Memento

Any model, same memory. A bitemporal knowledge graph (tracking when facts were true vs. when they were learned) that gives AI agents persistent, structured memory across LLM providers, clients, and conversations.

Most AI memory systems dump text into a vector store and retrieve by similarity. Memento builds a knowledge graph that resolves entities, detects contradictions, tracks time, and composes answers from structured relationships rather than raw chunks.

Works with any MCP-compatible client (Claude Desktop, Cursor, Claude Code, Cline, Windsurf, OpenClaw, Continue.dev) and any LLM backend (Claude, GPT, Gemini, Llama, Mistral, Ollama, or any OpenAI-compatible endpoint).

90.8% overall accuracy, 92.2% task average on LongMemEval (500 questions, GPT-4o judge) — a benchmark for long-term conversational memory covering temporal reasoning, knowledge updates, multi-session recall, and preference tracking.

Quick Start

MCP Server

pip install memento-memory[anthropic]
export ANTHROPIC_API_KEY=your-key
memento-mcp

Add to your MCP client config (e.g., Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "memento": {
      "command": "memento-mcp",
      "env": { "ANTHROPIC_API_KEY": "your-key" }
    }
  }
}

That's it. The agent now has persistent memory and calls memory_ingest to store facts and memory_recall to retrieve them. Every MCP client on the same machine shares the same knowledge graph.

Python API

from memento import MemoryStore

store = MemoryStore()

# Ingest — extracts entities, resolves against the graph, detects contradictions
store.ingest("John Smith is VP of Sales at Alpha Corp.")
store.ingest("Alpha Corp is acquiring Beta Inc.")

# Recall — graph traversal + ranking + context budgeting
memory = store.recall("What should I know about John?")
print(memory.text)
# ## John Smith (person)
# - title: VP of Sales
# - → [works_at] Alpha Corp
#
# ## Alpha Corp (organization)
# - → [acquiring] Beta Inc

# Point-in-time queries
memory = store.recall("Where was John in January?", as_of="2025-01-31T00:00:00Z")

# Direct manipulation
store.correct(entity_id, "title", "VP of Sales", reason="Promoted")
store.forget(entity_id=entity_id)
store.merge(entity_a_id, entity_b_id)

# Introspection
conflicts = store.conflicts()
health = store.health()
entities = store.entity_list()

# Privacy
export = store.export_entity_data(entity_id)
chain = store.audit_belief(entity_id, "title")
receipt = store.hard_delete(entity_id)

# Consolidation
store.consolidate()

# Session tracking (scratchpad with coreference)
session = store.start_session()
session.on_turn("I met John Smith today.")
session.on_turn("He mentioned a new project.")
session.end()  # Flushes through ingestion pipeline

LLM Providers

Memento is provider-agnostic. Swap the backend via config — no code changes.

How It Works

Agent / LLM
  │ query              │ ingest
  ▼                    ▼
Retrieval Engine    Ingestion Pipeline
  │                    │
  ▼                    ▼
Bitemporal Knowledge Graph (SQLite)
  │
  ├── Consolidation Engine (decay, dedup, prune)
  ├── Verbatim Fallback (FTS5 + vector search)
  └── Privacy Layer (export, audit, hard delete)

• Entity resolution — "John," "John Smith," and "the Alpha guy" become one node. Tiered matching: exact/fuzzy/phonetic (cheap) before embedding similarity and LLM tiebreaker (expensive).
• Contradiction detection — flags when new facts conflict with existing ones
• Bitemporal model — every fact tracks when it was true (valid time) and when the system learned it (transaction time)
• Immutable history — facts are never deleted, only superseded. Full audit trail.
• Verbatim fallback — raw text stored alongside the graph, so extraction loss doesn't mean information loss
• Compositional retrieval — "What should I know before my meeting with John?" traverses the graph, not just retrieves chunks
• Confidence decay — multiplicative decay prevents artificial confidence floors from repeated confirmations
• Consolidation — background engine decays stale info, merges duplicates, prunes orphans

Benchmarks

90.8% overall accuracy on LongMemEval (500 questions across 6 categories):

Full methodology and reproduction steps: BENCHMARKS.md

CLI

Admin and introspection tools for the knowledge graph:

memento entities                        # List all entities
memento entity <id>                     # Show entity details
memento history <id> <key>              # Property history over time
memento snapshot <id> --as-of 2025-06   # Point-in-time view
memento stats                           # Graph statistics
memento merge <id_a> <id_b>             # Merge two entities
memento consolidate                     # Run maintenance pass
memento export <id>                     # GDPR data export (JSON)
memento audit <id> <key>                # Trace a belief to its source
memento delete <id> --hard              # Hard delete with receipt

Configuration

License

MIT