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graphjin

Go compiler + runtime exposing databases, warehouses, files and code as one governed GraphQL + MCP graph for agents — catalog-first discovery, allow-lists, built-in agent, standing watches.

3,234 207 Go Apache-2.0updated 5 days ago
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Curator's take

Use it when agents need to query real company data across Postgres/MySQL/Mongo/Snowflake/S3 without raw credentials: discovery-before-action, read-only boundaries, allow-listed queries and audit come built in, and the same binary serves your apps as a GraphQL/REST API. Skip it if you only need one DB behind a single text-to-SQL tool, or want curated metric definitions — a semantic layer fits better; the surface area here is large and opinionated.

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README.md2 min read

GraphJin — One Governed Graph for Your AI Agents

Apache 2.0 NPM Package Docker Pulls Discord Chat GoDoc GoReport

GraphJin is a compiler and runtime that gives AI agents one governed graph over the systems a real company already has: databases, warehouses, files, source code, workflows, metadata, and security policy. Instead of handing an agent raw credentials and hoping it guesses correctly, GraphJin exposes that graph through GraphQL + MCP: the agent discovers before acting, validates queries, runs approved work, observes runtime status — and every answer is checked against an execution ledger before it leaves the server.

It is not only for agents. GraphJin is still a high-performance GraphQL-to-database compiler, Go library, standalone API service, REST/OpenAPI gateway, and real-time subscription server. The agent use case is where everything comes together: the same compiler that serves your apps can also give AI a smart, auditable way to work across data, code, and operations.

Works with PostgreSQL, MySQL, MongoDB, SQLite, Oracle, MSSQL, Snowflake, Redshift, BigQuery, Apache Cassandra / Amazon Keyspaces, S3/GCS/local files, CodeSQL source indexes - and models from Claude/GPT-4 to local 7B models.

Why GraphJin For Agents

  • A built-in agent, one call away - POST one instruction to /api/v1/agent (or call the ask_graphjin_agent MCP tool) and GraphJin runs the discovery loop itself - as the caller, under the caller's permissions - and returns a typed, evidence-backed answer. See Server-Side Agent.
  • One governed surface for many systems - Query operational databases, warehouses, MongoDB, object stores, local files, CodeSQL source indexes, workflows, and GraphJin system roots through GraphQL and MCP.
  • Smart discovery before action - Agents start with query_catalog(search: "<user instruction>"), graphql_help, relationship evidence, examples, config recipes, and safety notes before writing or running queries.
  • Guarded action, not raw access - Source-mode access, query allow-lists, read-only boundaries, policy-aware MCP tools, local encrypted secrets, and gj_config preview/apply keep changes auditable.
  • Operational awareness - gj_security, gj_runtime, and the built-in console expose policy and bounded runtime status so agents can check what is safe before they act.
  • Standing questions, not just answers - Hand a cursor-paginated subscription to gj_watch and GraphJin keeps answering it under the owner's permissions, resumes from persisted cursors across restarts, treats absence as a first-class event, and files what it finds in a durable inbox (gj_watch_event), a webhook, or a workflow. Evaluation runs on database polls, not model calls - a model writes the watch once, and nothing calls one again unless you turn on optional per-watch triage. See Watches.
  • Durable memory - Saved queries, fragments, and workflows live in the owner-scoped gj_artifacts store. Normal watches are durable by default; explicit ephemeral watches use TTL leases.

Installation

npm (all platforms)

npm install -g graphjin

macOS (Homebrew)

brew