When should you use AutoGen, and when should you skip it?

Use AutoGen when: research into conversable multi-agent systems. Skip it when: you need something stable and documented for production.

When should you use CrewAI, and when should you skip it?

Use CrewAI when: role-based orchestration where agents are framed as a team. Skip it when: you want direct control over the loop itself.

When should you use Pydantic AI, and when should you skip it?

Use Pydantic AI when: type-safe agents where schema correctness is the priority. Skip it when: you want a batteries-included ecosystem.

← Comparisons

AutoGen vs CrewAI vs Pydantic AI

All three let agents talk to each other, but they disagree on who is in charge. The decision is whether you want a conversation, a crew with roles, or typed agents you orchestrate yourself.

Short answer

Pick on the shape of the collaboration. CrewAI when the workflow is a set of roles passing work along; AutoGen when agents genuinely need to converse and challenge each other; Pydantic AI when the risk is malformed tool calls and you want the types to catch them.

By Kunj Shah · Licence and cost facts verified 2026-09 · method

Agent Frameworks

AutoGenConversable agents in a chat room.
CrewAIRole-based crew with task handoffs.
Pydantic AITyped agents you compose yourself.

AutoGen vs CrewAI vs Pydantic AI compared across 6 dimensions
Dimension	AutoGen	CrewAI	Pydantic AI
Orchestration model	Group chat among agents	Sequential or hierarchical crew	You write the control flow
Type safety	Moderate, Python-first	Moderate, task and role configs	Strong, Pydantic models end to end
Observability	Built-in tracing and logs	Execution traces per task	Via your own tracing layer
Best for	Research-style exploration and debate	Business workflows with clear roles	Agents where bad tool args are the risk
Operational surface	Medium, many agent types	Medium, crew config plus tools	Small core, you own orchestration
Where it loses	Harder to reason about at scale	Less flexible than writing the loop	No batteries-included multi-agent glue

Scroll the table sideways to see every tool.

What this table is not. These are editorial judgements, not benchmarks: Lattice has not run these tools head to head, and no cell uses GitHub stars or vendor benchmark claims as evidence. Licence and cost facts are re-checked on a schedule the build enforces: the receipt is public.

The recommendation

Which should you choose: AutoGen, CrewAI or Pydantic AI?

Rules of thumb

Multi-agent systems multiply failure modes. Start with one agent and add a second only when a single agent cannot hold the context.
A framework orchestrates calls inside one process. It does not make long runs survive a deploy.
Fewer tools beats a longer system prompt. Narrow the agent before you add another integration.

Quick answers

When should you use AutoGen, and when should you skip it?: Use AutoGen when: research into conversable multi-agent systems. Skip it when: you need something stable and documented for production.
When should you use CrewAI, and when should you skip it?: Use CrewAI when: role-based orchestration where agents are framed as a team. Skip it when: you want direct control over the loop itself.
When should you use Pydantic AI, and when should you skip it?: Use Pydantic AI when: type-safe agents where schema correctness is the priority. Skip it when: you want a batteries-included ecosystem.