Supermemory

Long-term agent memory with atomic fact extraction, relational versioning, semantic search, and entity profiles. Extracts facts from conversations, tracks how knowledge changes over time (updates/contradicts/extends), and provides instant recall across sessions and agents. Local-first (SQLite + on-device embeddings).

Long-term memory for AI agents. Extracts atomic facts from text, tracks relations between memories (updates, contradicts, extends), embeds locally for semantic search, and auto-builds entity profiles.

Setup

bash

pip install openclaw-supermemory[local]
supermemory init        # creates ~/.supermemory/memory.db
supermemory serve       # starts API on :8642

Requires an LLM API key for fact extraction (default: Anthropic Haiku).

bash

export ANTHROPIC_API_KEY=sk-...
# or configure via ~/.supermemory/config.yaml

Commands

Ingest (extract facts from text)

bash

supermemory ingest "The project deadline moved to April 15. Sarah replaced Tom as lead." \
  --session meeting-notes --agent kit

LLM extracts atomic facts, categorizes them (person, decision, event, insight, preference, project), detects entities, and finds relations to existing memories. When a fact updates an existing one, the old memory is marked superseded.

Search

bash

supermemory search "project deadline" --top-k 10
supermemory search "project deadline" --all          # include superseded
supermemory search "project deadline" --as-of 2026-03-01  # time travel

Entity operations

bash

supermemory stats                # counts, categories
supermemory history Sarah        # version timeline
supermemory profile Sarah        # auto-built entity profile

API

text

GET  /api/health                 # status + memory count
POST /api/search                 # {"query": "...", "top_k": 10}
POST /api/ingest                 # {"text": "...", "session_id": "..."}
GET  /api/entities               # all known entities
GET  /api/entity/{name}          # entity memories + profile
POST /api/search_entities        # entity-aware cross-session search
POST /api/aggregate              # count/sum queries over event clusters

Search latency: ~32ms warm, ~8s cold start (embedding model load).

Agent integration

Recall at session start

Inject relevant context before the agent processes a message:

bash

supermemory search "current projects and priorities" --top-k 5

Auto-ingest from responses

After meaningful agent turns, extract and store facts:

bash

supermemory ingest "$RESPONSE_TEXT" --session $SESSION --agent $AGENT_ID

OpenClaw plugin (zero-config)

Install the supermemory-claw plugin for automatic memory injection and extraction with no agent code changes.

Architecture

• Storage: SQLite with WAL mode (concurrent reads, single writer)
• Embeddings: Local sentence-transformers (free, on-device) or API (OpenAI/Cohere/Voyage via litellm)
• Extraction: LLM-based atomic fact extraction with relation detection (default: Haiku)
• Entity system: Join tables, aliases, auto-merged profiles across sources
• Multi-agent: Single DB with agent_id tagging, cross-agent semantic search

Cost

~$0.01-0.02 per ingest (3 LLM calls: extract, relate, profile). Search is free (local embeddings).

Links

• PyPI
• GitHub
• Plugin