Framework Comparison
Choosing the right agentic AI framework is one of the most consequential early decisions in a project. Each framework encodes a set of opinions about how agents should be built, and those opinions have downstream effects on development velocity, debugging, scalability, and maintenance. This page provides a structured comparison to help you make an informed choice.
Decision Matrix
The following table scores each framework across key dimensions on a 1-5 scale (5 = best).
| Dimension | LangChain | LangGraph | CrewAI | AutoGen | Semantic Kernel | LlamaIndex | Assistants API |
|---|---|---|---|---|---|---|---|
| Ease of Use | 4 | 3 | 5 | 3 | 3 | 3 | 5 |
| Flexibility | 4 | 5 | 3 | 4 | 3 | 4 | 2 |
| Multi-Agent | 2 | 5 | 5 | 5 | 2 | 2 | 1 |
| Production Readiness | 4 | 4 | 3 | 3 | 4 | 4 | 4 |
| RAG Capabilities | 4 | 3 | 2 | 2 | 3 | 5 | 3 |
| Observability | 5 | 5 | 3 | 3 | 4 | 3 | 3 |
| Community Size | 5 | 4 | 3 | 4 | 3 | 4 | 5 |
| Learning Curve | 3 | 2 | 4 | 3 | 3 | 3 | 4 |
| Provider Agnostic | 5 | 5 | 4 | 3 | 4 | 4 | 1 |
| Enterprise Support | 4 | 4 | 2 | 4 | 5 | 3 | 4 |
:::info How to Read This Table Higher scores are better. Learning Curve is inverted: a score of 4 means the framework is easy to learn. Provider Agnostic scores how well the framework works across LLM providers (OpenAI, Anthropic, open-source models). :::
Framework Profiles
LangChain
Architecture: Chain/Runnable composition via LCEL (LangChain Expression Language).
Sweet spot: Rapid prototyping, RAG pipelines, single-agent tool use, broad integrations.
Key trade-off: The massive ecosystem and frequent API changes mean you trade stability for breadth. The abstraction layers can make debugging non-trivial.
Best for: Teams that need to integrate with many different data sources and LLM providers quickly.
LangGraph
Architecture: Directed state graphs with nodes, edges, and conditional routing.
Sweet spot: Complex agent workflows with branching, cycles, persistence, and human-in-the-loop.
Key trade-off: More powerful than LangChain agents but requires thinking in terms of graph topology, which has a steeper learning curve.
Best for: Production agent systems that need explicit control flow, checkpointing, and multi-step reasoning with quality gates.
CrewAI
Architecture: Role-based multi-agent crews with sequential or hierarchical execution.
Sweet spot: Team-of-specialists workflows where agents have distinct roles and collaborate on well-defined tasks.
Key trade-off: Intuitive and fast to set up, but limited control flow (no cycles, no conditional branching). Harder to customize the internal orchestration logic.
Best for: Content production, research pipelines, and workflows that naturally decompose into role-based tasks.
AutoGen
Architecture: Conversable agents communicating through message passing, with group chat coordination.
Sweet spot: Code generation workflows, collaborative problem-solving, scenarios where agents iterate through conversation.
Key trade-off: Powerful conversation-based orchestration, but debugging multi-agent conversations is challenging and termination conditions require careful tuning.
Best for: Development automation, code review pipelines, and research scenarios where iterative refinement through dialogue is the core pattern.
Semantic Kernel
Architecture: Central kernel with a plugin/function model, automatic function calling.
Sweet spot: Enterprise .NET applications, Azure-native deployments, structured plugin ecosystems.
Key trade-off: Excellent enterprise integration and clean architecture, but smaller Python community and limited multi-agent capabilities.
Best for: Enterprise teams on the Microsoft stack building internal AI tools with Azure OpenAI.
LlamaIndex
Architecture: Data-first framework with query engines, indexes, and retrieval pipelines as first-class agent tools.
Sweet spot: Agents that primarily need to query, synthesize, and reason over complex data sources.
Key trade-off: Best-in-class RAG capabilities, but less suited for action-oriented agents that need to modify external systems.
Best for: Knowledge management, document Q&A, multi-source research, and any use case where intelligent data retrieval is the core capability.
OpenAI Assistants API
Architecture: Managed stateful agent runtime with built-in tools (Code Interpreter, File Search).
Sweet spot: Single-agent applications where you want minimal infrastructure and fast time-to-market.
Key trade-off: Fastest path to a working agent, but complete vendor lock-in to OpenAI with limited customization.
Best for: Prototypes, internal tools, and applications where OpenAI lock-in is acceptable and infrastructure simplicity is valued.
Detailed Comparison by Capability
State Management
| Framework | Approach | Persistence |
|---|---|---|
| LangChain | Stateless chains; state via external stores | Manual |
| LangGraph | Built-in state graph with typed reducers | Checkpointers (Postgres, Redis, memory) |
| CrewAI | Task context passing; agent memory | Built-in short/long-term memory |
| AutoGen | Conversation history in agent objects | Manual serialization |
| Semantic Kernel | Chat history object, memory plugin | Plugin-based (volatile, Azure) |
| LlamaIndex | Query engine state; chat memory | Manual or via storage context |
| Assistants API | Server-managed threads and messages | Automatic (OpenAI servers) |
Tool Integration
| Framework | Definition Style | Notable Features |
|---|---|---|
| LangChain | @tool decorator, BaseTool class | 300+ built-in tools, LangChain Hub |
| LangGraph | Inherits LangChain tools + ToolNode | Prebuilt tool node for graphs |
| CrewAI | @tool decorator, crewai_tools library | Role-scoped tool assignment |
| AutoGen | register_function, inline execution | Auto code execution, Docker sandbox |
| Semantic Kernel | @kernel_function decorator | Plugin-scoped, filter middleware |
| LlamaIndex | FunctionTool, QueryEngineTool | Query engines as tools |
| Assistants API | JSON schema function definitions | Code Interpreter, File Search |
Multi-Agent Capabilities
| Framework | Pattern | Coordination |
|---|---|---|
| LangChain | Limited; single agent focus | N/A |
| LangGraph | Subgraphs, multi-actor state graphs | Graph edges and conditional routing |
| CrewAI | Crew of role-based agents | Sequential, hierarchical, delegation |
| AutoGen | Group chat, nested chats | GroupChatManager, speaker selection |
| Semantic Kernel | Single kernel with multiple plugins | N/A (not multi-agent by design) |
| LlamaIndex | Limited; single agent with sub-queries | Sub-question decomposition |
| Assistants API | Single assistant per thread | N/A |
Recommendations by Use Case
"I need a simple chatbot with tool access"
Recommended: OpenAI Assistants API or LangChain
If you are committed to OpenAI, the Assistants API is the fastest path. If you need provider flexibility, use LangChain with a create_tool_calling_agent.
"I need complex, multi-step agent workflows"
Recommended: LangGraph
No other framework gives you the same level of control over branching, cycles, persistence, human-in-the-loop, and state management. It is the industry standard for production agent systems.
"I need multiple agents collaborating"
Recommended: CrewAI (simple teams) or LangGraph (complex orchestration)
CrewAI is faster to set up if your workflow maps to roles and tasks. LangGraph is more powerful if you need conditional routing between agents or complex coordination patterns.
:::tip Combining Frameworks You do not have to pick just one. A common production pattern is:
- LangGraph for orchestration and control flow
- LlamaIndex for RAG tools within the graph
- LangSmith for observability
These frameworks are designed to interoperate. :::
"I need intelligent document Q&A"
Recommended: LlamaIndex
LlamaIndex's query engines, sub-question decomposition, and index variety make it the best choice for complex document retrieval and synthesis.
"I need to build for a .NET / Azure enterprise"
Recommended: Semantic Kernel
Native .NET support, deep Azure OpenAI integration, and a clean plugin model make it the natural choice for Microsoft-stack enterprises.
"I need agents that write and execute code"
Recommended: AutoGen or OpenAI Assistants API
AutoGen's built-in code execution with Docker sandboxing is purpose-built for this. The Assistants API Code Interpreter is simpler but less customizable.
"I need a production-grade system with observability"
Recommended: LangGraph + LangSmith
The combination of LangGraph's explicit state management and LangSmith's tracing provides the most comprehensive production story.
Migration Considerations
| Migrating From | Migrating To | Difficulty | Key Changes |
|---|---|---|---|
| LangChain AgentExecutor | LangGraph | Medium | Rewrite loops as graph nodes and edges |
| Assistants API | LangGraph | High | Build your own state management and tool execution |
| CrewAI | LangGraph | Medium | Convert roles/tasks to nodes; add explicit routing |
| AutoGen | LangGraph | High | Restructure conversation patterns into graph topology |
:::warning Avoid Premature Optimization Start with the simplest framework that meets your requirements. A LangChain chain or an Assistants API agent can be built in an afternoon. Only move to LangGraph when you have concrete evidence that you need its control flow primitives. Premature adoption of complex frameworks slows down iteration. :::
Decision Flowchart
Use this flowchart to narrow down your choice:
-
Do you need multi-agent collaboration?
- Yes, with simple role-based tasks -> CrewAI
- Yes, with complex coordination -> LangGraph
- Yes, with code generation focus -> AutoGen
- No -> Continue to question 2
-
Is your primary need data retrieval and Q&A?
- Yes, across complex/multiple data sources -> LlamaIndex
- Yes, simple RAG -> LangChain
- No -> Continue to question 3
-
Are you in a Microsoft/.NET enterprise?
- Yes -> Semantic Kernel
- No -> Continue to question 4
-
Do you need complex control flow (branching, loops, human-in-the-loop)?
- Yes -> LangGraph
- No -> Continue to question 5
-
Do you want minimal infrastructure and are committed to OpenAI?
- Yes -> OpenAI Assistants API
- No -> LangChain
Interview Talking Points
- Walk through the decision flowchart for a given scenario and justify your framework choice.
- Explain why LangGraph has emerged as the production standard for complex agent systems.
- Discuss the trade-off between ease of use and flexibility across the spectrum (Assistants API vs. LangGraph).
- Describe a scenario where you would combine multiple frameworks (e.g., LangGraph + LlamaIndex).
- Explain why starting simple and migrating is generally better than choosing the most powerful framework upfront.
- Compare how different frameworks handle state management and why this matters for production reliability.