What each framework is good at
Agent framework choice is an architecture decision, not a popularity contest. The right answer depends on workflow shape, state requirements, language, production maturity, MCP needs, and how much control the team is willing to own.
What each framework is good at
LangGraph is strongest when state, branching, and checkpointing matter. CrewAI is fast for role-based prototypes and team-style workflows. AutoGen is useful for conversation-heavy multi-agent experiments. The OpenAI Agents SDK fits OpenAI-centric stacks with hosted primitives. Custom orchestration fits regulated or unusual systems where control matters more than speed.
When to roll your own
Rolling your own makes sense when the workflow is stable, compliance constraints are high, framework abstractions fight your runtime, or your team already owns a durable workflow layer. It is a poor default for teams that have not yet proven the product workflow.
Assumptions and methodology
Frameworks are scored against state needs, workflow shape, language, production maturity, MCP needs, and control preference.
The page keeps the interaction fast, accessible, and dependency-light while preserving the important planning behaviour from the implementation plan: editable inputs, visible outputs, no signup gate, structured data, internal links, citations, and clear caveats. Heavy runtime features such as Monaco, solc, exact tokenizers, or PDF export should remain code-split when added so the public page stays fast.
Sources and review cadence
Assumptions should be reviewed quarterly, and pricing-sensitive assumptions should be reviewed monthly. The public data files in the repo include last-reviewed or last-verified dates where the plan calls for them.
Scenario pages
The implementation plan calls for indexable scenario pages. These pages provide a direct recommendation for common agent patterns and link back to the chooser.
Need the architecture behind the number?
Use this tool to narrow the conversation, then bring the scenario into an architecture review if the decision affects budget, security, compliance, or production reliability.
Book a strategy callFrequently asked questions
What's the difference between LangChain and LangGraph?
LangChain is a broad library for LLM application building. LangGraph focuses on stateful graphs, branching, cycles, and checkpointed execution. For agentic workflows that need durable state and controlled transitions, LangGraph is usually the more relevant comparison point.
When does CrewAI win?
CrewAI wins when the team needs a fast, readable role-based prototype and the workflow maps naturally to agents with responsibilities. LangGraph tends to win when production state, branching, retries, and explicit control matter more than quick setup.
Is AutoGen production-ready?
AutoGen can be useful, but teams should treat production readiness as their own responsibility. Review observability, state, deployment model, failure handling, and governance before betting a customer-facing workflow on any multi-agent framework.
Should I use the OpenAI Agents SDK if I am OpenAI-only?
It is a strong option for OpenAI-centric teams because it aligns with OpenAI primitives and reduces glue code. Still compare it against your state, deployment, governance, and portability requirements before choosing it by default.
How does MCP change framework choice?
MCP makes tool integration more portable by standardizing how models and agents connect to external systems. A framework with good MCP support can reduce custom adapter work, but it does not remove the need for permissions, audit logs, and failure handling.
What if my team is TypeScript-only?
Prefer TypeScript-friendly options or a custom orchestration layer. A Python-only framework can still work if the agent service is isolated, but forcing a second runtime into a small team often slows debugging, deployment, and ownership.