TL;DR

Built the same customer support agent in all three frameworks. Here's what matters:

• LangChain: Most mature, biggest ecosystem, steepest learning curve. Best for complex multi-agent systems. Rating: 4.1/5
• LlamaIndex: Simplest for RAG/search use cases, best DX for beginners. Limited beyond retrieval. Rating: 4.3/5
• Haystack: Enterprise-focused, strong pipeline abstractions, smaller community. Best for production NLP pipelines. Rating: 3.9/5

Quick recommendation: LlamaIndex for RAG-heavy projects, LangChain for complex agents, Haystack if already using enterprise NLP tools.

Time to first working agent: LlamaIndex (45 min), LangChain (2 hours), Haystack (3 hours).

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LangChain vs LlamaIndex vs Haystack: Agent Framework Comparison

You're building an AI agent. Do you use LangChain (everyone talks about it), LlamaIndex (heard it's simpler), or Haystack (enterprise-grade, apparently)?

I built the same agent -customer support bot with knowledge base retrieval, tool calling, and conversational memory -in all three frameworks. Timed development, measured performance, tracked bugs. Here's what actually matters.

Test Agent Specification

To compare fairly, built identical agent across all frameworks:

Requirements:

1. Answer customer questions using knowledge base (500 support docs)
2. Call external tools (check order status, create support ticket)
3. Maintain conversation history (remember context from earlier in chat)
4. Handle errors gracefully (fallback responses when retrieval fails)

Success criteria:

• Development time from zero to working agent
• Code complexity (lines of code, readability)
• Performance (latency, accuracy)
• Production-readiness (error handling, observability)

LangChain

Verdict: Most powerful, most complex. Use for sophisticated multi-agent systems.

Overview

LangChain is the 800-pound gorilla. Largest ecosystem, most integrations, most tutorials. Also the most complex.

Core concepts:

• Chains: Sequence of components (prompt → LLM → output parser)
• Agents: Autonomous decision-makers that use tools
• Memory: Conversation history management
• Retrievers: Document search (vector DB integration)
• Tools: External functions agents can call

Version tested: LangChain 0.1.0, LangGraph 0.0.20 (new orchestration framework)

Setup & Development Experience

Installation:

pip install langchain langchain-openai langchain-pinecone

Time to first working agent: 2 hours (including reading docs, trial and error).

Code complexity: 180 lines for full agent (retrieval + tools + memory + error handling).

Example code (simplified):

from langchain.agents import create_openai_functions_agent
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain.memory import ConversationBufferMemory
from langchain_pinecone import PineconeVectorStore

# Setup retrieval
vectorstore = PineconeVectorStore.from_existing_index(
    index_name="support-docs",
    embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Define tools
def check_order_status(order_id: str) -> str:
    # Call external API
    return f"Order {order_id} status: Shipped"

tools = [
    Tool(
        name="search_knowledge_base",
        description="Search support documentation for answers to customer questions",
        func=lambda q: retriever.get_relevant_documents(q)
    ),
    Tool(
        name="check_order_status",
        description="Check order status by order ID",
        func=check_order_status
    )
]

# Setup agent
llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful customer support agent. Use available tools to answer questions."),
    MessagesPlaceholder(variable_name="chat_history"),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

agent = create_openai_functions_agent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True)

# Run agent
response = agent_executor.invoke({"input": "What's your return policy?"})
print(response['output'])

Pros

1. Most comprehensive ecosystem

2,000+ integrations (every vector DB, every LLM, every tool imaginable).

Need to integrate with obscure API? LangChain probably has it.

2. LangGraph for complex orchestration

New framework (LangGraph) enables graph-based agent workflows. Best-in-class for multi-agent systems.

Example (coordinator agent delegates to specialist agents):

from langgraph.graph import StateGraph, END

workflow = StateGraph()

workflow.add_node("coordinator", coordinator_agent)
workflow.add_node("research_agent", research_agent)
workflow.add_node("analysis_agent", analysis_agent)

workflow.add_edge("coordinator", "research_agent")
workflow.add_edge("research_agent", "analysis_agent")
workflow.add_edge("analysis_agent", END)

app = workflow.compile()

3. Production observability (LangSmith)

LangSmith (paid tool, £0-400/month) provides deep tracing, debugging, evaluation.

Best observability of any framework we tested.

4. Active development

Weekly releases, responsive maintainers, massive community (Discord, GitHub discussions).

Cons

1. Steep learning curve

Concepts are abstract (chains, runnables, LCEL syntax). Takes 5-10 hours to feel productive.

Quote from James Park, ML Engineer: "LangChain has a learning curve that feels more like a learning cliff. Took me 2 full days to build what should've been a 2-hour project."

2. Abstraction overload

Everything is abstracted. Sometimes you want to just call an LLM -LangChain makes you build a Chain with a Prompt Template and an Output Parser.

Example (simple LLM call in LangChain vs raw):

LangChain way (verbose):

from langchain.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser

prompt = ChatPromptTemplate.from_template("Answer: {question}")
llm = ChatOpenAI(model="gpt-4-turbo")
chain = prompt | llm | StrOutputParser()
response = chain.invoke({"question": "What is RAG?"})

Raw OpenAI (simpler):

from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Answer: What is RAG?"}]
)

For simple use cases, LangChain feels like overkill.

3. Frequent breaking changes

LangChain evolves fast. We've upgraded 5 times in 6 months, each time requiring code changes.

4. Bloated dependencies

Installing LangChain pulls in 50+ dependencies. Slow install, large Docker images.

Performance

Tested agent on 100 customer support queries:

Latency breakdown:

• Retrieval: 180ms
• LLM call: 1,900ms
• LangChain overhead: 260ms (chains, memory, parsing)

LangChain overhead is highest of the 3 frameworks (260ms vs 120ms LlamaIndex, 180ms Haystack).

Rating: 4.1/5

Use LangChain if:

• Building complex multi-agent systems (LangGraph is unmatched)
• Need extensive integrations (2,000+ connectors)
• Want production observability (LangSmith)
• Team has time to learn (not a weekend hackathon)

Skip LangChain if:

• Simple RAG use case (LlamaIndex easier)
• Want lightweight (too many dependencies)
• Need stability (frequent breaking changes)

LlamaIndex

Verdict: Simplest for retrieval-focused agents. Limited beyond RAG.

Overview

LlamaIndex (formerly GPT Index) is laser-focused on one thing: connecting LLMs to data. If your agent is 80% retrieval, 20% other stuff, LlamaIndex is cleanest.

Core concepts:

• Index: Data structure for storing and querying documents
• Query Engine: High-level interface for asking questions
• Agents: Tool-using agents (simpler than LangChain's)
• Retrievers: Customizable retrieval strategies

Version tested: LlamaIndex 0.9.18

Setup & Development Experience

Installation:

pip install llama-index llama-index-vector-stores-pinecone

Time to first working agent: 45 minutes (fastest of the three).

Code complexity: 95 lines for full agent (retrieval + tools + memory).

Example code:

from llama_index import VectorStoreIndex, ServiceContext
from llama_index.vector_stores import PineconeVectorStore
from llama_index.tools import QueryEngineTool, ToolMetadata, FunctionTool
from llama_index.agent import OpenAIAgent
from llama_index.memory import ChatMemoryBuffer

# Setup retrieval
vector_store = PineconeVectorStore(index_name="support-docs")
index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = index.as_query_engine(similarity_top_k=5)

# Define tools
search_tool = QueryEngineTool(
    query_engine=query_engine,
    metadata=ToolMetadata(
        name="search_knowledge_base",
        description="Search support documentation"
    )
)

def check_order_status(order_id: str) -> str:
    return f"Order {order_id} status: Shipped"

order_tool = FunctionTool.from_defaults(
    fn=check_order_status,
    name="check_order_status",
    description="Check order status by order ID"
)

# Setup agent
memory = ChatMemoryBuffer.from_defaults(token_limit=3000)

agent = OpenAIAgent.from_tools(
    tools=[search_tool, order_tool],
    memory=memory,
    verbose=True,
    system_prompt="You are a helpful customer support agent."
)

# Run agent
response = agent.chat("What's your return policy?")
print(response)

Pros

1. Fastest time to working agent

45 minutes from zero to production-ready agent. Clean, intuitive APIs.

Best developer experience for retrieval-focused use cases.

2. Built-in RAG optimizations

LlamaIndex ships with advanced retrieval patterns (hybrid search, reranking, query transformations) out of the box.

Example (hybrid search + reranking in 5 lines):

from llama_index.retrievers import BM25Retriever
from llama_index.postprocessor import SentenceTransformerRerank

retriever = BM25Retriever.from_defaults(index=index, similarity_top_k=10)
reranker = SentenceTransformerRerank(top_n=5)

query_engine = index.as_query_engine(
    retriever=retriever,
    node_postprocessors=[reranker]
)

LangChain requires custom code for this.

3. Lightweight

Minimal dependencies (20 vs LangChain's 50+). Faster installs, smaller Docker images.

4. Excellent documentation

Clear, example-rich docs. Easiest to learn.

Cons

1. Limited beyond retrieval

LlamaIndex excels at RAG. Multi-agent orchestration, complex tool workflows, advanced memory -clunky compared to LangChain.

2. Smaller ecosystem

~200 integrations vs LangChain's 2,000+. Common tools covered, but niche integrations missing.

3. Less production tooling

No equivalent to LangSmith. Observability requires custom instrumentation.

4. Agent capabilities lag LangChain

LlamaIndex agents work for simple tool calling. For complex multi-agent systems, LangChain (LangGraph) is superior.

Performance

Why faster than LangChain? Less abstraction overhead (120ms vs 260ms).

Why better retrieval accuracy? Built-in hybrid search + optimized defaults.

Rating: 4.3/5

Use LlamaIndex if:

• Agent is primarily retrieval-focused (>70% RAG)
• Want fastest development time
• Simple tool usage (1-3 tools)
• Prefer lightweight, minimal dependencies

Skip LlamaIndex if:

• Building complex multi-agent systems (use LangChain)
• Need extensive integrations (smaller ecosystem)
• Require production observability (no LangSmith equivalent)

Haystack

Verdict: Enterprise NLP pipelines. Overbuilt for simple agents.

Overview

Haystack (by deepset.ai) started as an NLP framework for search and QA. Added agent capabilities later.

Strongest for: Production pipelines (document preprocessing, ETL, complex retrieval flows).

Weakest for: Rapid prototyping, simple agents.

Core concepts:

• Pipelines: DAG-based workflows (nodes connected by edges)
• Nodes: Processing units (retrievers, generators, preprocessors)
• Agents: Tool-using agents (newest feature, less mature)
• Document Stores: Abstraction over vector DBs, SQL, Elasticsearch

Version tested: Haystack 1.22.0

Setup & Development Experience

Installation:

pip install farm-haystack[pinecone]

Time to first working agent: 3 hours (longest learning curve).

Code complexity: 210 lines for full agent (most verbose).

Example code:

from haystack import Pipeline
from haystack.nodes import PromptNode, PromptTemplate, AnswerParser
from haystack.agents import Agent, Tool
from haystack.document_stores import PineconeDocumentStore
from haystack.nodes.retriever import EmbeddingRetriever

# Setup retrieval
document_store = PineconeDocumentStore(
    api_key="...",
    index="support-docs"
)

retriever = EmbeddingRetriever(
    document_store=document_store,
    embedding_model="text-embedding-3-small"
)

# Define tools
search_tool = Tool(
    name="search_knowledge_base",
    pipeline_or_node=retriever,
    description="Search support documentation"
)

def check_order_status(order_id: str) -> str:
    return f"Order {order_id} status: Shipped"

order_tool = Tool(
    name="check_order_status",
    pipeline_or_node=check_order_status,
    description="Check order status"
)

# Setup agent
prompt_node = PromptNode(
    model_name_or_path="gpt-4-turbo",
    api_key="...",
    max_length=1000
)

agent = Agent(
    prompt_node=prompt_node,
    tools=[search_tool, order_tool]
)

# Run agent
response = agent.run("What's your return policy?")
print(response['answers'][0].answer)

Pros

1. Best for complex pipelines

If you need multi-stage document processing (OCR → chunking → embedding → retrieval → summarization), Haystack's pipeline abstraction is cleanest.

Example (complex ETL + retrieval pipeline):

pipeline = Pipeline()
pipeline.add_node(component=pdf_converter, name="PDFConverter", inputs=["File"])
pipeline.add_node(component=preprocessor, name="Preprocessor", inputs=["PDFConverter"])
pipeline.add_node(component=embedder, name="Embedder", inputs=["Preprocessor"])
pipeline.add_node(component=document_store, name="DocumentStore", inputs=["Embedder"])

2. Enterprise features

• Support for SQL databases (not just vector DBs)
• Elasticsearch integration (full-text search + vector search)
• Built-in evaluation frameworks

Best for regulated industries (finance, healthcare) needing audit trails, structured data handling.

3. Type safety

Haystack uses Pydantic models extensively. Catches errors at development time (vs runtime errors in LangChain/LlamaIndex).

Cons

1. Overengineered for simple use cases

Building a basic RAG agent requires 200+ lines of boilerplate (pipelines, nodes, document stores).

LlamaIndex does same thing in 50 lines.

2. Agent support immature

Agents added recently (2023). Less polished than LangChain/LlamaIndex. Limited examples, rougher APIs.

3. Smaller community

~5K GitHub stars vs LangChain's 80K. Fewer tutorials, slower Stack Overflow responses.

4. Documentation gaps

Core framework well-documented. Agent-specific docs sparse (many features undocumented or example-only).

Performance

Why slowest? Pipeline overhead (nodes, type validation).

Why lowest tool success? Agent implementation less mature (occasionally fails to parse tool calls correctly).

Rating: 3.9/5

Use Haystack if:

• Building complex document processing pipelines
• Enterprise requirements (SQL support, Elasticsearch, audit trails)
• Already using Haystack for other NLP tasks
• Need type safety (Pydantic models)

Skip Haystack if:

• Building simple agent (overengineered)
• Want mature agent capabilities (agents are newer, less polished)
• Need fast development (steepest learning curve)

Feature Comparison Matrix

Performance Benchmarks

Identical agent (retrieval + 2 tools + memory), 100 queries:

LlamaIndex wins on speed and retrieval accuracy. LangChain competitive on tool calling (more mature agent implementation).

When to Use Each Framework

Choose LangChain if:

✅ Complex multi-agent workflows (coordinator → specialist agents) ✅ Need 50+ integrations (CRMs, databases, APIs) ✅ Production observability critical (LangSmith required) ✅ Team has time to learn (not weekend project)

Example use cases: Multi-step research agents, sales pipeline automation, complex approval workflows.

Choose LlamaIndex if:

✅ Agent is primarily retrieval-focused (>70% RAG) ✅ Want fastest development (ship in <2 hours) ✅ Simple tool usage (1-5 tools) ✅ Lightweight deployment (Docker, serverless)

Example use cases: Customer support bots, internal knowledge search, document QA.

Choose Haystack if:

✅ Building ETL + retrieval pipelines (complex document processing) ✅ Enterprise requirements (SQL, Elasticsearch, audit trails) ✅ Already using Haystack for NLP ✅ Type safety critical (Pydantic models)

Example use cases: Enterprise search, regulatory compliance pipelines, document processing workflows.

Frequently Asked Questions

Can I switch frameworks later?

Yes, but expect 2-5 days of migration work. Core concepts translate (retrieval, tools, memory), but APIs differ significantly.

Least painful migration: LlamaIndex → LangChain (LangChain is superset, most LlamaIndex patterns have LangChain equivalents).

Most painful: Haystack → anything else (pipeline abstraction unique to Haystack).

What about OpenAI Agents SDK?

OpenAI released native Agents SDK (beta). Tightly integrated with OpenAI models, simplest option if you're committed to OpenAI.

Trade-off: Locked into OpenAI (can't use Claude, Gemini, open-source models). LangChain/LlamaIndex/Haystack are model-agnostic.

For OpenAI-only projects: Consider OpenAI SDK (simpler). For multi-model: Stick with LangChain/LlamaIndex.

Which has the best future prospects?

LangChain: Massive community, well-funded (raised $25M), most momentum. Safest bet long-term.

LlamaIndex: Growing fast (especially for RAG use cases), strong niche. Likely to remain best-in-class for retrieval.

Haystack: Solid for enterprise, but smaller community. May struggle to keep up with LangChain's pace.

Can I use multiple frameworks together?

Technically yes (use LlamaIndex for retrieval, LangChain for orchestration), but adds complexity.

Better approach: Pick one, use it for everything. Mixing frameworks doubles debugging surface area.

Bottom line: LlamaIndex for RAG-focused agents (fastest, simplest), LangChain for complex multi-agent systems (most powerful, steepest curve), Haystack for enterprise NLP pipelines (overbuilt for simple agents).

Start with LlamaIndex. Migrate to LangChain if you outgrow it. Use Haystack only if you have specific enterprise requirements.

Next steps: Read our Multi-Agent Systems Production Guide for orchestration patterns that work across all frameworks.