samyama-graph

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 AWS account or credentials needed — the image is publicly available

Step 2 — Pull the Docker image

docker pull public.ecr.aws/f9f6l5u4/samyama-graph:1.1.0

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: public.ecr.aws/f9f6l5u4/samyama-graph:1.1.0
    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 — Samyama Visualizer

Visualize your imported graph data using the Samyama cloud visualizer at https://graph.samyama.cloud/

1. Open https://graph.samyama.cloud/ in your browser.
2. Sign up for a new account, or sign in if you already have one.
3. From the left sidebar, click Home.
4. In the connection field, enter your local graph server URL: http://localhost:8080.
5. Click Connect — the status will change to Connected.

7a — Download snapshot

Tip: Save the file in the same folder as docker-compose.yml to avoid path errors.

• Windows: C:\samyama-graph\dbms-research.sgsnap
• Linux / Mac: ./samyama-graph/dbms-research.sgsnap

7b — Create tenant

Linux & Mac:

curl -X POST http://localhost:8080/api/tenants \
  -H "Content-Type: application/json" \
  -d '{"id": "dbms-research", "name": "dbms-research"}'

Windows (PowerShell):

curl.exe -X POST http://localhost:8080/api/tenants `
  -H "Content-Type: application/json" `
  -d '{"id": "dbms-research", "name": "dbms-research"}'

7c — Import snapshot

Linux & Mac:

curl -X POST http://localhost:8080/api/snapshot/import \
  -F "file=@./samyama-graph/dbms-research.sgsnap" \
  -F "tenant_id=dbms-research"

Windows (PowerShell):

curl.exe -X POST http://localhost:8080/api/snapshot/import `
  -F "file=@C:\samyama-graph\dbms-research.sgsnap" `
  -F "tenant_id=dbms-research"

Note: On Windows always use curl.exe — PowerShell's curl alias does not support -F.

Step 8 — Stop / reset

Stop the server:

docker compose down

Reset all data (⚠️ deletes volume):

docker compose down -v

⚠️ This deletes all graph data stored in the Docker volume.

Option 2 — Build from source

# Build from source
git clone https://github.com/samyama-ai/samyama-graph && cd samyama-graph
cargo build --release
./target/release/samyama    # RESP on :6379, HTTP on :8080

# Connect with any Redis client
redis-cli -p 6379
GRAPH.QUERY mydb "CREATE (a:Person {name: 'Alice'})-[:KNOWS]->(b:Person {name: 'Bob'})"
GRAPH.QUERY mydb "MATCH (a)-[:KNOWS]->(b) RETURN a.name, b.name"

What can you build with Samyama Graph?

Samyama Graph is useful when your application needs both connected-data reasoning and semantic retrieval.

You can use it to build:

• GraphRAG systems that combine vector search with graph traversal
• Knowledge graph applications for enterprise, research, healthcare, and operations data
• AI agent memory where entities, tools, actions, and context are stored as a graph
• Biomedical and clinical graphs across papers, trials, pathways, drugs, and conditions
• Fraud and investigation graphs for relationship discovery and pattern analysis
• Infrastructure and dependency graphs for impact analysis and root-cause exploration
• Large-scale graph analytics using built-in graph algorithms

We loaded the entire PubMed corpus — every article published since 1966 — plus ClinicalTrials.gov, Reactome pathways, and DrugBank into one graph. Then we asked:

"What drugs are most tested in cancer clinical trials?"

MATCH (m:MeSHTerm)<-[:ANNOTATED_WITH]-(a:Article)
      -[:REFERENCED_IN]->(t:ClinicalTrial)-[:TESTS]->(i:Intervention)
WHERE m.name = 'Neoplasms'
RETURN i.name, count(DISTINCT t) AS trials
ORDER BY trials DESC LIMIT 5

5.2 seconds. One query. Four databases. 74 million nodes. 1 billion edges. A single machine.

See all 100 benchmark queries →

⭐ Find this useful? A GitHub star helps more developers discover Samyama Graph.

Demo

Cricket KG — 36K nodes, 1.4M edges, live graph simulation

Click for full demo (1:56)

Infrastructure failure-propagation

One query family — reachability, criticality, N-1 contingency — runs identically across infrastructure domains. Both demos use real CC BY 4.0 data.

Power Grid — IEEE 14-bus system (pglib-opf): degree centrality → connectivity → N-1 line contingency.

Telecom — GÉANT 2012 pan-European backbone (Internet Topology Zoo): 40 PoPs across 37 countries; N-1 link contingency exposes 8 single points of failure.

Case Studies — prove it yourself

case_studies/ lets anyone who clones this repo download a real public knowledge graph, import it, run showcase Cypher (and vector search), and render the session as a narrated GIF — one command, no database to install. Every showcase query is gated to return real rows before any GIF is recorded (see the Definition of Done).

cargo build --release && pip install rich requests
cd case_studies/cricket && ./run.sh          # fetch snapshot → import → validate → demo
RECORD=1 ./run.sh                            # also (re)generate demo.gif

Each snapshot is small enough to run on a laptop; every query returns real rows. GIFs can't pause in a browser, so each domain also ships its demo.cast — replay it pausably (space) with asciinema play case_studies/<domain>/demo.cast.