Contents
- AI Agent Framework: Overview
- Framework Comparison Table
- LangChain
- LlamaIndex
- CrewAI
- AutoGen
- Claude Agent SDK
- OpenAI Agents SDK
- Architecture Patterns
- Production Readiness
- FAQ
- Related Resources
- Sources
AI Agent Framework: Overview
Six frameworks dominate agent development as of March 2026. LangChain is the volume play: most jobs, most tutorials, largest ecosystem. LlamaIndex specializes in retrieval-augmented generation (RAG) agents. CrewAI abstracts orchestration (multi-agent teams). AutoGen handles conversational agents with model routing. Claude Agent SDK is closed (tightly integrated with Anthropic's models). OpenAI Agents SDK is new (2025), vendor-locked to GPT-4. Choosing one hinges on whether building retrieval agents, orchestration agents, or direct model integration.
Framework Comparison Table
| Framework | Best For | Learning Curve | Maturity | Multi-Agent | Tool Calling | License |
|---|---|---|---|---|---|---|
| LangChain | Retrieval + workflows | Moderate | Stable (2.0) | Basic chains | Native | MIT |
| LlamaIndex | RAG agents | Low | Stable (0.10) | Tree/graph queries | Via tools | MIT |
| CrewAI | Multi-agent teams | Moderate | Stable | Strong orchestration | Native | MIT |
| AutoGen | Conversational agents | High | Research (0.2.x) | Explicit groups | Native | Apache |
| Claude Agent SDK | Claude models | Low | Stable | Pattern (not lib) | Native | Proprietary |
| OpenAI Agents SDK | GPT-4 only | Moderate | Beta | Basic routing | Native | Proprietary |
Data as of March 2026 from official repos and documentation.
LangChain
What It Does
LangChain is a framework for composing LLM workflows. Chains (sequences of steps), agents (models deciding tool use), and retrieval pipelines. It's the most popular framework: 60K+ stars on GitHub.
Strengths
Ecosystem. Integrations with 500+ tools, data loaders, and vector stores (OpenSearch, Weaviate, Pinecone, FAISS, Milvus). If another framework exists, LangChain probably wraps it.
Maturity. Version 2.0 (released late 2024) stabilized the API. Fewer breaking changes than 0.x versions.
Tool calling. Native tool definition and binding to OpenAI, Anthropic, and Groq models. Models return structured tool calls; LangChain executes them automatically.
Community. Largest community = most tutorials, most third-party tools, fastest bug fixes.
Weaknesses
Complexity. Many abstractions (Chain, LLMChain, AgentExecutor, OutputParser). Beginners get lost. Migration from 0.x to 2.0 required code refactoring for some projects.
Single-agent focus. Multi-agent workflows are possible (via manual routing) but not the intended use case. Better frameworks exist for team coordination.
Performance. LangChain is a thin wrapper on model APIs. No performance benefit. No execution optimization. It orchestrates, it doesn't optimize.
Code Example
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
@tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for {query}"
agent = ChatOpenAI(model="gpt-4").bind_tools([search])
agent.invoke({"messages": [{"role": "user", "content": "Find Python tutorials"}]})
Simple. Tool is a function. Model is bound to tools. Invoke returns tool calls.
LlamaIndex
What It Does
LlamaIndex (formerly GPT Index) specializes in RAG. Document loaders, vector store abstractions, query engines, and retrieval-augmented generation chains. It's tightly integrated with the indexing problem.
Strengths
RAG focus. Purpose-built for RAG agents. Query engines know how to traverse indices (tree, graph, keyword). Best-in-class abstractions for document chunking, embedding, and retrieval.
Simple API. Load documents, index them, query. Three lines of code for basic RAG. Lower learning curve than LangChain for retrieval workflows.
Integration with LLMs. Works with any model (OpenAI, Anthropic, local, Groq). Not vendor-locked.
Production patterns. Query engines cache results, batch embed documents, support async operations out of the box.
Weaknesses
Limited orchestration. Good at retrieving documents, weak at orchestrating multi-step workflows. If the task involves retrieval followed by complex reasoning, LlamaIndex retrieval + LangChain reasoning is needed.
Smaller ecosystem. Fewer third-party integrations than LangChain. Tool definitions require more manual wiring.
Maturity. Still moving fast (minor version bumps). API changes less frequently than LangChain 0.x, but stability not guaranteed.
Code Example
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
docs = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query("What is LlamaIndex?")
Three lines. Load, index, query. No explicit embedding steps or vector store setup (defaults to in-memory).
CrewAI
What It Does
CrewAI abstracts multi-agent workflows. Agents (with roles, goals, tools) work together on tasks. Agent Manager orchestrates who talks to whom.
Strengths
Multi-agent orchestration. First-class support for agent teams. Define agents with roles and goals. CrewAI coordinates communication. No manual routing needed.
Human-like workflows. Agents have personalities (role, goal, backstory). System message is auto-generated. Less boilerplate than writing system prompts manually.
Memory. Shared team memory (what other agents have done) helps coordination. Agents reference previous steps without explicit prompt engineering.
Simplicity. Clean API. Less boilerplate than AutoGen.
Weaknesses
Less research-grade. AutoGen (Microsoft) is more battle-tested in academic settings. CrewAI is newer (2023), production-ready but less proven at scale.
Single-model assumption. Designed for one model serving all agents. Mixing models (GPT-4 for reasoning, Claude for coding) requires workarounds.
Tool support. Tool calling works but integration is less polished than LangChain. Less documentation on edge cases.
Code Example
from crewai import Agent, Task, Crew, Process
research_agent = Agent(
role="Researcher",
goal="Research the topic",
tools=[search_tool],
llm=ChatOpenAI(model="gpt-4")
)
task = Task(description="Investigate AI safety", agent=research_agent)
crew = Crew(agents=[research_agent], tasks=[task], process=Process.sequential)
crew.kickoff()
Agent is defined with role and goal. Task assigned to agent. Crew orchestrates execution.
AutoGen
What It Does
AutoGen (Microsoft) is a multi-agent framework for conversational agents. Agents exchange messages. An agent's response can be another agent's input. Flexible routing and conditional execution.
Strengths
Research-grade. Built by Microsoft Research. Published in top venues. Widely used in academic agent research.
Flexible agent types. ConversableAgent (basic), UserProxyAgent (human feedback loop), AssistantAgent (LLM-backed). Mix and match to build custom workflows.
Function calling. Native support for OpenAI function calling, Anthropic tool use, and custom function schemas.
Nested agents. Agents can call other agents. Multi-level hierarchies possible. Complex workflows can be expressed cleanly.
Weaknesses
High learning curve. More low-level control = more code required. Beginners find AutoGen verbose.
Active research. Version 0.2.x is still changing. API stability not guaranteed (though team is more careful now).
Less turnkey. Doesn't come with built-in recipes for common patterns (RAG, web search, etc.). Users implement these themselves.
Code Example
from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
config_list = config_list_from_json("OAI_CONFIG_LIST")
assistant = AssistantAgent("assistant", llm_config={"config_list": config_list})
user_proxy = UserProxyAgent("user", human_input_mode="TERMINATE")
user_proxy.initiate_chat(
assistant,
message="Solve the math problem: 2x + 3 = 7"
)
Two agents. User sends message. Assistant responds. Loop until termination.
Claude Agent SDK
What It Does
Anthropic's Agent SDK (released 2025) is a pattern library, not a framework. Shows how to build agents using Claude. Tight integration with tool_use (Claude's native tool calling).
Strengths
Claude-native. Uses Claude's native tool_use (not a wrapper). Direct integration = fewer bugs, better performance.
Agentic loop simplicity. Standard pattern (call model, execute tools, repeat) is shown clearly. Beginners understand the flow.
Cost-effective. Works with Claude Haiku ($1/$5 per million tokens). Cheaper than GPT-4 agents for simple tasks.
Production patterns. Includes guidance on error handling, tool retry logic, and context management. Opinionated in the right ways.
Weaknesses
Closed ecosystem. Works only with Claude. Teams using GPT-4 or other models must use other frameworks.
Not a library. Claude Agent SDK is documentation + examples, not a pip-installable package. Users copy patterns into their code.
Limited multi-agent. Single agent with tools. Multi-agent orchestration is possible (manual routing) but not the design goal.
Smaller community. Newer framework. Fewer tutorials and third-party tools than LangChain.
Code Pattern
from anthropic import Anthropic
client = Anthropic()
tools = [
{
"name": "calculator",
"description": "Perform arithmetic",
"input_schema": {"type": "object", ...}
}
]
messages = [{"role": "user", "content": "What is 2+2?"}]
response = client.messages.create(
model="claude-opus-4-6",
max_tokens=1024,
tools=tools,
messages=messages
)
Standard agentic loop. Call model with tools. Execute tool calls. Append results. Repeat.
OpenAI Agents SDK
What It Does
OpenAI's Agents SDK (beta, 2025) automates the agentic loop for OpenAI models. Similar to Claude Agent SDK: documentation + SDK helpers. Tight integration with GPT-4.
Strengths
GPT-4 integration. Direct APIs for tool calling, parallel function execution, and context management.
Vendor integration. Comes with integrations to OpenAI's add-ons (web browsing, code interpreter, file retrieval). Immediate access to web search, Python execution.
Simplicity. Agentic loop is abstracted. Call agent, get result. No manual tool execution.
Weaknesses
Vendor lock-in. GPT-4 only (OpenAI's business model). Switching models requires rewriting.
Beta status. Early. APIs may change. Not recommended for production workloads requiring stability.
Limited documentation. Smaller community than LangChain. Fewer examples on GitHub.
Cost. GPT-4 is 10x more expensive than Claude Haiku. Agent costs scale with token usage.
Architecture Patterns
Agentic Loop (The Core Pattern)
All frameworks implement the same fundamental loop:
- Initialize: Combine model instructions + tool definitions.
- Call LLM: Send model the current state (messages, tool definitions).
- Model decides: Generate text OR request tool call.
- Execute: If tool call, run the tool and capture result.
- Append result: Add tool output to message history.
- Repeat: Loop until model stops requesting tools or max iterations reached.
Difference is abstraction level:
- Claude Agent SDK: Shows loop explicitly (50 lines of code). Users implement error handling and state management.
- LangChain: Abstracts loop (AgentExecutor class). Single method call (
agent.invoke()), all loop logic hidden. - CrewAI: Hides loop completely. Users define agents + tasks; framework runs loop transparently.
- AutoGen: Hybrid. Explicit message passing, implicit tool execution.
Tool Calling Standards
Different models use different tool formats:
OpenAI function calling: {"type": "function", "function": {"name": "search", "arguments": "{...}"}}
Anthropic tool_use: {"type": "tool_use", "name": "search", "input": {...}}
Groq tools: OpenAI-compatible (function calling).
Local models: Custom JSON schemas (no standard).
All frameworks normalize these. Define tool once, works with any model backend. LangChain handles conversion automatically; Claude Agent SDK requires manual model-specific handling.
Multi-Agent Coordination Patterns
LangChain (Sequential): Manual routing. If task is retrieval, call retrieval agent. If task is reasoning, call reasoning agent. Chains flow sequentially. Limited scaling (hard to manage 10+ agents).
CrewAI (Manager pattern): Agent Manager acts as orchestrator. Assigns tasks to agents, routes results. Scales to 5-10 agents. Best for functional teams (researcher, analyst, writer).
AutoGen (Message passing): Agents communicate directly. Agent A sends message, Agent B receives and responds. Flexible but requires explicit message management. Scales to hierarchical structures (manager agent, worker agents).
LlamaIndex (Retrieval): Not multi-agent framework. Single retrieval agent with pluggable tools. Best for RAG, not orchestration.
Comparison: When Each Pattern Wins
Sequential (LangChain): Best for pipelines. Step 1 = retrieve, Step 2 = reason, Step 3 = generate. Each step is a model call or tool invocation.
Manager pattern (CrewAI): Best for parallel work. Manager assigns task to multiple agents simultaneously. Agents work in parallel, report back.
Message passing (AutoGen): Best for negotiation/collaboration. Agent A proposes solution, Agent B critiques, Agent A refines. Asynchronous back-and-forth.
Production Readiness
State & Persistence
LangChain: SQLAlchemy-based memory (Redis, PostgreSQL, in-memory backends). Persistent chat history out of the box. Supports sliding windows (keep last N messages), summary windows (compress old messages). Battle-tested in production.
LlamaIndex: Stateless query engines. Persistence via external vector stores (Weaviate, Pinecone, Milvus). State is document indices, not conversation memory. Better for retrieval-heavy agents than conversational agents.
CrewAI: In-memory by default. Can wire in databases. Limited persistence patterns compared to LangChain. Better for short-lived agent teams than long-running assistants.
AutoGen: Manual memory management (users append to message history). No built-in persistence. Requires custom wiring for production (save/restore message lists).
Claude Agent SDK: No built-in persistence. Users implement storage layer (DynamoDB, PostgreSQL). Pattern assumes stateless API calls, not persistent agent.
For production: LangChain has most mature persistence patterns. AutoGen and Claude require careful engineering.
Error Handling
LangChain: Catches model errors (timeouts, rate limits, parse errors). Retry logic via LLMChain (exponential backoff). Output parsing validates model responses automatically.
CrewAI: Handles task failures, agent fallbacks. If agent fails, assigns task to backup agent (if defined). Reduces cascading failures.
AutoGen: Manual (try/catch required). Models can throw exceptions; user responsible for recovery.
LlamaIndex: Graceful degradation. Query engine falls back to simpler retrieval if complex queries fail.
CrewAI and LangChain better for production reliability (fewer crashes). AutoGen and Claude require explicit error boundaries.
Async & Concurrency
LangChain: Full async support (async/await). Can invoke multiple chains in parallel. Threading safe. Scales to 1000s of concurrent requests.
LlamaIndex: Async query engines. Batch index operations. Good for I/O-heavy workloads (vector store lookups).
CrewAI: Sequential by default. Parallel task execution possible (via Task dependencies). No async primitives in 0.x; coming in 1.0.
AutoGen: Synchronous message passing. Can wrap in async library (asyncio) but not natively concurrent. No built-in thread safety.
For high-throughput inference (1000+ concurrent users): LangChain and LlamaIndex. CrewAI and AutoGen for < 100 concurrent users.
Monitoring & Debugging
LangChain: LangSmith (paid, $29-299/month) for tracing, debugging, evaluating agents. Shows full call tree, latencies, token usage, errors. Integrates into observability stack (integrates with DataDog, New Relic).
Claude Agent SDK: Comes with detailed examples. No built-in logging. Print statements for debugging. Assumes users add observability layer.
AutoGen: Print-based debugging. No tracing library. Harder to diagnose issues in production.
CrewAI: Basic logging. Can wire in custom loggers. No built-in observability.
LangSmith is best-in-class for agent debugging in production. Worth the cost if building customer-facing agents. DIY logging acceptable for internal tools.
Deployment Patterns
LangChain: Stateless agent over API (FastAPI, Flask). Load-balances across instances. Works serverless (AWS Lambda with 15-min timeout limit).
LlamaIndex: Query engine as microservice. Index served separately (Weaviate on Kubernetes). Scales horizontally per component.
CrewAI: Single-instance agent server. Not horizontally scalable (team state not distributed). Deploy on VM or container, not serverless.
AutoGen: Single instance or Playwright-based distributed (experimental). Not cloud-native.
For scaling: LangChain easiest. Others require custom work.
FAQ
Which framework should I use for my startup?
If budget is tight (< $1k/month on API calls): Claude Agent SDK + Claude Haiku. If you need RAG: LlamaIndex. If you need multi-agent: CrewAI. If you need maximum flexibility: LangChain.
Can I mix frameworks? Use LlamaIndex retrieval with LangChain agents?
Yes. LlamaIndex retrieval pipelines are LLM-agnostic. Can be wrapped in LangChain tools. Common pattern in production.
What's the performance difference between frameworks?
None. All are thin wrappers on model APIs. LangChain might add 50-100ms overhead (network, Python overhead). Negligible. Bottleneck is model latency (2-10 seconds per call).
Is AutoGen production-ready?
Yes, but less polished than LangChain. Best for research and prototyping. For production at scale, LangChain is safer.
Should I use OpenAI Agents SDK or Claude Agent SDK?
Claude Agent SDK if using Claude (cheaper, simpler). OpenAI Agents if you need GPT-4. Don't use OpenAI Agents yet (beta, APIs may break).
How do I choose between CrewAI and LangChain for multi-agent?
CrewAI if you want high-level abstractions (agents with roles and goals). LangChain if you want fine-grained control. CrewAI is faster to prototype; LangChain is more flexible.
Can I deploy agents serverless (Lambda, Cloud Functions)?
Yes, all frameworks work serverless. Recommended: stateless agents that don't maintain conversation history. For stateful agents, use external database (Redis, DynamoDB). LangChain has better serverless memory backends than others.