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A memory graph designed for the agent that uses it, not the human who feeds it. Engrama reconstructs context from associations on demand, replacing the "stuff everything into the prompt" reflex with targeted graph traversal. SQLite default, Neo4j optional.

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# Engrama

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

[![PyPI](https://img.shields.io/pypi/v/engrama.svg)](https://pypi.org/project/engrama/)
[![Python](https://img.shields.io/badge/python-3.11+-blue.svg)](https://python.org)
[![Backend](https://img.shields.io/badge/backend-SQLite_%7C_Neo4j-green.svg)](https://raw.githubusercontent.com/scops/engrama/main/docs/backends.md)
[![License](https://img.shields.io/badge/license-Apache_2.0-blue.svg)](https://raw.githubusercontent.com/scops/engrama/main/LICENSE)
[![Status](https://img.shields.io/badge/status-alpha-orange.svg)](https://raw.githubusercontent.com/scops/engrama/main/)

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, `pip install engrama` and you're running.
- **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](docs/backends.md)**
for a full decision guide; the rest of this README assumes the SQLite
default.

Since **0.13.0**, every node and relation is owned by an
`(org_id, user_id)` identity and reads are **fail-closed**: a missing or
partial scope matches nothing rather than falling back to "see all". A
single-process install runs as one stable standalone identity and needs
no configuration; a multi-tenant deployment supplies the identity per
request from an authenticating gateway. Each identity can permanently
erase its own memory through the `engrama_gdpr_forget` tool
(GDPR right-to-erasure). See
**[docs/security.md](docs/security.md#tenant-isolation-multi-tenant)**.

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

---

## 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 |

---

## 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](https://www.python.org/downloads/) |
| **uv** (Python package manager) | any recent | `uv --version` | [docs.astral.sh/uv](https://docs.astral.sh/uv/getting-started/installation/) |

> **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](https://obsidian.md/) — for vault sync features.
- A local embedder for semantic search.
- [Docker Desktop](https://www.docker.com/products/docker-desktop/) —
  only if you opt into the Neo4j backend.

---

## Quick start (SQLite, zero-dep)

### Step 1: Install

From PyPI (recommended):

```bash
pip install engrama          # or: uv add engrama
```

Or from source, for development:

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

> The commands below assume a PyPI install (`engrama ...`). From a source
> checkout, prefix each one with `uv run` (`uv run engrama ...`).

### Step 2: Initialise the schema

```bash
engrama init --profile developer
```

### Step 3: Verify

```bash
engrama verify
```

### Step 4: Use it

**A) From Python:**
```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:**
```bash
engrama search "FastAPI"
engrama reflect
```

---

## 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:

```bash
pip install "engrama[neo4j]"     # or, from source: 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:

```bash
engrama init --profile developer
engrama verify
```

---

## Security considerations

Engrama stores everything an agent learns, so treat the memory graph as
sensitive data. The full policy lives in
**[docs/security.md](docs/security.md)**; the essentials:

- **Data residency.** On the default SQLite backend all data lives in a
  single local file (`~/.engrama/engrama.db` by default) — nothing leaves
  your machine. It is plain SQLite: keep it off shared filesystems, back
  it up, and rely on filesystem permissions for at-rest protection. On the
  Neo4j backend, data resides wherever you host Neo4j; you own that
  deployment and its region.
- **Neo4j authentication.** Supply credentials through `.env` /
  environment variables (`NEO4J_URI`, `NEO4J_USERNAME`, `NEO4J_PASSWORD`)
  — never commit them. The shipped `docker-compose.yml` is for local dev;
  change the default password and enable TLS before any networked use.
- **Embedding providers.** Endpoints reached via `OPENAI_BASE_URL` should
  use HTTPS unless they are on localhost or a trusted network. With
  `EMBEDDING_PROVIDER=none` no text is sent anywhere; search degrades to
  fulltext-only.
- **Tenant isolation.** Since 0.13.0 every node and relation is owned by an
  `(org_id, user_id)` identity and reads are **fail-closed**. A single
  install runs as one stable standalone identity; a multi-tenant
  deployment must inject the identity per request from an authenticating
  gateway (set `ENGRAMA_REQUIRE_IDENTITY=1` to fail closed on missing
  headers). See
  **[docs/security.md](docs/security.md#tenant-isolation-multi-tenant)**.
- **Right to erasure.** Each identity can permanently erase its own memory
  via the `engrama_gdpr_forget` tool (GDPR). There is no undo and no
  server-side backup.
- **Network exposure.** The MCP server is meant for a local client. The
  optional Streamable HTTP transport ships **without authentication** —
  keep it on loopback or behind your own authenticated gateway.

---

## 📚 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](https://scops.github.io/engrama/)**

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