Open-source AI agents are moving from demo loops into real engineering workflows: research, support triage, coding, browser tasks, and multi-step internal automation. The important choice is no longer which project has the most impressive demo. It is whether you need a deterministic workflow with a few model-assisted steps, a team of role-based agents, or an autonomous runtime that can operate tools inside a sandbox.
This comparison separates agent frameworks from finished agent applications. CrewAI, LangGraph, AutoGen, PydanticAI, and smolagents are libraries for building your own system. OpenHands is closer to a ready-to-run software-development agent. That distinction determines how much application code, observability, and security work your team must own.
Key takeaways
- Choose LangGraph when explicit state, branching, and human approval points matter most.
- Choose CrewAI when role-based multi-agent collaboration matches the problem and the team wants a higher-level API.
- Choose AutoGen for flexible conversational agent experiments and Microsoft-aligned ecosystems.
- Choose PydanticAI when typed Python outputs and validation are more important than autonomous behavior.
- Choose smolagents for a compact Hugging Face-friendly starting point with local-model options.
- Choose OpenHands for a packaged coding agent, not as a generic workflow library.
Quick comparison
| Project | Best fit | Control model | Local-model path |
|---|---|---|---|
| LangGraph | Stateful production workflows | Explicit graph and checkpoints | Provider adapters, including local endpoints |
| CrewAI | Role-based agent teams | Crews plus deterministic Flows | Ollama and compatible providers |
| AutoGen | Conversational multi-agent systems | Message-driven agents and teams | Configurable model clients |
| PydanticAI | Typed Python applications | Schema-first tools and outputs | Provider-dependent |
| smolagents | Small experiments and Hugging Face stacks | Compact code/tool agents | Transformers and compatible providers |
| OpenHands | Delegated software tasks | Packaged coding-agent runtime | Local GUI plus configurable models |
LangGraph
LangGraph
A graph-based orchestration framework for long-running, stateful agents. Its main advantage is explicit control: nodes, transitions, checkpoints, interrupts, and human review can be designed rather than hidden inside one autonomous loop. The trade-off is framework depth and a larger conceptual surface. Choose it when the workflow must be inspectable, resumable, and recoverable in production.
- Best for
- Teams building stateful agents with approval gates and durable execution.
- Deployment
- Python or JavaScript application; infrastructure depends on your persistence and model choices.
- Pricing
- Open-source framework; model, hosting, and optional managed services are separate.
- Unique
- Strongest fit for explicit state machines rather than agent theatre.
CrewAI
CrewAI
A Python framework built around agents with roles, tasks, crews, and deterministic Flows. It is approachable when the problem naturally decomposes into researcher, reviewer, and executor responsibilities. The risk is unnecessary multi-agent complexity: several agents do not automatically outperform one well-designed workflow. Choose CrewAI when role separation improves accountability and testability, not merely because the demo looks collaborative.
- Best for
- Python teams modeling collaborative, role-based automation.
- Deployment
- Python package; deploy with your normal worker or API stack.
- Pricing
- MIT-licensed framework; models and infrastructure are separate.
- Unique
- Accessible multi-agent vocabulary plus a deterministic Flow layer.
AutoGen
AutoGen
Microsoft's framework for composing conversational agents and agent teams. It is useful for prototyping message-driven coordination, tool use, and human participation. The flexibility can also produce systems whose behavior is difficult to predict unless you add strict termination, budgets, and evaluation. Choose it when conversational collaboration is central and your team is prepared to instrument every loop.
- Best for
- Research and platform teams experimenting with conversational agent systems.
- Deployment
- Python or .NET application, depending on the AutoGen package used.
- Pricing
- Open-source framework; provider and hosting costs separate.
- Unique
- Broad conversational-agent primitives and a strong experimentation posture.
PydanticAI and smolagents
Not every application needs an autonomous team. PydanticAI is compelling when the output must pass a typed schema and integrate cleanly with an existing Python service. smolagents is attractive when a small, readable agent implementation and the Hugging Face ecosystem are the priorities. Both are good reminders that the smallest agent loop is often the easiest one to secure and evaluate.
- Use PydanticAI for structured extraction, validated decisions, and typed tool contracts.
- Use smolagents for concise experiments, code agents, and Hugging Face model paths.
- Start with one agent and one or two tools; add roles only when evaluation proves the need.
- Keep provider adapters isolated so local and hosted models can be compared on the same tasks.
OpenHands is a product-shaped agent
OpenHands belongs in the shortlist when the goal is software development: repository edits, terminal commands, tests, and delegated issues. Its SDK can be composed, but the Local GUI and CLI make it much closer to a finished coding agent than the general frameworks above. That means faster time to a coding pilot, but less reason to choose it for an unrelated support or procurement workflow.
Production checklist
- Create a task-level evaluation set before changing prompts or models.
- Give every tool the minimum permissions needed and log each invocation.
- Set iteration, time, and token budgets with explicit termination conditions.
- Require human approval before writes, payments, external messages, or destructive actions.
- Persist workflow state so failed jobs resume instead of repeating side effects.
- Track model, prompt, tool version, latency, cost, and outcome for every evaluated run.
Frequently asked questions
- What is the best open-source AI agent framework?
- There is no universal winner. LangGraph fits controlled stateful workflows, CrewAI fits role-based collaboration, PydanticAI fits typed Python services, and OpenHands fits delegated coding tasks. Start from the workflow and failure model, not GitHub stars.
- Can these frameworks run with local models?
- Many can connect to Ollama or OpenAI-compatible local endpoints, but tool-use reliability depends on the selected model. Test the exact tasks and schemas you need; local support in a provider list is not proof of production quality.
- Do I need multiple agents?
- Usually not at first. One agent with explicit tools and a deterministic workflow is cheaper to observe and debug. Add roles when separate context, permissions, or evaluation criteria provide a measurable benefit.
- Are open-source agents free to operate?
- The framework may be free, but inference, GPUs, storage, tracing, and operations are not. Compare total cost per successful task, including failed loops and human review.
Conclusion
For most production teams, the strongest default is the framework that exposes the most control with the least unnecessary autonomy. Prototype the same three tasks in LangGraph, CrewAI, or a typed PydanticAI workflow, then compare success rate, review time, and cost. Reach for OpenHands only when the job is genuinely software development, and keep model routing replaceable from day one.
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