AI orchestration frameworks connect LLMs to tools, data, and each other. They handle the plumbing between a user's request and a useful response: retrieving context, calling models, executing tools, managing state, and routing between agents.
The framework landscape in 2026 has matured but also fragmented. Seven frameworks have meaningful adoption, each with a different philosophy. This guide compares them head-to-head so you can pick the right one without wasting weeks evaluating each.
Framework Comparison at a Glance
| Framework | Primary Use Case | Complexity | GitHub Stars | Best For |
|---|---|---|---|---|
| LangChain | General-purpose orchestration | High | 98K+ | Teams wanting maximum flexibility |
| LlamaIndex | Data-centric RAG | Medium | 38K+ | RAG-heavy applications |
| CrewAI | Multi-agent systems | Low | 25K+ | Teams building agent teams |
| AutoGen (Microsoft) | Conversational agents | Medium | 36K+ | Multi-agent conversations |
| Semantic Kernel (Microsoft) | Enterprise AI integration | Medium | 22K+ | .NET/Java enterprises |
| Haystack (deepset) | Production NLP pipelines | Medium | 18K+ | Production search & QA |
| DSPy (Stanford) | Programmatic prompt optimization | High | 20K+ | Research and prompt optimization |
LangChain: The Kitchen Sink
What It Does
LangChain is the most comprehensive orchestration framework. It provides abstractions for everything: prompt templates, LLM calls, chains, agents, memory, tools, retrievers, and output parsers. LangGraph (its agent framework) adds stateful, graph-based agent orchestration. LangSmith provides observability.
Strengths
- Ecosystem breadth. 700+ integrations. If a tool, database, or model exists, LangChain probably has a connector.
- LangGraph. The most mature agent framework for building complex, stateful workflows with cycles, branching, and human-in-the-loop.
- Community. Largest community means the most tutorials, examples, and Stack Overflow answers.
- LangSmith integration. First-party observability that traces every step of your chain.
Weaknesses
- Complexity. The abstraction layers add cognitive overhead. Simple tasks require understanding chains, runnables, and LCEL syntax.
- API instability. Breaking changes between versions have frustrated developers.
- Performance overhead. Abstraction layers add latency. Direct API calls are measurably faster for latency-sensitive applications.
When to Choose LangChain
You need a general-purpose framework that can handle any AI workflow. You want agent capabilities (LangGraph). You value ecosystem breadth over simplicity. See our LangChain vs LlamaIndex guide or LangChain Alternatives page.
LlamaIndex: Data-First RAG
What It Does
LlamaIndex started as a framework for connecting LLMs to data and evolved into a comprehensive RAG platform. It handles data ingestion, indexing, retrieval, and response synthesis. LlamaParse handles complex document parsing (PDFs, tables, images).
Strengths
- RAG focus. The best framework for retrieval-augmented generation. Data connectors, chunking strategies, index types, and query engines are all first-class concepts.
- LlamaParse. The best document parser in the ecosystem. Handles tables, images, and complex layouts.
- Simpler mental model. Data-in, query-out is easier to reason about than LangChain's chain-of-everything approach.
- Production-ready. LlamaCloud provides managed indexing and retrieval infrastructure.
Weaknesses
- Narrower scope. Less suitable for non-RAG use cases. Complex agent workflows are better served by LangGraph.
- Agent capabilities maturing. Workflows is newer and less battle-tested than LangGraph.
- Smaller ecosystem. Fewer integrations than LangChain.
When to Choose LlamaIndex
Your primary use case is RAG or knowledge-base Q&A. You're working with complex documents. You want a simpler framework than LangChain. See our LlamaIndex Alternatives.
CrewAI: Multi-Agent Made Simple
What It Does
CrewAI takes a role-based approach to multi-agent systems. You define agents with specific roles (Researcher, Writer, Analyst), assign them tasks, and let them collaborate.
Strengths
- Simplicity. Role-task-crew mental model is intuitive. Working multi-agent system in under 50 lines of code.
- Fast prototyping. Gets you to a working demo faster than any other multi-agent framework.
- Built-in tool integration. Web search, file operations, and API calls available as pre-built tools.
Weaknesses
- Limited control flow. Automatic agent interactions limit customization for complex workflows.
- Token consumption. Multi-agent conversations consume lots of tokens.
- Production maturity. Newer than LangChain and LlamaIndex.
When to Choose CrewAI
You want multi-agent capabilities without LangGraph's complexity. Your use case maps naturally to roles and tasks. See our LangChain vs CrewAI guide.
AutoGen: Conversational Multi-Agent
What It Does
Microsoft's AutoGen builds multi-agent systems through conversation. Agents communicate by sending messages to each other, with support for human-in-the-loop, code execution, and tool use.
Strengths
- Conversational paradigm. Natural for code review, brainstorming, debate, and collaborative analysis.
- Code execution. Built-in sandboxed code execution lets agents write and run code.
- Microsoft ecosystem. Tight Azure OpenAI integration.
- AutoGen Studio. Visual interface for designing agent workflows without code.
Weaknesses
- Conversation overhead. Multi-turn agent conversations consume significant tokens and add latency.
- Less suitable for structured pipelines. Conversational framework adds unnecessary complexity for linear workflows.
- Architecture transitions. The 0.2 to 0.4 migration caused community fragmentation.
When to Choose AutoGen
You're building collaborative AI systems where agents need to discuss or iteratively refine outputs. You're in the Microsoft ecosystem. You want visual agent design (AutoGen Studio).
Semantic Kernel: Enterprise Integration
What It Does
Microsoft's SDK for integrating AI into existing enterprise applications. Supports C#, Python, and Java, making it the only major framework with first-class .NET and Java support.
Strengths
- .NET and Java support. The only production-ready option with native C# and Java support.
- Enterprise integration patterns. Designed for adding AI to existing applications.
- Plugin system. Clean abstraction for AI capabilities as composable plugins.
- Azure native. Deep integration with Azure AI services.
Weaknesses
- Smaller Python community. Most Python developers choose LangChain or LlamaIndex.
- Less flexible. Enterprise-focused design limits unconventional architectures.
- Microsoft-centric. Best with Azure. Less compelling on GCP or AWS.
When to Choose Semantic Kernel
Your application is C# or Java. You're adding AI to an existing enterprise application. You're in the Microsoft/Azure ecosystem.
Haystack: Production NLP Pipelines
What It Does
Haystack by deepset builds production NLP and LLM applications using a pipeline-based architecture. Components (retrievers, generators, rankers) connect into directed acyclic graphs.
Strengths
- Production-first design. Components are type-checked, serializable, and testable.
- Pipeline architecture. DAG-based pipelines are easier to reason about, test, and deploy than chains.
- Document stores. First-class support for Elasticsearch, OpenSearch, Weaviate, Pinecone, Qdrant, and pgvector.
Weaknesses
- Smaller community. Fewer tutorials and third-party resources than LangChain.
- Less agent support. Pipeline architecture is less natural for dynamic agent behavior.
When to Choose Haystack
You're building production search or Q&A systems. You value type safety and testability. Your workflow is a structured pipeline, not a dynamic agent.
DSPy: Programmatic Prompt Optimization
What It Does
DSPy from Stanford takes a radically different approach. Instead of manually writing prompts, you define input/output signatures and let DSPy optimize the prompt automatically using machine learning techniques.
Strengths
- Automatic prompt optimization. Can find prompts that outperform hand-written ones.
- Composability. DSPy modules compose cleanly like neural network layers.
- Model-agnostic optimization. Re-run the optimizer when switching models instead of rewriting prompts.
- Research-backed. Published at NeurIPS.
Weaknesses
- Steep learning curve. DSPy's paradigm is unfamiliar to most developers.
- Optimization requires examples. You need labeled training data.
- Less ecosystem. Fewer integrations, tutorials, and community resources.
- Production deployment. Research-oriented; deployment patterns are less documented.
When to Choose DSPy
You have labeled data. You're spending significant time manually optimizing prompts. You want reproducible, measurable prompt performance. You're comfortable with ML-style workflows.
Decision Framework
1. What's your primary use case?
- RAG / Knowledge Q&A: LlamaIndex (first choice) or LangChain
- Multi-agent systems: CrewAI (simple) or LangGraph (complex)
- Production search: Haystack
- Enterprise integration (.NET/Java): Semantic Kernel
- Prompt optimization: DSPy
- General-purpose / unsure: LangChain
2. What's your team's experience level?
- New to AI frameworks: CrewAI (easiest) or LlamaIndex (moderate)
- Experienced Python developers: LangChain, Haystack, or DSPy
- Enterprise/Java/.NET background: Semantic Kernel
3. Prototype or production?
- Prototype: CrewAI or LangChain (fastest to demo)
- Production: Haystack, LangChain + LangGraph, or LlamaIndex (most battle-tested)
Can You Use Multiple Frameworks?
Yes, and it's more common than you'd think. A typical pattern:
- LlamaIndex for document ingestion and indexing
- LangChain/LangGraph for agent orchestration and tool use
- LangSmith for observability across both
The frameworks are increasingly interoperable. LlamaIndex retrievers can plug into LangChain chains. DSPy modules can wrap LangChain components. Don't feel locked into one framework for your entire stack.
For more on building with these frameworks, see our Building AI Agents guide, LLM Orchestration Frameworks directory, and the AI Agent Frameworks comparison.