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Mnemon

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LLM-supervised persistent memory for AI agents.

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LLM agents forget everything between sessions. Context compaction drops critical decisions, cross-session knowledge vanishes, and long conversations push early information out of the window.

Mnemon gives your agent persistent, cross-session memory — a four-graph knowledge store with intent-aware recall, importance decay, and automatic deduplication. The mnemon memory path remains one local binary with zero API keys and one setup command.

Mnemon ships one executable with two separate surfaces. Memory stays at the mnemon root; Agency Preview lives at mnemon agency ... and adds durable, project-local responsibility and effect admission to an existing Pi agent. Agency does not replace Memory or the Agent Runtime.

Claude Max / Pro subscriber? Mnemon works entirely through your existing subscription — no separate API key required. Your LLM subscription is the intelligence layer. Two commands and you're done.

Why Mnemon?

Most memory tools embed their own LLM inside the pipeline. Mnemon takes a different approach: your host LLM is the supervisor. The binary handles deterministic computation (storage, graph indexing, search, decay); the LLM makes judgment calls (what to remember, how to link, when to forget). No middleman, no extra inference cost.

Pattern	LLM Role	Representative
LLM-Embedded	Executor inside the pipeline	Mem0, Letta
File Injection	None — reads file at session start	Claude Code Memory
MCP Server	Tool provider via MCP protocol	claude-mem
LLM-Supervised	External supervisor of a standalone binary	Mnemon

Mnemon also addresses a gap in the protocol stack. MCP standardizes how LLMs discover and invoke tools. ODBC/JDBC standardizes how applications access databases. But how LLMs interact with databases using memory semantics — this layer has no protocol. Mnemon's three primitives — remember, link, recall — form an intent-native protocol: command names map to the LLM's cognitive vocabulary (remember not INSERT, recall not SELECT), and output is structured JSON with signal transparency rather than raw database rows.

<p align="center"> <img src="docs/diagrams/llm-supervised-concept.jpg" width="720" alt="LLM-Supervised Architecture — three patterns compared, with Mnemon hooks, protocol boundary, and deterministic memory engine" /> <br /> _{The LLM-Supervised pattern: hooks drive the lifecycle, the host LLM makes judgment calls, the binary handles deterministic computation.} </p>

Memory has a compound interest effect — the longer it accumulates, the greater its value. LLM engines iterate constantly, skill files cost nearly nothing to write, but memory is a private asset that grows with the user. It is the only component in the agent ecosystem worth deep investment.

<p align="center"> <img src="docs/diagrams/10-knowledge-graph.jpg" width="720" alt="Knowledge Graph — 87 insights connected by temporal, entity, semantic, and causal edges" /> <br /> _{A real knowledge graph built by Mnemon — 87 insights, 2150 edges across four graph types.} </p>

See Design & Architecture for details.

Quick Start

Install

npm (recommended; macOS / Linux / Windows, Node.js 22+):

npm install --global @mnemon-dev/mnemon

Upgrade the npm-managed CLI at any time:

mnemon update

The npm package installs the matching native Go executable for the host OS and CPU. Mnemon's engine remains a single native binary; Node.js is used only by the npm launcher and package manager.

Alternative installers:

brew install --cask mnemon-dev/tap/mnemon
go install github.com/mnemon-dev/mnemon@latest

Homebrew, go install, source builds, and other Node package managers must continue to use their original installation method. To migrate one of these installations, run the npm install command once and ensure the npm global bin directory precedes the old executable on PATH; subsequent mnemon update calls are npm-managed.

Windows supports the core Memory commands. Agency remains unavailable on Windows until its local authority boundary has native Windows security.

From source (macOS / Linux):

git clone https://github.com/mnemon-dev/mnemon.git && cd mnemon
make install

Verify installation:

mnemon --version
mnemon agency --version

Agency (Preview · Pi-first)

mnemon agency setup --runtime pi --project-root .

Set up each project once, then use Pi normally. Agency is available on macOS and Linux and remains independent from Memory: mnemon setup --target pi --yes enables Memory, while the command above enables Agency. See the Agency guide for its operating model, Preview compatibility boundary, and optional peers.

Claude Code

mnemon setup

mnemon setup auto-detects Claude Code, then interactively deploys skill, hooks, and behavioral guide. Start a new session — memory just works.

Codex

mnemon setup --target codex --yes

One command deploys the mnemon skill, prompt files, and Codex lifecycle hooks (SessionStart, UserPromptSubmit, Stop) in .codex/hooks.json.

Cursor

mnemon setup --target cursor --yes

One command deploys the mnemon skill, prompt files, and Cursor lifecycle hooks to .cursor/. The integration primes new agent sessions with Mnemon guidance and memory status, then nudges for durable-memory writeback after responses.

ZCode

mnemon setup --target zcode --global --yes

ZCode installs the Mnemon skill under ~/.zcode/skills/ and registers user-level lifecycle hooks in ~/.zcode/cli/config.json. The hooks prime new sessions, add recall guidance before model calls, and prompt for durable-memory writeback at stop. Without --global, setup installs only the project skill; ZCode currently ignores project-level hook configuration.

MiniMax Code

mnemon setup --target minimax-code --yes

One command deploys the Mnemon skill to .minimax/skills/mnemon/SKILL.md. Add --global to use ~/.minimax/skills/mnemon/SKILL.md across projects. Current MiniMax Code releases discover both roots natively. The integration is intentionally skill-only: in MiniMax Code 3.0.65, the local Agent V2 path does not dispatch the user-prompt lifecycle hook required for dependable automatic recall.

TRAE (TRAE Work)

mnemon setup --target trae --yes

One command deploys the mnemon skill, prompt files, and TRAE native hooks for both TRAE IDE and TRAE Work to .trae/. The integration uses SessionStart, UserPromptSubmit, and Stop hooks in .trae/hooks.json.

Qoder (QoderWork)

mnemon setup --target qoder --yes
mnemon setup --target qoderwork --yes

Qoder deploys the mnemon skill, prompt files, and native hooks to .qoder/ or ~/.qoder/. QoderWork uses its native user config at ~/.qoderwork/. Both integrations register SessionStart, UserPromptSubmit, and Stop hooks in settings.json.

CodeBuddy

mnemon setup --target codebuddy --yes

CodeBuddy deploys the mnemon skill, prompt files, and native hooks to .codebuddy/ or ~/.codebuddy/. The integration registers SessionStart, UserPromptSubmit, and Stop hooks in settings.json.

WorkBuddy

mnemon setup --target workbuddy --yes

WorkBuddy deploys the mnemon skill, prompt files, and native hooks to .workbuddy/ or ~/.workbuddy/. The integration registers SessionStart, UserPromptSubmit, and Stop hooks in settings.json.

Kimi Code

mnemon setup --target kimi --yes

Kimi Code deploys the mnemon skill, prompt files, and native lifecycle hooks to ~/.kimi-code/ or $KIMI_CODE_HOME/. The integration registers SessionStart, UserPromptSubmit, and Stop hooks in config.toml.

OpenCode

mnemon setup --target opencode --yes

OpenCode deploys the mnemon skill to .opencode/skills/, registers the generated guide through opencode.json instructions, and installs a native plugin in .opencode/plugins/. The plugin injects recall context before chat requests and adds Mnemon guidance to session compaction.

OpenClaw

mnemon setup --target openclaw --yes

One command deploys skill, hook, plugin, and behavioral guide to ~/.openclaw/. Restart the OpenClaw gateway to activate.

Pi

mnemon setup --target pi --yes

One command deploys the mnemon skill, prompt files, and a Pi TypeScript extension to .pi/. The extension maps Mnemon's lifecycle reminders onto Pi events (resources_discover, before_agent_start, agent_end, session_before_compact). Start a new Pi session or run /reload to activate.

Hermes Agent

mnemon setup --target hermes --yes

One command deploys the mnemon skill, prompt files, and Hermes shell hooks to ~/.hermes/. The integration uses Hermes' native lifecycle hooks: on_session_start, pre_llm_call, post_llm_call, and optional on_session_finalize. Hermes may prompt once to approve the installed shell hooks.

DeepSeek Harness

DeepSeek Harness (DSH) integrates through the dsh-mnemon plugin, which layers DSH's runtime memory, managed project documents, and Mnemon's long-term memory spaces into one supervised three-tier memory system.

With mnemon installed on the host (see Install), add the plugin and restart your DSH Web profile:

dsh plugin --profile web add dsh-mnemon
dsh --profile web

The Mnemon repository is also a direct GitHub installation source. Unreleased plugin builds can still be installed from the dedicated repository, and local development checkouts use an absolute path:

dsh plugin --profile web add github:mnemon-dev/mnemon
dsh plugin --profile web add "github:omdsh-dev/dsh-mnemon"
dsh plugin --profile web add "link:/absolute/path/to/dsh-mnemon"

New installations from the Mnemon repository resolve the latest npm release of dsh-mnemon, so publishing a new plugin release does not require a matching change in this repository. Existing installations remain on their resolved version until the plugin is reinstalled or updated.

Then open DSH's Settings → Plugin Config → Mnemon to pick a storage scope, and use the Memory System tab in a session to create or activate memory spaces. Recall reads only from active memory spaces; durable writes go through supervised sub-agents.

NanoClaw

NanoClaw runs agents inside Linux containers. Use the /add-mnemon skill to integrate:

1. Install mnemon on the host (see above)
2. In your NanoClaw project, run /add-mnemon — Claude Code will modify the Dockerfile, add a container skill, and set up volume mounts
3. Each WhatsApp group gets its own isolated memory store, with optional global shared memory (read-only)

The skill is available at .claude/skills/add-mnemon/ in the NanoClaw repo.

Nanobot

mnemon setup --target nanobot --global --yes

One command writes a skill file to ~/.nanobot/workspace/skills/mnemon/SKILL.md. Memory is shared across all Nanobot sessions and projects. Use --global (recommended) because Nanobot discovers skills from the global workspace directory.

Uninstall

mnemon setup --eject

How it works

Once set up, Memory operates through lightweight runtime projections: a runtime-specific SKILL.md teaches commands, a shared guide.md (by default ~/.mnemon/prompt/guide.md) carries judgment guidance, and native hooks or extensions surface reminders at supported lifecycle boundaries. The mnemon binary executes deterministic memory operations, while mnemon setup installs the closest native mapping for each supported runtime.

Session starts
    |
    v
  Prime   -> make skill, guide, and active store visible
    |
    v
User prompt arrives
    |
    v
  Remind  -> decide whether recall could change this task
    |
    v
Agent works and calls Mnemon only when useful
    |
    v
  Nudge   -> decide whether durable writeback is justified
    |
    v
Before context compaction
    |
    v
  Compact -> preserve only critical continuity

The four hook phases are reminders, not a hard workflow. Prime makes the skill, guide, and active store visible. Remind prompts a recall decision. Nudge prompts a writeback decision. Compact preserves only critical continuity before context compression.

You don't run mnemon commands yourself. The agent does when the guide says memory is useful.

Features

• Zero user-side operation — install once; supported runtimes can use hooks, minimal runtimes can use persistent rules
• LLM-supervised — the host LLM decides what to remember, update, and forget; no embedded LLM, no API keys
• Multi-framework support — Claude Code, Codex, Cursor, ZCode, TRAE/TRAE Work, Qoder/QoderWork, CodeBuddy, WorkBuddy, Kimi Code, OpenCode, and Hermes Agent (hooks/plugins), OpenClaw (plugins), Pi (extensions), MiniMax Code and Nanobot (skills), DeepSeek Harness (via the dsh-mnemon plugin), and more
• Runtime-native integration — runtime-specific SKILL.md, shared guide.md, and supported hooks or extensions
• Four-graph architecture — temporal, entity, causal, and semantic edges, not just vector similarity
• Intent-native protocol — three primitives (remember, link, recall) map to the LLM's cognitive vocabulary, not database syntax; structured JSON output with signal transparency
• Intent-aware recall — graph traversal + optional vector search (RRF fusion), enabled by default for all queries
• Built-in deduplication — remember auto-detects duplicates and conflicts; skips or auto-replaces
• Retention lifecycle — importance decay, access-count boosting, and garbage collection
• Privacy-safe receipts — export hashed operation receipts for memory-boundary audits without raw memory contents or queries
• Optional embeddings — works fully without an embedding provider; add local Ollama or an OpenAI-compatible server for enhanced vector+keyword hybrid search

Vision

All your local agentic AIs — across sessions and frameworks — sharing one pool of live memory.

  Claude Code ───────┐
                     │
  Codex ─────────────┤
                     │
  Cursor ────────────┤
                     │
  ZCode ─────────────┤
                     │
  MiniMax Code ──────┤
                     │
  TRAE ──────────────┤
                     │
  TRAE Work ─────────┤
                     │
  Qoder ─────────────┤
                     │
  QoderWork ─────────┤
                     │
  CodeBuddy ─────────┤
                     │
  WorkBuddy ─────────┤
                     │
  Kimi Code ─────────┤
                     │
  Hermes Agent ──────┤
                     │
  DeepSeek Harness ──┤
                     │
  OpenClaw ──────────┤
                     │
  Pi ────────────────┤
                     │
  Nanobot ───────────┤
                     │
  NanoClaw ──────────┤
                     ├──▶  ~/.mnemon  ◀── shared memory
  OpenCode ──────────┤
                     │
  Gemini CLI ────────┘

The foundation is in place: a single ~/.mnemon database that any agent can read and write. Claude Code, Codex, Cursor, ZCode, TRAE/TRAE Work, Qoder/QoderWork, CodeBuddy, WorkBuddy, Kimi Code, OpenCode, and Hermes Agent setup automate hook/plugin installation; OpenClaw can use plugin hooks; Pi integrates via native skills and TypeScript lifecycle extensions; MiniMax Code and Nanobot integrate via skill files; NanoClaw integrates via container skills and volume mounts. The same integration bundle can be installed in any LLM CLI that supports skills, rules, system prompts, or event hooks.

The longer-term direction is a memory gateway: protocol decoupled from storage engine. The current SQLite backend is the first adapter; the protocol surface (remember / link / recall) can sit on top of PostgreSQL, Neo4j, or any graph database. Agent-side optimization (when to recall, what to remember) and storage-side optimization (indexing, graph algorithms) evolve independently. See Future Direction for details.

FAQ

Do different sessions share memory? Yes. By default, all sessions use the same default store — a decision remembered in one session is available in every future session.

Can I isolate memory per project or agent? Yes. Use named stores to separate memory:

mnemon store create work        # create a new store
mnemon store set work           # set as default
MNEMON_STORE=work mnemon recall "query"  # or use env var per-process

Different agents/processes can use different stores via the MNEMON_STORE environment variable — no global state contention.

Local or global mode? mnemon setup defaults to local (project-scoped .claude/), recommended for most users. Global (mnemon setup --global, installed to ~/.claude/) activates mnemon across all projects — convenient if you want other frameworks (e.g., OpenClaw) to share memory by forwarding requests through Claude Code CLI, but may add maintenance overhead.

How do I customize the behavior? Edit the generated guideline (~/.mnemon/prompt/guide.md in current setup flows). Skill files should stay focused on command syntax.

What is sub-agent delegation? Sub-agent delegation is optional. When a runtime supports it, the main agent can decide what to remember and ask a cheaper or isolated worker to execute mnemon remember. It is a useful execution strategy, not a required part of the Mnemon architecture.

Configuration

Environment Variable	Default	Description
MNEMON_DATA_DIR	~/.mnemon	Base data directory
MNEMON_STORE	(active file or default)	Named memory store for data isolation

Retention:

Environment Variable	Default	Description
MNEMON_MAX_INSIGHTS	1000	Active-insight ceiling; 0 disables automatic pruning
MNEMON_AUTO_PRUNE_MIN_AGE	24h	Grace period before an insight can be auto-pruned; accepts 24h, 7d, or 0

Each automatic deletion is soft, appears in the oplog as a prune operation, and is reported by ID in the triggering command's auto_pruned_ids field.

Embedding (only relevant if using embeddings):

Environment Variable	Default	Description
MNEMON_EMBED_ENDPOINT	http://localhost:11434	Embedding API endpoint
MNEMON_EMBED_MODEL	nomic-embed-text	Embedding model name
MNEMON_EMBED_PROTOCOL	(auto-detect)	ollama or openai; auto-detected from an endpoint ending in /v1
MNEMON_EMBED_API_KEY	(none)	Bearer token for OpenAI-compatible servers (oMLX, vLLM, etc.)
MNEMON_EMBED_DIMENSIONS	(native)	Optional Matryoshka dimension truncation

The embedding client speaks the Ollama API by default and the OpenAI-compatible embeddings API when the endpoint ends in /v1 (or when MNEMON_EMBED_PROTOCOL=openai is set). OpenAI-compatible servers are normally probed via their models route; servers that do not serve that route (e.g. Voyage AI) are detected via an embeddings round-trip instead. For example, a local server such as oMLX can be configured with:

export MNEMON_EMBED_ENDPOINT=http://127.0.0.1:18000/v1
export MNEMON_EMBED_MODEL=bge-m3-mlx-8bit
export MNEMON_EMBED_API_KEY=sk-... # omit for keyless local servers
mnemon embed --status

A hosted provider such as Voyage AI needs only the endpoint, model, and key:

export MNEMON_EMBED_ENDPOINT=https://api.voyageai.com/v1
export MNEMON_EMBED_MODEL=voyage-3.5
export MNEMON_EMBED_API_KEY=pa-...
mnemon embed --status

Development

make build          # build the single mnemon executable
make install        # build + install to $GOBIN
make test           # run deterministic CI tests
make test-integration  # opt-in CLI E2E and Agency boundary tests
mnemon setup        # interactive setup
mnemon setup --eject  # remove all integrations
make help           # show all targets

Dependencies: Go 1.24+, modernc.org/sqlite, spf13/cobra, google/uuid

See Development and Deployment for Docker, Compose, Ollama embedding, and release setup.

Documentation

• Agency Preview — maturity boundary, Pi setup, operating model, completion semantics, and optional peers
• Go Engineering Standard — maintainability, concurrency, persistence, testing, and review thresholds
• Design & Architecture — current engine architecture, algorithms, integration design
• Memory Usage & Reference — root Memory commands, import, receipts, and embedding support
• Memory Import Guide — schema and LLM prompt for importing historical chats
• Architecture Diagrams — system architecture, pipelines, lifecycle management

Star History

<a href="https://star-history.dera.page/#mnemon-dev/mnemon"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://star-history.dera.page/svg?repos=mnemon-dev/mnemon&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://star-history.dera.page/svg?repos=mnemon-dev/mnemon" /> <img alt="Star History Chart" src="https://star-history.dera.page/svg?repos=mnemon-dev/mnemon" /> </picture> </a>

References

Mnemon combines the paradigm of one paper with the methodology of another, grounded in the structural insight that graph memory is isomorphic to LLM attention. See Theoretical Foundations for details.

• RLM — Zhang, Kraska & Khattab. Recursive Language Models. 2025. Establishes the paradigm: LLMs are more effective as orchestrators of external environments than as direct data processors.
• MAGMA — Zou et al. A Multi-Graph based Agentic Memory Architecture. 2025. Provides the methodology: four-graph model (temporal, entity, causal, semantic) with intent-adaptive retrieval.
• Graph-LLM Structural Insight — Joshi & Zhu. Building Powerful GNNs from Transformers. 2025; and the Graph-based Agent Memory survey (Chang Yang et al., 2026). Confirms that LLM attention is computationally equivalent to GNN operations — graph memory is a structural match, not an engineering convenience.

License

Copyright 2026 Grivn and Mnemon contributors.

Apache-2.0

The bracketed copyright example near the end of LICENSE is part of Apache 2.0's standard application appendix; this section carries the project's actual copyright notice.