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Engram

Persistent semantic memory layer for AI agents. Local-first storage (SQLite+LanceDB) with Ollama embeddings. Store and recall facts, decisions, preferences, events, relationships across sessions. Supports memory decay, deduplication, typed memories (5 types), memory relationships (7 graph relation types), agent/user scoping, semantic search, context-aware recall, auto-extraction from text (rules/LLM/hybrid), import/export, REST API, MCP protocol. Solves context window and compaction amnesia. Server at localhost:3400, dashboard at /dashboard. Install via npm (engram-memory), requires Ollama with nomic-embed-text model.

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Target platform: OpenClaw
Install method: Manual import
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Prerequisites: OpenClaw
Primary doc: SKILL.md

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Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 0.2.1

Provenance

Publisher: Dannydvm
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 15 sections Open source page

Engram: Persistent Memory for AI Agents

Engram gives you durable semantic memory that survives sessions, compaction, crashes. All local, no cloud, no token cost.

Boot Sequence (MANDATORY)

On every session start, run: engram search "<current task context>" --limit 10 Example: engram search "client onboarding status churn risk" --limit 10 This recalls relevant memories from previous sessions before you start work.

Storing Memories

5 memory types: fact | decision | preference | event | relationship # Facts — objective information engram add "API rate limit is 100 req/min" --type fact --tags api,limits # Decisions — choices made engram add "We chose PostgreSQL over MongoDB for better ACID" --type decision --tags database # Preferences — user/client likes/dislikes engram add "Dr. Steph prefers text over calls" --type preference --tags dr-steph,communication # Events — milestones, dates engram add "Launched v2.0 on January 15, 2026" --type event --tags launch,milestone # Relationships — people, roles, connections engram add "Mia is client manager, reports to Danny" --type relationship --tags team,roles When to store: Client status changes (churn risk, upsell opportunity, complaints) Important decisions made about projects/clients Facts learned during work (credentials, preferences, dates) Milestones completed (onboarding steps, launches)

Searching

Semantic search (finds meaning, not just keywords): # Basic search engram search "database choice" --limit 5 # Filter by type engram search "user preferences" --type preference --limit 10 # Filter by agent (see only your memories + global) engram search "project status" --agent theo --limit 10

Context-Aware Recall

Recall ranks by: semantic similarity × recency × salience × access frequency engram recall "Setting up new client deployment" --limit 10 Better than search when you need the most relevant memories for a specific context.

Memory Relationships

7 relation types: related_to | supports | contradicts | caused_by | supersedes | part_of | references # Manual relation engram relate <memory-id-1> <memory-id-2> --type supports # Auto-detect relations via semantic similarity engram auto-relate <memory-id> # List relations for a memory engram relations <memory-id> Relations boost recall scoring — well-connected memories rank higher.

Auto-Extract from Text

Ingest extracts memories from raw text (rules-based by default, optionally LLM): # From stdin echo "Mia confirmed client is happy. We decided to upsell SEO." | engram ingest # From command engram extract "Sarah joined as CTO last Tuesday. Prefers async communication." Uses memory types, tags, confidence scoring automatically.

Management

# Stats (memory count, types, storage size) engram stats # Export backup engram export -o backup.json # Import backup engram import backup.json # View specific memory engram get <memory-id> # Soft delete (preserves for audit) engram forget <memory-id> --reason "outdated" # Apply decay manually (usually runs daily automatically) engram decay

Memory Decay

Inspired by biological memory: Every memory has salience (0.0 → 1.0) Daily decay: salience *= 0.99 (configurable) Accessing a memory boosts salience Low-salience memories fade from search results Nothing deleted — archived memories can be recovered

Agent Scoping

4 scope levels: global → agent → user → session By default: Agents see their own memories + global memories --agent <agentId> filters to specific agent Scope isolation prevents memory bleed between agents

REST API

Server runs at http://localhost:3400 (start with engram serve). # Add memory curl -X POST http://localhost:3400/api/memories \ -H "Content-Type: application/json" \ -d '{"content": "...", "type": "fact", "tags": ["x","y"]}' # Search curl "http://localhost:3400/api/memories/search?q=query&limit=5" # Recall with context curl -X POST http://localhost:3400/api/recall \ -H "Content-Type: application/json" \ -d '{"context": "...", "limit": 10}' # Stats curl http://localhost:3400/api/stats Dashboard: http://localhost:3400/dashboard (visual search, browse, delete, export)

MCP Integration

Engram works as an MCP server. Add to your MCP client config: { "mcpServers": { "engram": { "command": "engram-mcp" } } } MCP tools: engram_add, engram_search, engram_recall, engram_forget

Configuration

~/.engram/config.yaml: storage: path: ~/.engram embeddings: provider: ollama # or "openai" model: nomic-embed-text ollama_url: http://localhost:11434 server: port: 3400 host: localhost decay: enabled: true rate: 0.99 # 1% decay per day archive_threshold: 0.1 dedup: enabled: true threshold: 0.95 # cosine similarity for dedup

Best Practices

Boot with recall — Always engram search "<context>" --limit 10 at session start Type everything — Use correct memory types for better recall ranking Tag generously — Tags enable filtering and cross-referencing Ingest conversations — Use engram ingest after important exchanges Let decay work — Don't store trivial facts; let important memories naturally stay salient Use relations — auto-relate after adding interconnected memories Scope by agent — Keep agent memories separate for clean context

Troubleshooting

Server not running? engram serve & # or install as daemon: see ~/.engram/daemon/install.sh Embeddings failing? ollama pull nomic-embed-text curl http://localhost:11434/api/tags # verify Ollama running Want to reset? rm -rf ~/.engram/memories.db ~/.engram/vectors.lance engram serve # rebuilds from scratch Created by: Danny Veiga (@dannyveigatx) Source: https://github.com/Dannydvm/engram-memory Docs: https://github.com/Dannydvm/engram-memory/blob/main/README.md

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SKILL.md Primary doc