Samyama Graph

Samyama Graph A Rust-native graph-vector database for GraphRAG, knowledge graphs, and billion-edge analytics.

<strong>99.9% of the openCypher TCK's evaluated scenarios pass · SNB Interactive 21/21 and SNB BI 20/20, no timeouts · 1B edges on one machine</strong>









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What is Samyama Graph?

Samyama Graph is a Rust-native graph-vector database that lets developers store, query, search, and analyze connected data in one system.

It brings together graph traversal, OpenCypher-style querying, vector search, graph algorithms, and Redis-compatible access, making it useful for GraphRAG, knowledge graphs, AI agent memory, and large-scale relationship analytics.

Quickstart

Option 1 — Run with Docker Compose

Step 1 — Prerequisites

• ✅ Docker Desktop installed and running — Watch setup video →
• ✅ No account or credentials needed — the image is public on GitHub Container Registry (latest is the newest release; pin a version such as :1.11.0 for a reproducible setup)

Step 2 — Pull the Docker image

docker pull ghcr.io/samyama-ai/samyama-graph:latest

Step 3 — Docker Compose setup

Create a clean folder, then create docker-compose.yml inside it.

Linux & Mac:

mkdir -p samyama-graph
cd samyama-graph
touch docker-compose.yml

Windows (PowerShell):

mkdir C:\samyama-graph
cd C:\samyama-graph
notepad docker-compose.yml

ℹ️ Replace <your-openai-api-key> with your actual key. Generate one at platform.openai.com/api-keys.

version: "3.9"
services:
  samyama-graph:
    image: ghcr.io/samyama-ai/samyama-graph:latest
    container_name: samyama-graph
    restart: unless-stopped
    ports:
      - "6379:6379"
      - "8080:8080"
    environment:
      EMBED_ENABLED: "true"
      EMBED_PROVIDER: openai
      EMBED_MODEL: text-embedding-3-small
      EMBED_API_KEY: <your-openai-api-key>
      EMBED_DIMENSION: 1024
    volumes:
      - samyama-data:/app/samyama_data
    networks:
      - samyama-network
networks:
  samyama-network:
    driver: bridge
volumes:
  samyama-data:

Step 4 — Start the server

docker compose up -d

Server will be available at http://localhost:8080

Step 5 — Verify it's running

docker ps
docker logs -f samyama-graph

You should see samyama-graph with status Up.

Step 6 — Run your first query

curl -X POST http://localhost:8080/api/query \
  -H 'content-type: application/json' \
  -d '{"query":"CREATE (a:Person {name: \"Alice\"})-[:KNOWS]->(b:Person {name: \"Bob\"}) RETURN a.name, b.name","graph":"default"}'

{"columns":["a.name","b.name"],"edges":[],"nodes":[],"records":[["Alice","Bob"]]}

Then read it back:

curl -X POST http://localhost:8080/api/query \
  -H 'content-type: application/json' \
  -d '{"query":"MATCH (a:Person)-[:KNOWS]->(b:Person) RETURN a.name, b.name","graph":"default"}'

Parameters. A runtime value goes in params, not in the query text:

curl -X POST http://localhost:8080/api/query \
  -H 'content-type: application/json' \
  -d '{"query":"MATCH (p:Person) WHERE p.name = $name RETURN p.name","params":{"name":"Alice"}}'