LLM Orchestration Frameworks: LangChain vs LlamaIndex vs Custom

Why Orchestration Matters

Building a production LLM application involves far more than calling a model API. You need to manage prompt templates, chain multiple LLM calls, integrate retrieval systems, handle tool calling, manage conversation memory, and implement error handling. Orchestration frameworks aim to standardize these patterns.

As of early 2026, three approaches dominate the landscape: LangChain (the full-featured framework), LlamaIndex (the data-focused framework), and custom orchestration (building your own thin layer). Each has clear strengths and weaknesses.

LangChain: The Swiss Army Knife

LangChain has evolved significantly since its 2022 launch. The 0.3.x release in late 2025 brought a cleaner architecture with LangChain Core, LangChain Community, and LangGraph as separate packages.

Architecture

LangChain is built around three core abstractions:

• Runnables: Composable units of work with a standard interface (.invoke(), .stream(), .batch())
• Chains: Sequences of runnables piped together using LCEL (LangChain Expression Language)
• Agents: LLM-driven decision makers that choose which tools to call

from langchain_core.prompts import ChatPromptTemplate
from langchain_anthropic import ChatAnthropic
from langchain_core.output_parsers import StrOutputParser

# LCEL chain composition
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a technical writer. Write concisely."),
    ("user", "Explain {topic} in {word_count} words.")
])
model = ChatAnthropic(model="claude-sonnet-4-20250514")
parser = StrOutputParser()

chain = prompt | model | parser

result = await chain.ainvoke({
    "topic": "vector databases",
    "word_count": "200"
})

LangGraph for Stateful Agents

LangGraph, released as a separate package, has become LangChain's answer for building stateful, multi-step agents. It models agent workflows as directed graphs where nodes are computation steps and edges are conditional transitions.

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    next_action: str

def research_node(state: AgentState) -> AgentState:
    # Perform research using tools
    result = research_tool.invoke(state["messages"][-1])
    return {"messages": [result], "next_action": "analyze"}

def analyze_node(state: AgentState) -> AgentState:
    # Analyze research results
    analysis = llm.invoke(state["messages"])
    return {"messages": [analysis], "next_action": "complete"}

graph = StateGraph(AgentState)
graph.add_node("research", research_node)
graph.add_node("analyze", analyze_node)
graph.add_edge("research", "analyze")
graph.add_edge("analyze", END)
graph.set_entry_point("research")

agent = graph.compile()

Strengths

• Massive ecosystem with 700+ integrations (vector stores, LLM providers, tools)
• LangSmith provides excellent tracing and debugging
• LangGraph handles complex stateful workflows well
• Active community and extensive documentation

Weaknesses

• Abstraction overhead adds latency (10-30ms per chain step in benchmarks)
• Rapid API churn -- code written six months ago often needs updates
• Over-abstraction makes debugging difficult when things go wrong
• LCEL syntax has a steep learning curve for complex chains

LlamaIndex: The Data Framework

LlamaIndex focuses specifically on connecting LLMs to data. While LangChain tries to be a general-purpose framework, LlamaIndex excels at building RAG pipelines, data agents, and structured data querying.

Architecture

LlamaIndex is organized around:

• Data Connectors: Loaders for 160+ data sources (PDFs, databases, APIs, Notion, Slack)
• Indexes: Structures for organizing data (vector, keyword, knowledge graph, tree)
• Query Engines: Components that combine retrieval and LLM generation
• Agents: Tool-using agents optimized for data-heavy workflows

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.anthropic import Anthropic

# Build a RAG pipeline in 5 lines
documents = SimpleDirectoryReader("./docs").load_data()
splitter = SentenceSplitter(chunk_size=512, chunk_overlap=50)
nodes = splitter.get_nodes_from_documents(documents)
index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine(
    llm=Anthropic(model="claude-sonnet-4-20250514"),
    similarity_top_k=5
)

response = query_engine.query("What is our refund policy?")

Advanced RAG with LlamaIndex

LlamaIndex provides built-in support for advanced RAG techniques that would require significant custom code in other frameworks:

from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool, ToolMetadata

# Multi-document query decomposition
tools = [
    QueryEngineTool(
        query_engine=financial_index.as_query_engine(),
        metadata=ToolMetadata(name="financials", description="Financial reports")
    ),
    QueryEngineTool(
        query_engine=product_index.as_query_engine(),
        metadata=ToolMetadata(name="products", description="Product documentation")
    ),
]

engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=tools)
response = engine.query(
    "How did product launches impact Q3 2025 revenue?"
)

Strengths

• Best-in-class RAG pipeline tooling
• Clean abstractions for data loading, indexing, and querying
• Built-in evaluation framework for RAG quality
• Excellent for structured data querying (text-to-SQL, pandas integration)

Weaknesses

• Narrower scope -- not ideal for general agent workflows
• Smaller ecosystem for non-data-related integrations
• Agent capabilities lag behind LangChain/LangGraph
• Documentation can be sparse for advanced features

Custom Orchestration: Build Your Own

Many production teams in 2026 have moved to custom orchestration, especially after experiencing the pain of framework version churn. The approach: use the LLM provider's SDK directly, add thin wrappers for common patterns, and avoid external framework dependencies.

import anthropic
from dataclasses import dataclass

@dataclass
class Tool:
    name: str
    description: str
    input_schema: dict
    handler: callable

class AgentOrchestrator:
    def __init__(self, model: str = "claude-sonnet-4-20250514"):
        self.client = anthropic.AsyncAnthropic()
        self.model = model
        self.tools: list[Tool] = []

    def register_tool(self, tool: Tool):
        self.tools.append(tool)

    async def run(self, system: str, user_message: str, max_turns: int = 10):
        messages = [{"role": "user", "content": user_message}]
        tool_defs = [
            {"name": t.name, "description": t.description,
             "input_schema": t.input_schema}
            for t in self.tools
        ]

        for _ in range(max_turns):
            response = await self.client.messages.create(
                model=self.model,
                system=system,
                messages=messages,
                tools=tool_defs,
                max_tokens=4096,
            )

            # If no tool use, we are done
            if response.stop_reason == "end_turn":
                return self._extract_text(response)

            # Process tool calls
            messages.append({"role": "assistant", "content": response.content})
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    handler = self._get_handler(block.name)
                    result = await handler(block.input)
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": str(result),
                    })
            messages.append({"role": "user", "content": tool_results})

        raise MaxTurnsExceeded("Agent exceeded maximum turns")

Strengths

• Zero framework dependency risk -- you control the code
• Minimal abstraction overhead (fastest execution)
• Complete visibility into every step
• Easy to debug and customize

Weaknesses

• You rebuild common patterns from scratch
• No built-in tracing or evaluation tools
• Higher initial development time
• Missing ecosystem integrations

Decision Framework

Factor	LangChain	LlamaIndex	Custom
RAG pipelines	Good	Excellent	Manual
General agents	Excellent (LangGraph)	Adequate	Full control
Prototyping speed	Fast	Fast (for RAG)	Slower
Production stability	Improving	Good	Best
Debugging ease	Moderate (LangSmith helps)	Moderate	Excellent
Framework lock-in	High	Moderate	None
Team onboarding	Steep learning curve	Moderate	Depends on docs

Choose LangChain when:

• You need rapid prototyping with many integrations
• Your team is building complex multi-agent systems with LangGraph
• You want built-in tracing via LangSmith

Choose LlamaIndex when:

• Your primary use case is RAG or data-heavy querying
• You need to connect LLMs to diverse data sources
• You want built-in RAG evaluation tooling

Choose Custom when:

• You have strong engineering capacity
• Production stability and debuggability are priorities
• Your use case is well-defined and does not need dozens of integrations
• You want to avoid framework version churn

The Hybrid Approach

Many teams in 2026 use a hybrid: LlamaIndex for the RAG pipeline, custom orchestration for the agent loop, and LangSmith (standalone) for tracing. This picks the best tool for each concern without committing fully to any single framework.

The key insight is that orchestration frameworks are means, not ends. The best teams evaluate them based on how much they accelerate their specific use case, not on feature count.