mnemo-agent 0.3.1

Local-first agent memory CLI — dump, diff, migrate & query across Mem0, Letta, and more

🧠 mnemo

Local-first agent memory CLI — dump, diff, migrate, and query memories across Mem0, Letta, and your local filesystem.

Agents are finally getting good long‑term memory, but every framework (Mem0, Letta, Supermemory, custom Postgres) stores it differently. mnemo is a git‑like CLI for agent memory: you can dump, diff, migrate, and query what your agents know, all from your terminal, using a simple normalized schema and local‑first files. It’s designed for developers who want to own their agent’s “brain” instead of locking it into a single vendor.

Inspired by Mnemosyne (Greek goddess of memory), mnemo is a portable CLI for managing agent memory: capture facts, version-control dumps, compare snapshots, and sync to cloud memory providers — all from your terminal.

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Features

• 15 CLI commands with rich --help and tab-completion
• Normalized schema — facts with {entity, attribute, value, source, timestamp, confidence, metadata.tags}
• Multi-provider — local JSON, Mem0, Letta (stubs → real APIs with optional deps)
• TF-IDF search — mnemo recall "query" with zero external ML deps, filterable by --tag
• Rich tables — confidence color-coded (🟢 ≥0.8, 🟡 ≥0.5, 🔴 <0.5)
• HTML + graph diffs — visual diff between dump snapshots
• MCP server — JSON-RPC 2.0 + stdio transport; plug directly into Claude Desktop, Cursor, or any MCP client
• Push/pull sync — S3, Cloudflare R2, or local filesystem remote; timestamp-based merge
• Safe writes — --dry-run on load, pull, and migrate

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Quick Start

# Install
pip install mnemo-agent              # core (local only)
pip install "mnemo-agent[s3]"       # + S3/R2 push-pull sync
pip install "mnemo-agent[all]"      # everything (mem0 + letta + parquet + graph + s3)

# Initialize Joshua's job-prep agent
mnemo init --agent job-prep

# Add facts manually (entity defaults to agent name, --tag is repeatable)
mnemo add --fact "Joshua uses React, Node, Supabase, Vercel" --agent job-prep
mnemo add --fact "Joshua is based in Toronto" --agent job-prep --confidence 1.0
mnemo add --fact "Chose Supabase over Firebase for auth" --agent job-prep --attribute decision --tag decision --tag auth

# View stored memories — plain format shows IDs for retract/edit
mnemo show --agent job-prep
mnemo show --agent job-prep --format plain

# Recall using natural language, optionally filtered by tag
mnemo recall "tech stack" --agent job-prep
mnemo recall "auth" --agent job-prep --tag decision
mnemo search "Supabase database" --agent job-prep --limit 5

# Edit or remove facts by ID (use 'show --format plain' to find IDs)
mnemo retract a1b2c3d4 --agent job-prep
mnemo edit a1b2c3d4 --value "Updated wording" --agent job-prep

# List all agents
mnemo ls --pretty

# Dump to a timestamped file
mnemo dump --agent job-prep

# Load a sample dump
mnemo load --file tests/fixtures/job_prep_sample.json --agent job-prep

# Compare two agents (or two dump files)
mnemo diff --agent-a job-prep --agent-b job-prep-v2
mnemo diff dump1.json dump2.json --html diff_report.html

# Start the MCP server — HTTP mode
mnemo serve --agent job-prep --port 8080
# Or stdio mode (Claude Desktop / Cursor — no port needed)
mnemo serve --agent job-prep --stdio

# Sync to S3 (prompts for credentials on first add)
mnemo remote add origin s3://my-bucket/mnemo --agent job-prep
mnemo push --agent job-prep
mnemo pull --agent job-prep   # merges remote facts into local

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📋 All Commands

Command	Description
mnemo init --agent <name>	Initialize agent directory + config
mnemo add --fact "text" --agent <name>	Add a memory fact (--entity, --attribute, --tag supported)
mnemo dump --agent <name> [--source mem0|letta]	Dump memories to JSON
mnemo load --file dump.json --agent <name>	Load dump into local/Mem0/Letta
mnemo ls [--agent all]	List agents and fact counts
mnemo show --agent <name>	Display agent's latest memories (--format pretty|json|plain)
mnemo diff --agent-a <a> --agent-b <b>	Diff two agents (or diff a.json b.json)
mnemo recall "query" [--tag <tag>]	TF-IDF search across all agents, optional tag filter
mnemo search "query" [--limit 10] [--tag <tag>]	Alias for recall with higher default limit
mnemo retract <fact-id> --agent <name>	Remove a fact by ID or 8-char prefix
mnemo edit <fact-id> --agent <name>	Edit value/attribute/confidence of an existing fact
mnemo migrate --dump f.json --target mem0 --agent name	Migrate between providers
mnemo serve --agent <name> [--port 8080] [--stdio] [--read-only]	MCP server — HTTP (JSON-RPC 2.0) or stdio for Claude Desktop / Cursor
mnemo remote add <name> <url> --agent <name>	Add a named remote (s3://, r2://, file://)
mnemo remote list --agent <name>	List configured remotes
mnemo remote remove <name> --agent <name>	Remove a remote
mnemo push [--remote origin] --agent <name>	Push local memory to remote
mnemo pull [--remote origin] --agent <name>	Pull and merge remote memory into local

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Project Structure

mnemo-agent/
├── src/mnemo/
│   ├── __init__.py          # version
│   ├── cli.py               # Click CLI (all commands)
│   ├── models.py            # Pydantic: Fact, AgentDump, MnemoConfig
│   ├── storage.py           # Local file I/O (JSON, YAML, credentials)
│   ├── search.py            # TF-IDF search + diff engine
│   ├── remotes.py           # Push/pull backends: FileBackend, S3Backend
│   ├── server.py            # FastAPI MCP server (HTTP + JSON-RPC 2.0)
│   ├── stdio_server.py      # stdio MCP transport (Claude Desktop / Cursor)
│   └── adapters/
│       ├── mem0_adapter.py  # Mem0 API → normalized facts
│       └── letta_adapter.py # Letta API → normalized facts
├── tests/
│   ├── test_cli.py          # CLI command tests
│   ├── test_remote.py       # Remote backends, merge, push/pull tests
│   ├── test_server.py       # MCP server: JSON-RPC 2.0, tools, stdio transport
│   └── fixtures/
│       └── job_prep_sample.json
├── config.yaml              # Sample agent config
├── pyproject.toml
└── requirements.txt

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Memory Schema

{
  "agent": "job-prep",
  "dump_ts": "2026-03-21T23:00Z",
  "source": "manual",
  "version": "1",
  "facts": [
    {
      "id": "uuid",
      "entity": "Joshua",
      "attribute": "tech_stack",
      "value": "React, Node, Supabase, Vercel",
      "source": "chat|tool|manual|mem0|letta|import",
      "timestamp": "2026-03-21T20:00Z",
      "confidence": 0.95,
      "metadata": {}
    }
  ]
}

Example: Project Memory for advisor-prep

{
  "agent": "advisor-prep",
  "facts": [
    {
      "id": "uuid-1",
      "entity": "advisor-prep-agent",
      "attribute": "project_summary",
      "value": "CLI + agent that helps students prep for advisor meetings using UBC context.",
      "source": "manual",
      "timestamp": "2026-03-22T01:00Z",
      "confidence": 0.9,
      "metadata": { "tags": ["summary", "high-level"] }
    },
    {
      "id": "uuid-2",
      "entity": "advisor-prep-agent",
      "attribute": "decision",
      "value": "Chose Supabase over Firebase for auth due to better Postgres integration.",
      "source": "manual",
      "timestamp": "2026-03-22T01:05Z",
      "confidence": 0.95,
      "metadata": { "tags": ["decision", "auth"], "ticket": "ADR-001" }
    },
    {
      "id": "uuid-3",
      "entity": "advisor-prep-agent",
      "attribute": "stack",
      "value": "Next.js, React, Node, Supabase, Vercel.",
      "source": "manual",
      "timestamp": "2026-03-22T01:10Z",
      "confidence": 1.0,
      "metadata": { "tags": ["stack"] }
    }
  ]
}

---

🔌 MCP Server

mnemo implements the MCP 2024-11-05 spec and supports two transports.

stdio — Claude Desktop / Cursor

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "mnemo-job-prep": {
      "command": "mnemo",
      "args": ["serve", "--agent", "job-prep", "--stdio"]
    }
  }
}

That's it — Claude Desktop will spawn mnemo as a subprocess and communicate over stdin/stdout.

HTTP — REST + JSON-RPC 2.0

mnemo serve --agent job-prep --port 8080

Endpoint	Description
POST /	JSON-RPC 2.0 — initialize, tools/list, tools/call, ping
GET /mcp/list_tools	List available tools (legacy, kept for compatibility)
POST /mcp/call_tool	Call a tool by name (legacy, kept for compatibility)
GET /facts	REST: list all facts (?entity=, ?attribute=, ?tag=)
GET /search?q=query	REST: search memories (?tag= filter supported)
GET /health	Health check with version info
GET /docs	Swagger UI

Available MCP tools

Tool	Description
search_memory	TF-IDF keyword search; supports tag filter
list_facts	List all facts; filterable by entity, attribute, tag; shows IDs
upsert_fact	Add a fact; supports tags array
retract_fact	Remove a fact by ID or 8-char prefix
edit_fact	Update a fact's value, attribute, or confidence
get_agent_info	Agent name, fact count, last updated timestamp

retract_fact and edit_fact are disabled when --read-only is set.

---

⚙️ Configuration

Each agent has ~/.mnemo/<agent>/config.yaml:

agent: job-prep
default_source: local
default_target: local
mem0_api_key: null          # https://app.mem0.ai
mem0_user_id: joshua
letta_base_url: http://localhost:8283
letta_agent_id: null        # from your Letta agent
tags: [job-prep, interview]
notes: Memory store for interview prep agent
remotes:
  origin: s3://my-bucket/mnemo

Remote credentials (S3/R2 access keys) are stored separately in ~/.mnemo/credentials with chmod 600. They are populated automatically when you run mnemo remote add — you will be prompted for them interactively. Pass --no-creds to skip prompting and rely on the standard boto3 credential chain (AWS_ACCESS_KEY_ID env var, ~/.aws/credentials, IAM role).

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Environment Variables

Variable	Description
MNEMO_AGENT	Default agent name (skips --agent flag)
MNEMO_DIR	Override base directory (default: ~/.mnemo)
AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY	S3 credentials (alternative to prompting)
R2_ACCOUNT_ID	Cloudflare R2 account ID (alternative to prompting)

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Tests

pip install "mnemo-agent[dev,s3]"
pytest tests/ -v

114 tests across test_cli.py, test_remote.py, and test_server.py.

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Roadmap

• Push/pull sync to S3, R2, and local filesystem remotes
• Write-time conflict detection with overwrite / keep-both / abort prompt
• Full MCP 2024-11-05 protocol — JSON-RPC 2.0 + stdio transport (Claude Desktop / Cursor)
• Vector embeddings for semantic search (v2)
• Parquet export for analytics
• mnemo audit — fact provenance trace
• Web UI dashboard

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Example Use Case: job-prep Agent

# Bootstrap your interview prep memory
mnemo init --agent job-prep
mnemo load --file tests/fixtures/job_prep_sample.json --agent job-prep

# Connect to Claude Desktop (add to claude_desktop_config.json, then restart)
mnemo serve --agent job-prep --stdio

# Or run as an HTTP server for other MCP clients
mnemo serve --agent job-prep --port 8080

# After a practice interview, add what you learned
mnemo add --fact "Lead with Supabase migration story at FAANG interviews" \
  --agent job-prep --attribute interview_tip --confidence 0.9 --tag tip

# If you added a conflicting fact by mistake, retract it by ID prefix
mnemo show --agent job-prep --format plain   # see IDs
mnemo retract a1b2c3d4 --agent job-prep

# Before next session, recall relevant context
mnemo recall "React Supabase full-stack" --agent job-prep

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License

MIT © Joshua Ndala