samyama-ai/samyama-graph

Description and use

**Brief Summary**
Samyama‑Graph is a Rust‑based graph‑vector database that can store ~74 M nodes and ~1 B edges on a single machine and answer full‑graph queries for the cost of a few dollars. It combines OpenCypher query support, vector similarity search, and 14 built‑in graph algorithms, making it a compact back‑end for AI agents that need to retrieve structured and semantic knowledge quickly.

**Value Proposition**

• **Unified data access** – By exposing a single protocol that blends traditional graph traversal (OpenCypher) with vector‑based similarity, Samyama‑Graph lets LLM‑driven assistants retrieve both relational facts and “nearest‑neighbor” embeddings without wiring together separate stores.
• **Cost‑effective scaling** – Querying a billion edges for roughly $2.50 demonstrates that large‑scale graph analytics can run on commodity hardware, lowering infrastructure spend for knowledge‑rich AI products.
• **Ready‑to‑use algorithms** – The 14 built‑in graph algorithms (e.g., shortest path, community detection) provide immediate analytical capabilities for recommendation, reasoning, or tool‑selection logic in AI agents.

**Practical Adoption Path**

1. **Prototype** – Clone the repo, run the provided Docker/CLI container, and load a sample dataset. Use the REST/SDK endpoints to issue OpenCypher queries and vector searches from a Python or Rust client.
2. **Integrate** – Wrap the API behind a Model Context Protocol (MCP) server or a custom “tool‑connector” microservice that your AI assistant calls when it needs factual or contextual data.
3. **Validate** – Benchmark query latency and cost on a representative subset of your graph (e.g., a few million edges) to confirm that the $2.50 per‑billion‑edge claim holds for your workload.
4. **Scale** – Deploy the binary on a larger VM or bare‑metal box, tune memory/CPU limits, and enable persistence snapshots. Because the system runs on a single node, scaling is primarily vertical; sharding would require custom orchestration.

**Production Readiness**

• **Maturity** – Medium. The project is actively maintained (last commit 2026‑05‑11) and has modest community traction (61 ★, 4 forks). It is suitable for internal tools, prototypes, or low‑to‑moderate‑traffic services.
• **Dependencies** – Single‑binary Rust runtime with optional Python SDK; minimal external services, which simplifies deployment but still requires careful version pinning and security scanning of the compiled binary.
• **Risks** – License compliance, long‑term maintainer commitment, and security hardening have not been fully vetted. Before a production rollout, conduct a license review, run vulnerability scans on the container image, and establish an internal fallback plan (e.g., ability to switch to a more mature graph store).

In short, Samyama‑Graph offers a cost‑effective, single‑node solution for marrying graph reasoning with vector search, making it attractive for AI‑assistant back‑ends, provided the team performs the standard due‑diligence checks and validates performance at scale.

Use in a workflow

samyama-ai/samyama-graph helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.