Monorch
Compare
Library vs framework vs DIY.
Monorch is a TypeScript control plane with a Rust engine. Not a Studio. Not a RAG product. Here is how that sits next to common alternatives.
At a glance
One table. Honest columns.
Shape
- Monorch
- Library
- Mastra
- Framework
- LangGraph
- Library / platform family
- DIY
- Your code
HTTP
- Monorch
- BYO HTTP (Fastify, Hono, …)
- Mastra
- Owns more of the app shell
- LangGraph
- BYO / Lang ecosystem
- DIY
- Yours
Engine
- Monorch
- Rust validate · agent · graph
- Mastra
- TypeScript-first stack
- LangGraph
- Python-first (+ JS ports)
- DIY
- None
Studio
- Monorch
- No
- Mastra
- Yes
- LangGraph
- Studio / LangSmith adjacent
- DIY
- No
Events
- Monorch
- Shared AiEvent bus
- Mastra
- Framework events / APIs
- LangGraph
- Graph streaming APIs
- DIY
- Ad hoc
Checkpoints
- Monorch
- memorySaver / Postgres
- Mastra
- Platform patterns
- LangGraph
- Built-in persistence story
- DIY
- Roll your own
MCP
- Monorch
- Thin bridge → tool()
- Mastra
- Integrations vary
- LangGraph
- Integrations vary
- DIY
- Roll your own
Vs Mastra
Mastra wants to be the place you build AI apps. Monorch plugs into the server you already run. Keep routes, auth, and deploy unchanged — or choose Mastra when you want an opinionated kit with Studio.
Vs LangGraph
LangGraph is strong especially in Python, with a wide LangChain ecosystem. Monorch targets TypeScript backends with a Rust state machine for schema, permissions, agent steps, and graph cursors.
Vs DIY loops
DIY is fine until tool validation, handoffs, interrupts, checkpoints, and event streaming drift. Monorch covers that middle — not a platform, not a forever while-loop.
Product lock
We ship the control plane. You keep the rest.
Provide
- model / tool / agent / graph / workflow
- MCP → tool()
- memory interfaces
- OTel via AiEvent
- OpenAI-compatible providers
Do not provide
- HTTP framework
- ORM / auth / queues
- React chat UI
- RAG product
- Studio
More context in the Introduction and Architecture.