engrama 0.13.0

Graph-based long-term memory framework for AI agents (SQLite default, Neo4j optional)

Engrama

Graph-based long-term memory framework for AI agents.

Engrama gives any AI agent persistent, structured memory backed by a knowledge graph. Instead of flat key-value stores or opaque vector databases, Engrama stores entities, observations, and relationships — and lets agents traverse that graph to reason about their accumulated knowledge.

Two backends are first-class:

• SQLite + sqlite-vec (default since 0.9) — single file, zero external services, git clone + uv sync and you're running (Engrama is not yet on PyPI; install from source).
• Neo4j 5.26 LTS (opt-in) — for multi-process production setups, large-scale vector search, or teams that already use Cypher.

The data model is identical on both. See docs/backends.md for a full decision guide; the rest of this README assumes the SQLite default.

Inspired by Karpathy's second-brain concept, but built for agents instead of humans — and with graphs instead of wikis.

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Why graphs?

	Flat JSON / KV	Vector DB	Engrama (Graph)
Relationship queries	❌	❌	✅ native
Scales to 10k+ memories	❌ slow	✅	✅
Works without embeddings	✅	❌	✅ (optional)
Local-first / private	✅	depends	✅
Zero external services	✅	❌	✅ (SQLite)
"What projects use FastMCP?"	full scan	approximate	1-hop traversal

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Prerequisites

You need two things to run on the default SQLite backend. Docker is not required unless you opt into Neo4j.

Requirement	Version	How to check	Install guide
Python	3.11 or newer	python --version	python.org/downloads
uv (Python package manager)	any recent	uv --version	docs.astral.sh/uv

Windows users: after installing Python, make sure "Add Python to PATH" is checked. After installing uv, you may need to restart your terminal.

Optional:

• Obsidian — for vault sync features.
• A local embedder for semantic search.
• Docker Desktop — only if you opt into the Neo4j backend.

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Quick start (SQLite, zero-dep)

Step 1: Clone and install

git clone https://github.com/scops/engrama
cd engrama
uv sync

Step 2: Initialise the schema

uv run engrama init --profile developer

Step 3: Verify

uv run engrama verify

Step 4: Use it

A) From Python:

from engrama import Engrama

with Engrama() as eng:
    eng.remember("Technology", "FastAPI", "High-performance async framework")
    eng.associate("MyProject", "Project", "USES", "FastAPI", "Technology")
    results = eng.search("microservices")

B) From the command line:

uv run engrama search "FastAPI"
uv run engrama reflect

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Quick start (Neo4j, opt-in)

If you need multi-process writes, very large vector indexes, or an existing Cypher toolchain, install with the Neo4j extra:

git clone https://github.com/scops/engrama
cd engrama
uv sync --extra neo4j

Configure your credentials by copying .env.example to .env and setting GRAPH_BACKEND=neo4j. Start Neo4j with docker compose up -d, and then initialize the schema:

uv run engrama init --profile developer
uv run engrama verify

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📚 Full Documentation

All further details, including MCP integration (Claude Desktop), Obsidian sync, Architecture, and the complete API Reference, are available in the official documentation.

👉 Read the Full Documentation