| name | engram |
| description | Persistent AI memory backend with semantic search and knowledge graph. Stores episodic, semantic, and procedural memories in a local SQLite database and retrieves the most relevant ones using vector similarity and graph traversal. Use when you need to remember information across sessions, recall past decisions, store facts about a project, detect contradictions between beliefs, or give any AI model a persistent growing brain. All embeddings run locally - no API keys required. |
| license | MIT |
| compatibility | Requires Node.js 22+ and npm. Works on macOS, Linux, and Windows. |
| metadata | {"author":"ayvazyan10","version":"0.1.3","homepage":"https://engram.am"} |
Engram AI Memory
Universal AI memory backend — give any AI model persistent, structured memory that survives sessions, systems, and restarts.
Tools (21 total)
| Category | Tools |
|---|
| Memory | store_memory, search_memory, recall_context, add_knowledge, forget |
| Stats | memory_stats, index_status, embedding_status |
| Lifecycle | decay_sweep, decay_policy, re_embed |
| Contradictions | check_contradictions, resolve_contradiction |
| Tags | list_tags, tag_memory |
| Webhooks | webhook_subscribe, webhook_list |
| Plugins | plugin_list |
| Reflection | request_reflection, store_reflection, get_reflections |
Recommended workflow
Session start → recall_context("current task or question")
During session → store_memory("decision or finding", type="episodic")
Session end → store_memory("session summary", importance=0.8)
Key tools
recall_context — primary recall tool. Assembles the most relevant memories for a query and returns formatted context ready to inject into prompts. Call at the start of every session.store_memory — saves episodic events, semantic facts (type="semantic", concept="...") or procedural patterns (type="procedural").search_memory — semantic similarity search with optional type filter and threshold.check_contradictions — detects memories that conflict with a given memory ID.resolve_contradiction — resolves conflicts via strategy: keep_newest, keep_oldest, keep_important, keep_both, or manual.decay_sweep — runs Ebbinghaus forgetting curve decay; archives stale memories and consolidates old episodes into facts.
Configuration
| Variable | Default | Description |
|---|
ENGRAM_DB_PATH | ~/.engram/engram.db | SQLite database path |
ENGRAM_NAMESPACE | (global) | Isolate memories per project |
ENGRAM_EMBEDDING_MODEL | Xenova/all-MiniLM-L6-v2 | Local ONNX embedding model |
Links
