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Choose an agent framework for the workflow you need to coordinate—not because it appears at the top of a ranking. This editorial shortlist covers seven Python-oriented options with different approaches: graph-based control, role-based teams, conversational agents, document workflows, retrieval pipelines, typed interfaces, and Microsoft ecosystem integration. “Local” usually describes where inference runs; it does not mean the framework itself supplies a model runtime, or that every model feature works through every local adapter.
How to read this shortlist
This is a set of workflow-based candidates, not a universal top seven or a benchmark ranking. The descriptions reflect the approaches surfaced in a LangChain-authored 2026 landscape comparison and AWS Prescriptive Guidance on framework selection. Those sources help frame choices; they do not establish head-to-head performance. The evidence for current local-model support is also uneven: the AutoGen documentation directly describes an Ollama path, but comparable current primary-source details are not established here for all seven frameworks.
Use the “best first question” column to narrow the shortlist. It is a starting point for evaluation, not a claim that a framework cannot serve other workflows.
| Framework | Best first question | What the cited material establishes |
|---|---|---|
| LangGraph | Do I need explicit control over a stateful workflow? | The 2026 LangChain landscape article positions it for complex, precision-oriented agents and stateful orchestration; this is vendor-authored positioning, not a local-model test. |
| CrewAI | Does the work map naturally to roles and assigned tasks? | AWS guidance identifies role-based autonomous collaboration as a fit; the landscape article describes rapid role-based prototyping. |
| AutoGen | Should agents collaborate through conversation or asynchronous messages? | AutoGen’s official model documentation describes a local Ollama client and flags that integration as experimental in its model tutorial. |
| LlamaIndex | Is orchestration closely tied to documents and data? | The landscape article describes LlamaIndex Workflows as event-driven and document-centric; current official local-runtime details are not established here. |
| Haystack | Do retrieval or data pipelines need to work alongside agent components? | It is a candidate for combining Python retrieval/data pipelines with agents; the cited material does not establish detailed current local support. |
| PydanticAI | Are typed Python interfaces and structured application integration central? | It is a candidate for typed interfaces; exact current provider and local-runtime behavior is not established here. |
| Semantic Kernel | Is integration with the broader Microsoft ecosystem important? | The landscape article describes Microsoft Agent Framework as the unified successor to AutoGen and Semantic Kernel; verify current project stewardship and migration implications before choosing it for a new build. |
LangGraph: explicit state and workflow control
Start here when you want the orchestration itself to be a clearly controlled, stateful workflow rather than relying primarily on a team metaphor or open-ended agent conversation. The 2026 landscape article positions LangGraph for complex, precision-oriented agents. That positioning does not establish that it is superior for local inference or that a chosen local model will support the tools and outputs your workflow needs.
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CrewAI: roles and task assignment
CrewAI is a natural candidate when you can express the work as agents with distinct roles and tasks. AWS’s framework guidance identifies role-based autonomous collaboration as a fit, while the LangChain landscape article describes rapid role-based prototyping. Treat those descriptions as guidance on the abstraction, not evidence that a multi-agent team will outperform a simpler workflow.
AutoGen: conversational and asynchronous collaboration
AutoGen is worth evaluating when agents need to collaborate through messages, conversation, or asynchronous interaction. Its official model documentation includes a client for an Ollama server running locally. The Ollama integration is marked experimental in the model tutorial, so check the current API and test the exact model’s tool-call behavior before building around it.
Rank #2
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LlamaIndex: document-centered workflows
LlamaIndex is a candidate when document and data workflows are central to the orchestration. The 2026 landscape article describes LlamaIndex Workflows as event-driven and document-centric. That description does not establish current local-model compatibility; verify the framework’s current official guidance for your selected runtime and model.
Haystack: retrieval pipelines alongside agents
Consider Haystack if you need Python retrieval or data pipelines to fit alongside agent components. The available material supports that as a selection hypothesis, but does not establish detailed current local-runtime support or a reason to prefer Haystack over another candidate. Inspect the current project documentation for the components and model interface your application will use.
Rank #3
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PydanticAI: typed Python application interfaces
PydanticAI is a candidate when typed interfaces and structured integration into a Python application are priorities. The available material does not verify exact current provider integrations or local-runtime behavior. Confirm how the version you plan to use handles your chosen model’s structured outputs, tool calls, and validation needs.
Semantic Kernel: Microsoft ecosystem considerations
Semantic Kernel may merit evaluation when broader Microsoft ecosystem integration is an important constraint. The LangChain landscape article says Microsoft Agent Framework is the unified successor to AutoGen and Semantic Kernel. Because that is a consequential and time-sensitive project-status claim from a vendor-authored comparison, check Microsoft’s current project documentation and migration guidance before starting a new implementation.
Rank #4
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Separate orchestration from local inference
A local agent setup is a stack of distinct choices: the orchestration framework, an inference server or runtime, the model, the machine running it, and sometimes a proxy or adapter between the framework and runtime. Connecting to an Ollama server, for example, does not by itself prove that every Ollama model supports the framework’s expected tool calls, streaming behavior, or structured-output mode.
Local execution also does not, by itself, establish that an application is private, secure, or fully offline. Those properties depend on the complete deployment—including dependencies, network access, telemetry, model files, and any external services—not just the framework name.
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Choose by the constraint that matters most
AWS Prescriptive Guidance recommends matching both model-service integration and collaboration style to the requirements. Use these decision axes to turn the shortlist into a practical evaluation:
- Control: How much control do you need over graph structure, shared state, retries, and checkpoints?
- Collaboration: Does the work fit roles and handoffs, or message-driven and conversational agents?
- Data: Are retrieval and document pipelines core to the workflow or supporting components?
- Python ergonomics: How important are typed interfaces and structured outputs to the surrounding application?
- Integration: Does the framework support the model provider or runtime you intend to deploy, with acceptable portability?
- Deployment: Can the whole stack run within your machine, network, and operational constraints?
If one requirement dominates, shortlist frameworks around that requirement first. For example, a document-centered application should compare how candidates fit its retrieval and data flow; a conversational multi-agent prototype should focus on collaboration abstractions. Then test the exact runtime/model pairing rather than assuming that a framework-level integration guarantees every model capability.
Validate the exact local stack before committing
- Check project status and Python support. Confirm current maintenance, supported Python versions, package names, and installation instructions in the official documentation. The AutoGen installation page states Python 3.10 or later at the time reflected in the cited material; treat that as AutoGen-specific, not a requirement shared by all seven projects.
- Identify the runtime interface. Confirm whether the framework has an adapter for your inference server or whether you need a compatible API endpoint or proxy. Check current documentation rather than extrapolating support from another framework.
- Test the selected model’s capabilities. Run representative tool calls, structured-output requests, and streaming interactions through the actual framework and runtime combination. A connection that returns text is not proof that agent actions or required output modes work correctly.
- Exercise failure and recovery paths. Test timeouts, malformed outputs, tool errors, retries, and any state persistence or human approval flow your application requires.
- Check operational boundaries. Review network behavior, telemetry, dependencies, model storage, and machine-resource needs for the full deployment. Do not infer privacy or offline operation from local inference alone.
AutoGen’s official model documentation specifically cautions that small local models are typically less capable than larger cloud models and may perform poorly on some tasks. That warning is a reason to evaluate task quality with the model you intend to deploy; local execution alone says nothing about whether the resulting agent will be reliable enough for its job.
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