alibaba/UnifiedModel
The semantic layer that makes enterprise data understandable to AI agents — model entities and relations once, query through SPL/MCP/REST, and connect telemetry, services, and business objects in one object graph.
UnifiedModel (UModel) – Quick Overview
What it is
UModel (Unified Model) is an open‑source, vendor‑neutral runtime that lets enterprises describe their data, services, and infrastructure as a semantic graph. The graph lives in a local service (the UModel server) and can be queried by humans, programs, or AI agents through a single, consistent API.
Core ideas
| Idea | Why it matters |
|---|---|
| Model packs – declarative bundles that define entities, datasets, links, storage and topology semantics. | Gives teams a shared “language” for all their data sources without locking them to a specific vendor. |
| Workspace‑scoped graph – one isolated graph per workspace (e.g., a demo, a project, a department). | Enables safe local experimentation and fast start‑up while still representing a full enterprise view. |
Unified query surface – three SPL‑style entry points (.umodel, .entity, .topo). |
One query language for model metadata, runtime entities, and topology, simplifying tooling and agent integration. |
| AgentGateway & MCP – thin bridges that let LLM‑based agents discover the graph, run queries and invoke run‑book skills. | Turns the graph into actionable context for AI assistants, enabling root‑cause analysis, service discovery, etc. |
Public contracts – REST OpenAPI, CLI (umctl), and generated SDKs (Go, Python, Java). |
Guarantees that external tools can interact without needing to understand the server internals. |
Main components
- UModel Service – the Go server that stores the graph (in‑memory, file‑backed, Neo4j, Gremlin, etc.) and serves the Query Service.
umctlCLI – command‑line client for workspace management, queries, and agent operations.- MCP server – a lightweight control plane that exposes discovery and resource schemas to agents.
- AgentGateway – runtime that forwards agent requests to the Query Service and RunbookSet skills.
- React Web UI – visual explorer for models, entities, topology and agent activity.
- SDKs – auto‑generated client libraries for Go, Python, and Java.
Typical workflow (5‑minute quick start)
- Install prerequisites – Go 1.22+, Node 22+,
make,pnpm. - Run
make quickstart. This spins up the API, the UI, and loads a demo workspace (GRAPHSTORE=memory). - Open
http://localhost:5173→ explore the demo graph via the Explorer, Query, Data Store and Agent tabs. - Connect an AI agent:
umctl agent discover demo # registers the demo workspace with AgentGateway umodel-mcp # starts an MCP client for the agent - Query the graph from CLI or REST, or let an LLM‑based agent run a model‑guided root‑cause analysis (see the incident‑investigation example).
Who would use it?
- Enterprise data teams that need a unified semantic layer across heterogeneous sources (databases, monitoring tools, service registries).
- AI/ML engineers building agents that must understand the relationships between services, metrics, and logs.
- Observability platforms wanting a programmable “semantic operating system” rather than a static data dictionary.
- Developers looking for a local, extensible sandbox to prototype multi‑domain graph models before deploying to a cloud control plane.
Extensibility & deployment
- GraphStore plug‑ins – choose between in‑memory, file‑backed JSON, Neo4j, Gremlin, or Alibaba’s Ladybug runtime.
- Model packs – add new entity types or link semantics by dropping a YAML/JSON pack into the workspace.
- Agent skills – ship
SKILL.mdfiles that let agents read the graph, invoke run‑books, or perform root‑cause analysis. The repo provides ready‑made skill bundles for Claude, Qoder, Codex, etc. - Docker/Compose – ready‑made files for containerised deployment; the core remains vendor‑neutral.
License & community
- Apache‑2.0 open source license.
- Contribution guidelines, security policy, and code of conduct are provided.
- Support channels are listed in
SUPPORT.md.
Bottom line: UModel is a real, production‑grade open‑source platform that gives enterprises a programmable, vendor‑agnostic semantic graph. It bridges data governance, observability, and AI‑agent workflows through a unified runtime, CLI, UI, and SDKs.
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