remembered sessions need X/Twitter post, account, trend, or search context
Any MCP client	MCP (stdio/SSE)	One config line
Any Python agent	Direct SDK	import mnemosyne

See docs/integrations/ for complete setup guides per platform.

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Quick Start

pip install mnemosyne-memory

# With all features (vector search + MCP server)
pip install "mnemosyne-memory[all]"

# Upgrade
pip install --upgrade mnemosyne-memory

Add to your agent

MCP-based (Cursor, Claude Code, Codex, Windsurf):

{
  "mcpServers": {
    "mnemosyne": {
      "command": "mnemosyne",
      "args": ["mcp"],
      "env": {}
    }
  }
}

Python SDK (any agent):

from mnemosyne import remember, recall

remember("User prefers dark mode interfaces")
results = recall("user preferences")

OpenWebUI: Drop a 1-line bridge file into data/tools/.

OpenClaw: Add provider: mnemosyne.integrations.openclaw:create_provider to config.

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Benchmarks

Mnemosyne scores competitively on the two major memory benchmarks, LongMemEval (ICLR 2025) and BEAM (ICLR 2026), both in one SQLite file with no cloud dependency.

Read the version labels. These are point-in-time results, not a claim about the current build. The BEAM numbers were measured on v3.0.0 (May 2026) and predate polyphonic recall, enhanced recall, SHMR, and the persona tier. They have not been re-run since. Re-running BEAM and LongMemEval on the current tree is tracked as an open task.

LongMemEval (retrieval), measured April 2026

System	Score	Notes
Mnemosyne (dense)	98.9% Recall@All@5	Apr 2026, bge-small-en-v1.5, 100 instances
Mempalace	96.6% Recall@5	AAAK + Palace architecture
Backboard	93.4%	Independent assessment
Hindsight	91.4%	Vectorize.io

Note that Mnemosyne's row is Recall@All@5 while Mempalace's is Recall@5; the metrics are not identical and the ordering should not be read as a strict ranking.

BEAM (end-to-end QA), measured on v3.0.0

Scale	Mnemosyne v3.0.0	Honcho	Hindsight	LIGHT	RAG
100K	65.2%	63.0%	73.4%	35.8%	32.3%

Per-ability (100K): IE 91.5% · MR 87.5% · TR 75.0% · ABS 100.0% · CR 50.0% · KU 50.0% · EO 25.0% · IF 62.5% · PF 54.5% · SUM 55.6%

Judge caveat: Mnemosyne's run used Llama 3.3 70B with a DeepSeek V4 Flash judge, while Hindsight's published 73.4% used Llama-4-Maverick. Scores produced under different judges are not directly comparable, so the 65.2% and 73.4% figures in the same row should be read with that in mind. Hindsight leads on this benchmark as published. See beam-benchmark.md for the full methodology.

BEAM retrieval (pure recall)

This measures raw retrieval in isolation, with no answer synthesis, so it is a different quantity from the end-to-end QA scores above and is not comparable to them.

Scale	Recall@10	Latency	Storage	Messages
100K	20%	372ms	1.8 MB	200
500K	20%	412ms	3.2 MB	1,000
1M	20%	493ms	4.8 MB	2,000
10M	20%	35ms	7.2 MB	20,000

The notable property is that recall holds flat as the corpus grows by two orders of magnitude, and that storage grows sub-linearly: episodic compression delivers 9.4x savings. Abstention accuracy is 100%, meaning the system declines rather than inventing an answer when the corpus does not contain one. The absolute 20% Recall@10 is low, and the flatness rather than the level is the result worth citing.

Full reports: docs/beam-benchmark.md

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CLI Usage

If Mnemosyne is installed in an isolated venv, activate that venv or invoke its bin/mnemosyne executable before running these commands.

# MCP server (works with any MCP client)
mnemosyne mcp                          # stdio (default)
mnemosyne mcp --transport sse --port 8080  # SSE (web clients)

# Direct memory ops
mnemosyne store "User likes dark mode"
mnemosyne recall "preferences"
mnemosyne stats
mnemosyne sleep                         # Run consolidation

# Export / import
mnemosyne export backup.json
mnemosyne import backup.json

# Sync (bidirectional memory sync between instances)
mnemosyne sync --db-path /path/to/mnemosyne.db --remote https://my-vps:8765

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Python API

from mnemosyne import remember, recall

# Store a fact
remember("User prefers dark mode interfaces",
         importance=0.9, source="preference")

# Store globally (visible across all sessions)
remember("User email is [e-mail]",
         importance=0.95, scope="global")

# Store with expiry
remember("Temp token: abc123",
         importance=0.8, valid_until="2026-12-31")

# Search
results = recall("interface preferences", top_k=3)

# Temporal recall (recency boost)
results = recall("deployments",
                 temporal_weight=0.5, temporal_halflife=48.0)

# Entity extraction
remember("Met with Abdias about the v2 release",
         extract_entities=True)

# LLM-driven fact extraction
remember("User said they prefer Python for backend work",
         extract=True)

# Temporal triples (knowledge graph)
from mnemosyne.core.triples import TripleStore
kg = TripleStore()
kg.add("Maya", "assigned_to", "auth-migration",
       valid_from="2026-01-15")
kg.query("Maya", as_of="2026-02-01")

# Memory banks (per-domain isolation)
from mnemosyne.core.banks import BankManager
BankManager().create_bank("work")
work_mem = Mnemosyne(bank="work")
work_mem.remember("Sprint review on Friday")

Advanced: BEAM Direct Access

from mnemosyne.core.beam import BeamMemory

beam = BeamMemory(session_id="my_session")
beam.remember("Important context", importance=0.9)
beam.consolidate_to_episodic(
    summary="User likes Neovim",
    source_wm_ids=["wm1"]
)
results = beam.recall("editor preferences", top_k=5)

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Architecture

+------------------------------------------------------------+
|                    Any AI Agent                            |
|  (Hermes - Claude Code - Cursor - Codex - OpenWebUI - MCP) |
+------------------------+-----------------------------------+
                         | MCP / SDK / Plugin
+------------------------v-----------------------------------+
|                      Mnemosyne BEAM                         |
|  +------------+  +--------------+  +--------------------+   |
|  | Working    |  | Episodic     |  | TripleStore         |   |
|  | Memory     |->| Memory       |  | (Temporal KG)      |   |
|  | (hot ctx)  |  | (long-term)  |  +--------------------+   |
|  +------------+  +------+-------+                           |
|                         |                                    |
|              +----------v----------+                        |
|              |     SQLite DB       |                        |
|              |  (single file)      |                        |
|              |  sqlite-vec + FTS5  |                        |
|              |  MIB binary vectors |                        |
|              +---------------------+                        |
+-------------------------------------------------------------+

BEAM (Bilevel Episodic-Associative Memory):

• Working memory -- Hot context, auto-injected before LLM calls, TTL-based eviction
• Episodic memory -- Long-term storage with sqlite-vec + FTS5 hybrid search
• TripleStore -- Temporal knowledge graph with version chains

Hybrid scoring: 50% vector similarity + 30% FTS5 rank + 20% importance, all inside SQLite.

Binary vectors: Information-theoretic binarization (MIB) compresses 384-dim float32 embeddings into 48 bytes -- 32x reduction. Hamming distance entirely within SQLite. No ANN indices, no external vector DB.

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Why Mnemosyne?

Feature	Mnemosyne	mem0	Letta	Honcho	SuperMemory	Hindsight	ChromaDB
Local-first	✅ SQLite	⚠️ Hybrid	❌ Docker+PG	⚠️ PG+worker	❌ SaaS	✅ SQLite	✅ Embedded
Zero deps	✅ pip only	❌ Qdrant/PG	❌ PG+vector	❌ PG+3 LLMs	❌ SaaS infra	✅ pip only	✅ pip only
MCP server	✅ Built-in	❌	❌	❌	✅	❌	❌
Python SDK	✅	✅	✅	✅	✅	✅	✅
Multi-platform	✅ 8+ targets	⚠️ 3 adapters	❌ Agent-only	⚠️ 4 adapters	✅ MCP	❌ Agent-only	❌ Library only
Open source	✅ MIT	✅ Apache 2.0	✅ OSS	⚠️ AGPL	❌ Proprietary	✅ MIT	✅ Apache 2.0
Benchmark	65.2% BEAM / 98.9% LongMem	49% LongMem	83.2% LoCoMo	90.4% LongMem	85.2% MemoryBench	73.4% BEAM	N/A (vector DB)
Self-hosted	✅ Yes	✅ Optional	✅ Optional	✅ Yes	❌ Enterprise	✅ Yes	✅ Yes
Integration template	✅ Published	❌	❌	❌	❌	❌	❌
Memory architecture	BEAM (3-tier)	Session + facts	OS-virtual context	Peer + reasoning	5-layer stack	Episodic + semantic	Vector store only
Purpose	Full memory system	Memory API	Agent runtime	Managed memory	Consumer + agent	Research memory	Vector database

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Security & Privacy Model

You are solely responsible for the content stored in Mnemosyne. Mnemosyne Sync supports optional client-side encryption. When disabled, memory content travels over TLS and is stored according to your infrastructure's security settings.

Feature	Mnemosyne	Detail
Local-first by default	✅	No data ever leaves your machine unless you enable sync
No telemetry	✅	Zero tracking, zero analytics, zero cloud dependency
Optional sync	✅	Bidirectional delta sync between desktop and VPS
Client-side encryption (sync)	✅	Authenticated encryption via Fernet (AES-128-CBC) or PyNaCl SecretBox (XSalsa20-Poly1305). Key never leaves your machine.
BYOK / data-at-rest	✅	Via OS keychain, env vars, or passphrase-derived keys
Self-hostable	✅	Docker, bare metal, Fly.io -- you control the infrastructure
TLS enforcement	✅	HTTPS in production; point SSL_CERT_FILE at a private CA for self-signed dev certs.

When client-side encryption is enabled, the remote sync server sees only metadata (event IDs, timestamps, operation types, device IDs). Memory content, importance scores, source fields, and vector embeddings are all encrypted before transmission. The server cannot read your memories.

Full documentation: docs/security.md / docs/sync.md

Comparison: Mnemosyne is the only memory system with client-side encryption of sync payloads as a core feature. Zep offers BYOK for data-at-rest but manages the key server-side. Every other system (Mem0, Letta, Honcho, Supermemory) relies solely on self-hosting and TLS for privacy.

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Configuration

Environment Variables

Variable	Default	Description
MNEMOSYNE_DATA_DIR	~/.hermes/mnemosyne/data	Database directory
MNEMOSYNE_VEC_TYPE	int8	Vector compression: float32, int8, or bit
MNEMOSYNE_VEC_WEIGHT	0.5	Vector similarity weight
MNEMOSYNE_FTS_WEIGHT	0.3	FTS5 keyword weight
MNEMOSYNE_IMPORTANCE_WEIGHT	0.2	Importance weight
MNEMOSYNE_WM_MAX_ITEMS	10000	Working memory limit
MNEMOSYNE_RECENCY_HALFLIFE	168	Decay halflife in hours
MNEMOSYNE_CONTEXT_INCLUDE_CONSOLIDATED	(unset)	Include consolidated working-memory rows in get_context() prompt injection. Default: excluded. Truthy values: 1, true, yes, on. Does not affect recall().

| MNEMOSYNE_EMBEDDING_API_URL | ${OPENROUTER_BASE_URL:-https://openrouter.ai/api/v1} | Preferred name for custom embedding API endpoint (OpenAI-compatible). Falls back to OPENROUTER_BASE_URL. | | MNEMOSYNE_EMBEDDING_API_KEY | ${OPENROUTER_API_KEY:-${OPENAI_API_KEY:-}} | Preferred name for embedding API key. Falls back to OPENROUTER_API_KEY, then OPENAI_API_KEY. | | MNEMOSYNE_EMBEDDING_MODEL | BAAI/bge-small-en-v1.5 | Embedding model. Low-resource multilingual: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2; larger options: intfloat/multilingual-e5-base, BAAI/bge-m3. |

Full reference: docs/configuration.md

Language Support

Default embeddings are English-optimized (bge-small-en-v1.5). For non-English or multilingual recall, swap the model:

# Low-resource local multilingual embeddings
export MNEMOSYNE_EMBEDDING_MODEL=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

# Larger multilingual embeddings
export MNEMOSYNE_EMBEDDING_MODEL=intfloat/multilingual-e5-base

# Or Chinese-specific embeddings
export MNEMOSYNE_EMBEDDING_MODEL=BAAI/bge-small-zh-v1.5

See docs/configuration.md#custom-embedding-models for tradeoffs (RAM, speed, dimension changes).

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Hermes Plugin

When used with Hermes Agent, Mnemosyne exposes provider tools for the memory lifecycle, lifecycle hooks for automatic context injection, and MCP support.

For the full Hermes setup guide, see docs/hermes-integration.md. That is the canonical, most up-to-date reference.

Install profile comparison

Profile	When to use	RAM	Key tradeoff
mnemosyne-memory (core)	Low-resource (Raspberry Pi, 1 GB VPS), or when using a remote embedding API	~50 MB	No local embeddings. Point MNEMOSYNE_EMBEDDING_API_URL to an external endpoint.
mnemosyne-memory[embeddings]	Mid-range systems with local embedding support	~800 MB	Adds fastembed for local vector generation. Best for single-user desktop agents.
mnemosyne-memory[all]	Full-featured -- local embeddings + local LLM consolidation	~1.5 GB	Adds sentence-transformers + local LLM deps (ctransformers). Maximum capability.
mnemosyne-hermes	Hermes Agent users -- always pair with one of the above	Same as base	Wraps core library with plugin manifest + entry points. Run hermes config set memory.provider mnemosyne after install.

Hardware guidance: Core alone runs on a Raspberry Pi 4 (4 GB) with ~300 MB free for LLM. [embeddings] needs at least 2 GB free RAM. [all] recommends 8 GB+.

Install (Hermes users):

source ~/.hermes/hermes-agent/venv/bin/activate
python -m ensurepip --upgrade
python -m pip install --upgrade pip
python -m pip install mnemosyne-hermes
mkdir -p ~/.hermes/plugins/mnemosyne
ln -sfn "$(~/.hermes/hermes-agent/venv/bin/python -c 'import pathlib, mnemosyne_hermes; print(pathlib.Path(mnemosyne_hermes.__file__).resolve().parent)')"/* ~/.hermes/plugins/mnemosyne/
hermes config set memory.provider mnemosyne

After installing, verify the provider in the active Hermes profile and start a new session or restart the gateway:

hermes memory status

Do not use hermes tools disable memory: that disables the memory toolset, including provider tools. In current Hermes versions, built-in memory and an external provider are separate mechanisms; hermes memory off disables the external provider only. Keep existing built-in memory as a rollback/reference point during a transition and confirm the active provider with hermes memory status.

See docs/hermes-integration.md for the full setup guide.

Tool discovery

The provider tool inventory is version-specific. Confirm the active provider with hermes memory status, then inspect the runtime tool surface:

hermes tools list | grep mnemosyne_

Mnemosyne exposes memory, knowledge-graph, multi-agent-surface, working-note, and operational tools. Treat the runtime list as authoritative. The installer or wrapper registers the plugin manifest under $HERMES_HOME/plugins/mnemosyne, where Hermes discovers it.

Updating: For the persistent side-venv wrapper path, use the side venv rather than a bare pip:

export HERMES_HOME=/opt/data  # Replace with the active Hermes home
"$HERMES_HOME/.mnemosyne/venv/bin/python" -m pip install --upgrade 'mnemosyne-memory[embeddings]' mnemosyne-hermes
hermes gateway restart

For a direct or source install, use pip install --upgrade mnemosyne-hermes && hermes gateway restart or git pull && pip install --upgrade integrations/hermes && hermes gateway restart (source).

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Mnemosyne Sync

Bidirectional, delta-based memory sync between Mnemosyne instances. Designed for desktop-to-VPS sync, team collaboration, and backup.

Key features:

• Delta/change-based protocol -- only transfers changes since last sync
• Bidirectional, push-only, or pull-only modes
• Optional client-side payload encryption (Fernet, or PyNaCl SecretBox XSalsa20-Poly1305)
• API key and JWT authentication
• Timeline + importance conflict resolution
• Append-only event log for auditability

# Start a sync server on your VPS
mnemosyne sync-serve --port 8765 --api-key "your-secret-key"

# On your local machine, sync bidirectionally
mnemosyne sync --remote https://my-vps:8765

# With client-side encryption
export MNEMOSYNE_SYNC_KEY=$(mnemosyne sync-generate-key)
mnemosyne sync --remote https://my-vps:8765 --encrypt

# Check sync status
mnemosyne sync-status --remote https://my-vps:8765

When encryption is enabled, the remote server sees only metadata (event IDs, timestamps, operation types). Memory content is encrypted before leaving your machine and can only be decrypted with your key.

Full documentation: docs/sync.md / docs/security.md

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Contributing

See CONTRIBUTING.md for guidelines.

Full docs: docs/ . Changelog: CHANGELOG.md . Releases: GitHub Releases . Integrations: docs/integrations/

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Sponsors

Mnemosyne development is supported by companies providing compute credits (LLM, embedding, GPU), hosting, and developer tooling. See the full list and sponsorship policy at mnemosyne.site/partners.

Compute Partners

Atlas Cloud -- a full-modal AI inference platform: one API for 300+ curated LLM, image, and video models, so you connect once instead of maintaining a vendor integration per modality. Their OpenAI-compatible endpoint works with Mnemosyne and the Hermes plugin out of the box.

Atlas Cloud provides inference credits used for nightly recall benchmarks, multi-model parity tests, and OpenAI-compatible provider coverage. Budget-friendly API access is available through their coding plan.

Sponsored. Compute Partners are disclosed material connections under FTC Endorsement Guides (16 CFR Part 255). Sponsors have no editorial control over benchmark methodology or results.

Partner with us

If your company runs an OSS credits program and wants to partner, open an issue tagged sponsorship or email [e-mail].

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Support

Discord: Join the Mnemosyne community . Issues: GitHub Issues

Star the repo if you find it useful!

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License

MIT License -- See LICENSE

Copyright (c) 2026 Abdias J

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"The faintest ink is more powerful than the strongest memory." -- Hermes Trismegistus