| Tool |
License |
Stars |
Description |
| LlamaIndex |
MIT |
47.5k |
Document-focused RAG framework |
| CrewAI |
MIT |
47k |
Role-playing multi-agent system |
| Microsoft AutoGen |
MIT |
55.3k |
Conversational agent framework |
| Haystack |
Apache-2.0 |
15k+ |
NLP pipeline framework |
| Semantic Kernel |
MIT |
20k+ |
Microsoft’s LLM orchestration framework |
| Flowise |
Apache-2.0 |
50k+ |
Visual LangChain builder |
| Feature |
LangChain |
LlamaIndex |
CrewAI |
AutoGen |
| Primary Focus |
General LLM apps |
RAG/Documents |
Role-playing agents |
Conversational agents |
| Learning Curve |
Moderate |
Moderate |
Easy |
Steep |
| Integrations |
100+ |
50+ |
50+ |
Growing |
| Enterprise Platform |
✅ (LangSmith) |
❌ |
✅ (AMP Suite) |
❌ |
| Visual Builder |
❌ |
❌ |
✅ |
❌ |
| JavaScript Support |
✅ |
✅ |
❌ |
⚠️ (.NET) |
| Community Size |
500k+ |
100k+ |
100k+ |
Growing |
| Standalone |
✅ |
✅ |
✅ |
✅ |
- ✅ You need extensive integrations (100+ connectors)
- ✅ You want mature ecosystem with large community
- ✅ You need LangSmith for debugging/monitoring
- ✅ You want composable, modular architecture
- ✅ You need both Python and JavaScript support
- ✅ You’re building production applications
¶ LlamaIndex
- ✅ You focus primarily on document Q&A and RAG
- ✅ You need advanced document processing and OCR
- ✅ You want simpler API for RAG use cases
- ✅ You prefer document-centric approach
- ✅ You need role-playing agents with backstories
- ✅ You want standalone framework (no LangChain dependency)
- ✅ You value high performance for agent workflows
- ✅ You prefer simpler agent orchestration
- ✅ You prefer conversational agent paradigm
- ✅ You need .NET/C# support
- ✅ You’re in Microsoft ecosystem
- ⚠️ Note: Microsoft now recommends “Agent Framework” for new projects
- ✅ You need production-ready NLP pipelines
- ✅ You want strong document retrieval focus
- ✅ You prefer Apache-2.0 license
- ✅ You’re building search-heavy applications
- ✅ You’re in Microsoft/.NET ecosystem
- ✅ You need C# integration
- ✅ You want Microsoft’s enterprise support
- ✅ You’re building Azure-centric applications
- ✅ You want visual/no-code interface
- ✅ You prefer drag-and-drop workflow building
- ✅ You need quick prototyping without coding
- ✅ You want LangChain compatibility with visual UI
¶ From LangChain to LlamaIndex
- Simpler API for RAG use cases
- Better document processing capabilities
- Fewer integrations overall
- More focused on document-centric workflows
- Simpler agent orchestration
- Better performance for agent workflows
- Standalone (no LangChain dependency)
- Role-playing agent paradigm
¶ From LlamaIndex to LangChain
- More integrations available
- LangSmith for observability
- More flexible architecture
- Larger community support
- More extensive integrations
- LangSmith platform
- More mature ecosystem
- Better for complex workflows
Many projects use multiple frameworks together:
# LangChain + LlamaIndex
from llama_index.core import VectorStoreIndex
from langchain.agents import create_agent
# Use LlamaIndex for RAG
index = VectorStoreIndex.from_documents(docs)
retriever = index.as_retriever()
# Use LangChain for agent orchestration
agent = create_agent(
model="gpt-4o",
tools=[retriever]
)
# LangChain + CrewAI
from crewai import Agent, Crew
from langchain_openai import ChatOpenAI
# Use CrewAI for agent collaboration
llm = ChatOpenAI(model="gpt-4o")
researcher = Agent(
role="Researcher",
llm=llm, # LangChain LLM
tools=[langchain_tool]
)