Choosing the wrong agent framework adds months of rework. This guide covers the leading options and how to match them to your specific use case.
What an agent framework does
An agent framework is a software library that provides the scaffolding for building AI agents: managing the agent loop, routing between tools, handling memory, and orchestrating multi-agent workflows.
Without a framework, developers build these components from scratch, which is time-consuming and error-prone. Frameworks package common patterns so developers can focus on the specific logic of their use case rather than the plumbing.
The tradeoff is that frameworks impose abstractions and patterns that work well for the use cases they were designed for but can become constraints for unusual requirements. Choosing the right framework starts with understanding what your use case requires.
LangChain and LangGraph overview
LangChain is the most widely adopted agent framework and has the largest community and ecosystem. It provides abstractions for chains (sequential LLM calls), agents (LLM-driven tool selection loops), and retrieval (RAG integration).
LangChain strengths: extensive integrations with third-party tools and databases, large community with active documentation and examples, strong support for RAG patterns, and compatibility with most commercial LLMs.
LangChain weaknesses: the abstraction layers can be complex and opaque for developers new to the framework. Debugging multi-step failures requires understanding the framework’s internals. And the API has changed significantly across versions, which creates maintenance overhead.
LangGraph is LangChain’s newer graph-based orchestration layer, designed specifically for building stateful, multi-step agents with explicit control flow. It is better suited than core LangChain for complex agents with branching logic and persistent state. For new projects requiring complex agent orchestration, LangGraph is generally preferred over the older LangChain agent abstractions.
LangChain/LangGraph is a strong choice for teams that want a well-supported framework with broad integrations and are comfortable with a moderate learning curve.
AutoGen overview
AutoGen, developed by Microsoft Research, is designed specifically for multi-agent conversations. Its model is built around agents that communicate with each other through a conversation interface, which makes it particularly well-suited for tasks that benefit from multi-agent debate, review, or collaboration.
AutoGen strengths: excellent for multi-agent workflows where agents need to interact with each other, strong support for human-in-the-loop patterns, and a relatively straightforward model for defining agent roles and conversations.
AutoGen weaknesses: less mature ecosystem than LangChain, fewer pre-built tool integrations, and the conversation-centric model is less natural for linear workflow automation than for collaborative reasoning tasks.
AutoGen is the better choice when your use case genuinely benefits from multiple agents reasoning together, such as code review (author agent and reviewer agent), research synthesis (multiple specialist agents debating findings), or complex analysis (multiple analytical perspectives combined).
Other notable frameworks
CrewAI is designed specifically for role-based multi-agent systems where each agent has a defined role and goal. It is simpler than LangChain for specific multi-agent use cases and has been growing rapidly in 2026. It is a good choice for teams building agents around clearly defined roles (researcher, writer, reviewer) without needing the full complexity of LangGraph.
LlamaIndex (formerly GPT Index) focuses primarily on data retrieval and RAG rather than general agent orchestration. It is the best choice for projects where the primary requirement is sophisticated knowledge retrieval from large document collections, and agent functionality is secondary.
Semantic Kernel (Microsoft) is an enterprise-oriented framework with strong integration with Microsoft Azure, Office 365, and enterprise software ecosystems. For organizations heavily invested in the Microsoft stack, it offers tighter integration than framework alternatives.
Haystack (deepset) focuses on document processing and question-answering pipelines. It is particularly strong for organizations building retrieval-based applications on large document corpora.
How to choose a framework
Three questions narrow the choice effectively.
What is the primary capability you need? For RAG and knowledge retrieval: LlamaIndex or Haystack. For multi-agent collaboration: AutoGen or CrewAI. For general-purpose agents with broad tool integration: LangChain/LangGraph. For Microsoft ecosystem integration: Semantic Kernel.
What is your team’s technical experience? LangChain has the most extensive documentation and community resources, which makes the learning curve more manageable. AutoGen and CrewAI are simpler for specific use cases. If your team is new to agent frameworks, start with the one that has the most examples relevant to your use case.
Do you need multi-agent coordination? Single-agent workflows can use any framework. The choice is primarily about tool integration. Multi-agent workflows benefit from AutoGen or CrewAI’s purpose-built multi-agent models, or LangGraph for complex state management.
Framework comparison table
| Framework | Primary use case | Multi-agent | Integrations | Learning curve |
|---|---|---|---|---|
| LangChain/LangGraph | General purpose, RAG | Yes (LangGraph) | Extensive | Moderate |
| AutoGen | Multi-agent collaboration | Strong | Limited | Low-moderate |
| CrewAI | Role-based multi-agent | Yes | Growing | Low |
| LlamaIndex | RAG and retrieval | Limited | Moderate | Low |
| Semantic Kernel | Microsoft ecosystem | Limited | Microsoft-focused | Moderate |
| Haystack | Document QA pipelines | No | Moderate | Low-moderate |
No framework is universally best. The right choice is determined by your specific use case requirements and team capabilities.
Frequently asked questions
Can I switch frameworks after starting a project?
Yes, but it typically requires significant rework. Frameworks impose patterns and abstractions that are woven throughout the codebase. Switching after substantial development is expensive. Invest time in framework selection before starting to build.
Do I need a framework, or can I build agents without one?
For simple agents with one or two tools, building without a framework is feasible and avoids framework-specific complexity. For agents with multiple tools, persistent memory, and multi-step reasoning, a framework saves significant development time and provides tested solutions to common problems.
Are these frameworks compatible with all LLMs?
Most major frameworks support multiple LLMs including Claude, GPT-4, Gemini, and open-source models. LangChain has the broadest LLM compatibility. Check that your chosen LLM is supported before committing to a framework.
Want to choose the right framework for your agent project?
Framework selection is a consequential early decision. Getting it right saves months of rework. Getting it wrong means rebuilding on a better foundation later.
Path one: prototype before committing. Build a minimal prototype of your use case in the top one or two framework candidates. The practical experience of building reveals fit issues that documentation does not.
Path two: work with Phos AI Labs. If you want expert architecture guidance for your specific agent use case, Phos AI Labs is a CCA-F certified Claude implementation partner. Thirty minutes, no deck. Start here.