Direct answer
Use this chooser to narrow framework choice before a spike, especially when the team is comparing LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, and custom orchestration.
Customer support routing agent
Input: Branching workflow, checkpointed state, production maturity, TypeScript preference, MCP required.
Output: The output should rank LangGraph or the OpenAI Agents SDK near the top, with reasoning and caveats.
How to use this tool
- 1. Pick workflow shape.
- 2. Set state and maturity needs.
- 3. Choose language and MCP needs.
- 4. Review ranked recommendations and the matrix.
What each framework is good at
LangGraph is strong for explicit stateful workflows, checkpointing, and production control. CrewAI is fast for role-based collaboration patterns. AutoGen is useful for research-style multi-agent experiments. The OpenAI Agents SDK is attractive when you are OpenAI-first and want a supported path.
Custom orchestration wins when the team needs strict control, small runtime surface area, or framework independence. It also raises maintenance burden, so it should be chosen deliberately.
When the recommendation may be wrong
Framework choice depends on team skill, existing stack, compliance, observability, and migration constraints. Use this chooser as a structured starting point, then validate it against a spike that includes failure modes and deployment requirements.
Assumptions and methodology
This tool uses transparent browser-side calculations and curated assumptions rather than LLM-generated recommendations. Outputs are planning estimates. They should be validated against provider pricing, production traces, engineering quotes, or domain review before money, compliance, safety, or hiring decisions are made.
Numerical defaults are dated and surfaced on the page. The methodology favours explicit assumptions over false precision: every estimate is meant to expose the variable that drives the result, not to pretend that early planning data is exact.
Turn the result into an implementation plan
Bring the scenario to a strategy call and I will pressure-test the workflow, assumptions, failure modes, and delivery path.
Book a strategy callFrequently asked questions
- What's the difference between LangChain and LangGraph?
- LangChain provides broad integrations and abstractions. LangGraph focuses on explicit graph execution, state, retries, and checkpoints, which makes it a better fit for many production agent workflows.
- When does CrewAI win?
- CrewAI is attractive when the product maps naturally to role-based agents and the team wants speed over low-level graph control. It is often strong for prototypes, internal workflows, and collaboration metaphors.
- Is AutoGen production-ready?
- AutoGen can be useful, but teams should validate observability, state management, deployment, and failure handling before treating it as production infrastructure.
- Should I use OpenAI Agents SDK if OpenAI-only?
- If your organisation is OpenAI-first and values official support, the Agents SDK can be a practical default. If you need multi-provider portability, evaluate LangGraph or custom orchestration.
- How does MCP change framework choice?
- MCP standardises tool and context access. If MCP support is central to your roadmap, favour frameworks with active MCP integration and a clean tool permission model.
- When does rolling your own make sense?
- Custom orchestration makes sense when workflows are narrow, compliance is strict, runtime size matters, or the team already has strong workflow infrastructure. Most teams should start with a framework unless they can own the maintenance cost.