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 — Docker

Pulling the image needs no registry account: it is public on GitHub Container Registry. latest is the newest release; pin a version such as :1.12.0 for a reproducible setup.

Step 1 — Write docker-compose.yml in an empty folder:

services:
  samyama-graph:
    image: ghcr.io/samyama-ai/samyama-graph:latest
    container_name: samyama-graph
    restart: unless-stopped
    ports:
      - "6379:6379"   # RESP (Redis protocol)
      - "8080:8080"   # HTTP API
    environment:
      # Lets the hosted visualizer (Step 4) call this server from your browser.
      SAMYAMA_CORS_ORIGINS: https://graph.samyama.cloud
    volumes:
      - samyama-data:/app/samyama_data
volumes:
  samyama-data:

Step 2 — Start it

docker compose up -d
docker logs -f samyama-graph     # Ctrl-C to stop following the log

Step 3 — 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"]]}

Runtime values go in params, never in the query text — they are bound, so a value is never re-read as Cypher:

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"}}'

Step 4 — Explore it visually (optional). Open the hosted visualizer at https://graph.samyama.cloud/. The engine itself needs no account, but the visualizer asks you to sign up or sign in. Then click Home, enter http://localhost:8080 and click Connect. Without the SAMYAMA_CORS_ORIGINS line from Step 1 the browser refuses the call — deliberately, because /api/query runs arbitrary Cypher including DELETE (#1328).

Step 5 — Stop or reset

docker compose down        # stop
docker compose down -v     # stop and delete all graph data in the volume

The server needs no credential by default. To require a bearer token, serve TLS, or enable auto-embedding, see docs/DATA-HANDLING.md. To load a real dataset, pick one from the case studies.

Option 2 — Build from source

zstd-sys generates its bindings with bindgen, which needs libclang; without it the build fails part-way with a misleading 'stddef.h' file not found.

# Debian / Ubuntu
sudo apt-get install -y build-essential cmake pkg-config libssl-dev clang libclang-dev
# Fedora / RHEL
sudo dnf install -y gcc gcc-c++ cmake pkgconf-pkg-config openssl-devel clang clang-devel
# macOS — the Xcode Command Line Tools already provide clang
xcode-select --install

Then, with a stable Rust toolchain from rustup:

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"

Clients

Any Redis client also works over RESP on port 6379. Details: Python · TypeScript · Rust.

What can you build with Samyama Graph?

• 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, investigation, infrastructure and dependency graphs for pattern and impact analysis

We loaded the entire PubMed corpus plus ClinicalTrials.gov, Reactome pathways, and DrugBank into one graph, then 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

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

That is query XK02 in verified-results.csv: 10,250.2 ms, measured 2026-04-02 on one r6a.8xlarge and not re-measured since. Provenance of each row is in docs/BENCHMARKS.md.

See all 100 benchmark queries →

Demo

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

Click for the full demo (1:56).

Case Studies — prove it yourself

case_studies/ downloads a real public knowledge graph, imports it, runs showcase Cypher, and renders the session as a narrated GIF — one command. Every showcase query is gated to return real rows (Definition of Done).

cargo build --release && pip install rich requests
cd case_studies/cricket && ./run.sh          # fetch snapshot → import → validate → demo

Plus surveillance, health-determinants, health-systems and football — browse the catalogue →

Why Samyama Graph?

The 30-Second Tour

Cypher — MATCH, CREATE, MERGE, aggregations, pa