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There is no single best free AI agent framework on GitHub: the right choice depends on how your workflow handles state, routing, persistence, debugging, and model providers. Framework code may be free to use under its license, but model calls and production infrastructure can still cost money. The options below are documented fits—not a ranking based on hands-on tests.
Do you need an agent framework at all?
Use a framework when your application needs explicit orchestration—for example, routing between tools, coordinating multi-step work, maintaining state, or pausing for human approval. If the workflow is straightforward, direct model and tool calls may be easier to understand and maintain. Choose the least complex approach that gives you the control and recovery your application needs.
Popularity alone is a poor selection rule. A framework’s documentation, repository activity, or community visibility does not establish that it will fit your workflow or perform better on it. The June 6, 2026 comparison from LangChain distinguishes the options by their documented design and ecosystem; it is not a controlled benchmark of equivalent applications. Read the comparison.
Which free GitHub agent framework fits your workflow?
| Framework | Documented fit | Questions to ask |
|---|---|---|
| LangGraph | Stateful, cyclic orchestration with explicit control over multi-step agent workflows. LangChain also provides broader LLM application components. | Do you need loops, checkpoints, durable state, or human approval points? Can your team debug at this level of orchestration? |
| CrewAI | Role-based teams of agents and tasks with distinct responsibilities. | Does the task genuinely divide into roles, or would a direct workflow be simpler? |
| Microsoft Agent Framework | Python and .NET agent workflows with graph-based orchestration, aligned with Microsoft’s ecosystem. It is presented as the successor direction for AutoGen and Semantic Kernel. | Are you invested in Microsoft tooling? Do you need .NET support or a path from a predecessor? |
| Google ADK | An open-source, code-first toolkit with Google Cloud development and deployment options. | Would Google Cloud integration help? How much flexibility across model providers do you need? |
| OpenAI Agents SDK | A smaller set of primitives for tool calling and delegation, particularly for workflows built around OpenAI APIs. | Would a small API surface be easier to use than a general orchestration system? Where will persistence and durable execution live? |
| LlamaIndex Workflows | Event-driven orchestration suited to document-centric and data-intensive pipelines. | Does your project already use LlamaIndex for loading, parsing, or retrieval? Does event-driven control fit the task? |
| Mastra | A TypeScript-focused agent and application framework. The comparison describes licensing as partial, with different licenses for core and enterprise directories. | Is TypeScript a requirement? Have you checked the license for each component you intend to use? |
These are fit heuristics based on documented distinctions, not evidence that one framework is faster, more reliable, or better overall. Check each project’s current repository, documentation, and license before adopting it.
Compare the operational details before you commit
Start with the same questions for every candidate. A framework that looks convenient in a tutorial may leave essential production responsibilities to your application.
- Language and ecosystem: Does it support your team’s language and deployment environment? Microsoft Agent Framework, for example, documents Python and .NET support; Mastra is TypeScript-focused.
- Model providers: Which providers and models can you use, and how difficult is it to change them? A workflow centered on one provider may make a narrower SDK practical; a multi-provider requirement makes flexibility more important.
- Control flow: Can you represent the workflow’s branching, loops, tool routing, and approval points clearly? LangGraph emphasizes stateful cyclic orchestration, while the OpenAI Agents SDK emphasizes a smaller set of primitives.
- State, persistence, and recovery: Determine what the framework persists, how execution resumes after interruption, and what you must implement yourself. Do not assume that an agent loop automatically provides durable execution.
- Human review: If the workflow must pause for approval or correction, verify how that works in the exact framework version and deployment pattern you plan to use.
- Tracing and evaluation: Check how you will inspect runs, identify tool failures, and evaluate output quality. Separate framework features from optional hosted observability services.
- Deployment and license: Confirm the target runtime, supported integrations, and license terms for the exact packages and directories you plan to use.
For any two finalists, write down what each requires you to build or operate yourself—especially persistence, recovery, tracing, and provider integrations. That trade-off is more useful than a universal winner.
How much does a free framework cost to run?
“Free” describes the framework’s software license, not necessarily the full application. Model API calls, hosted compute, storage, data services, and optional commercial products may add recurring costs. Read the license for the exact components you use, then estimate operating costs from your expected workload and the current provider pricing.
Recommended Free Tools
For the OpenAI Agents SDK specifically, LangChain’s June 6, 2026 comparison says: “The SDK itself is free, but production costs are driven entirely by OpenAI API usage.” That is LangChain’s description of the SDK’s cost model on that date, not a promise that every deployment has no other expenses. API pricing can change. See LangChain’s comparison.
Observability can be a separate cost decision. LangChain’s LangSmith pricing page, accessed October 7, 2026, lists a Developer plan at $0 per seat per month with up to 5,000 base traces per month, followed by pay-as-you-go. Treat those figures as a dated pricing snapshot and confirm the current terms before choosing a plan. Check LangSmith pricing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should Microsoft-stack teams know about AutoGen and Semantic Kernel?
Microsoft Agent Framework is presented as Microsoft’s successor direction for AutoGen and Semantic Kernel. If you already use either predecessor, check Microsoft’s current support and migration guidance before starting new long-term work; do not infer a migration schedule or compatibility guarantee from the successor positioning alone. Review Microsoft Agent Framework documentation.
Quick Recap
A practical way to make the choice
- Describe the workflow first. List its steps, branches, tool calls, approval points, and failure cases without naming a framework.
- Decide which capabilities are essential. Mark whether you need durable state, recovery, multiple providers, .NET or TypeScript support, document pipelines, or a specific cloud environment.
- Shortlist by fit. Use the comparison table to identify frameworks whose documented approach matches those requirements; do not shortlist based only on popularity.
- Verify current project details. Check the repository and official documentation for supported languages, providers, deployment options, versions, and license terms for the packages you would actually use.
- Map what remains yours to operate. Identify how the application will handle persistence, retries, secrets, model spending, monitoring, and recovery if the framework does not provide the behavior you need.
- Choose the simplest adequate option. Prefer a smaller abstraction when it meets your control and operational needs; use richer orchestration when the workflow genuinely needs it.
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