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The right framework, built correctly

LangChain vs LlamaIndex development services: the right RAG framework

Choosing between LangChain and LlamaIndex determines how much you spend, how fast it ships, and how well it performs in production. I build with both frameworks and pick the right one, or the right combination, based on your actual retrieval and orchestration needs, not a default toolkit.

Experience5+ yrs production RAG
CoverageBoth frameworks, used correctly
Turnaround48h architecture rec.
Get a framework recommendation View case studies

Written recommendation with reasoning, before any build work begins

framework fit, illustrative live
~14msFramework overhead
BaselineCode volume
500+Integrations
FIT CHECK

Both frameworks, done rightLangChain, LangGraph, and LlamaIndex, used for what each is actually built for.
Recommendation in writingA specific framework choice, with the reasoning behind it, before any build begins.
Hybrid when it earns itLlamaIndex retrieval wrapped as a tool inside a LangGraph agent, the standard 2026 pattern.
Honest, not defaultIf your project doesn't need the complexity of one framework, I'll say so.

Plain answer

What is LangChain? What is LlamaIndex?

Quick answer

LangChain is a broad LLM application framework built around composable chains, agents, tools, and memory, with LangGraph as its production agent orchestration layer. LlamaIndex is a focused data framework for LLMs, purpose-built for ingesting, indexing, and querying document corpora via retrieval-augmented generation. Both are open-source, MIT-licensed, and by 2026 both frameworks handle RAG and agents to some degree, the real choice is about where your project's hardest problem actually lives.

The comparison

Framework comparison: LangChain vs LlamaIndex

Factor LangChain (via LangGraph) LlamaIndex
Primary strength Multi-step agent orchestration, state, tools, memory Retrieval quality: chunking, indexing, query synthesis
Best for Agentic systems where RAG is one component Retrieval-first products where document Q&A is the core
Code volume for equivalent RAG Baseline 30 to 40% less code
Framework overhead ~14ms (LangGraph) ~6ms
Ecosystem 500+ integrations, most mature agent tooling 300+ connectors, native document parsing via LlamaParse
Observability LangSmith (first-party, paid tiers) Typically requires third-party tracing

The 2026 reality

Most production systems use both together: LlamaIndex handling ingestion, indexing, and retrieval, with LangGraph orchestrating the broader multi-step agent logic around it. This is the standard architecture I build when a project genuinely needs both retrieval quality and agentic behavior.

What I build

RAG framework development services

What I build with each framework, matched to what your project actually needs.

CONSULT

Framework selection consulting

An independent architecture assessment of your use case, data, and requirements, resulting in a written recommendation: LangChain, LlamaIndex, or both, with the reasoning behind it.

BUILD

LlamaIndex RAG development

Retrieval-first builds: document ingestion, hierarchical chunking, auto-merging retrieval, and query synthesis for document-heavy applications where answer accuracy from your corpus is the product.

BUILD

LangChain & LangGraph agent development

Multi-step agent systems: tool use, conversational memory, state management, and human-in-the-loop checkpoints for AI that takes actions, not just answers questions.

BUILD

Hybrid architecture (both frameworks)

LlamaIndex as the retrieval layer wrapped as a tool inside a LangGraph agent, combining best-in-class retrieval with production-grade orchestration in one system.

AUDIT

Migration & framework audits

Assessment and migration support for teams that chose the wrong framework early and are hitting friction, or need to add agentic capability to an existing retrieval-only system.

Not sure which you need?

Try the fit-check widget above, or get it scoped properly.

Get a recommendation

The honest breakdown

Where each framework wins

LLAMAINDEX WINS WHEN

Retrieval quality is the bottleneck

  • Retrieval quality is the primary bottleneck, not orchestration complexity
  • You need complex multi-document reasoning: tree index and response synthesizers produce roughly 10 to 15% better answers on multi-source synthesis benchmarks
  • Your documents are hierarchically structured (legal, code, technical manuals): composable indices produce roughly 15 to 25% better answers
  • You want to ship faster with less code and lower latency overhead
LANGCHAIN WINS WHEN

RAG is one piece of a larger agent

  • RAG is one component of a larger agentic system, not the whole product
  • You need tool use, multi-step reasoning, or persistent memory across a conversation
  • Human-in-the-loop checkpoints matter: LangGraph has native checkpointing and interrupt/resume support
  • First-party observability (LangSmith) is a requirement, not an afterthought

How it gets built

Engagement process

PHASE 01days 1 to 2

Discovery

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Understanding your data, use case, and whether the core challenge is retrieval quality, agentic behavior, or both.
PHASE 02within 48h

Framework recommendation

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A written architecture spec with a specific framework recommendation and reasoning, delivered within 48 hours.
PHASE 03the decisive one

Build

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Development in milestones, retrieval layer first if applicable, then orchestration and guardrails.
PHASE 04before launch

Evaluation

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Testing against your real queries and use cases using RAGAS or a custom eval set.
PHASE 05handoff

Deployment

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Production deployment with monitoring, tracing, and documentation for your team.

Investment

Engagement Options

Engagement type What's included
Framework selection consulting Free Written architecture recommendation and spec
LlamaIndex RAG build Retrieval pipeline, ingestion, evaluation, deployment
LangChain/LangGraph agent build Multi-step agent, tools, memory, guardrails, deployment
Hybrid architecture build Both frameworks combined, full production system

FAQ

Frequently asked questions

Is LlamaIndex better than LangChain for RAG?
For pure retrieval-augmented generation over document corpora, LlamaIndex is generally the stronger choice: its default chunking, metadata filtering, and reranking are more production-calibrated out of the box. For RAG as part of a broader agent system, LangChain remains the stronger foundation.
Can you build with both frameworks in one project?
Yes, this is the most common production pattern I build in 2026: LlamaIndex handling retrieval, wrapped as a tool inside a LangGraph agent for orchestration. This combines the strengths of both rather than forcing one framework to do both jobs.
How do you decide which framework to recommend?
Based on where your hardest problem actually lives: if it is retrieval quality over a messy document corpus, LlamaIndex. If it is multi-step agent behavior, tool use, or memory, LangChain and LangGraph. The framework recommendation is delivered in writing with the reasoning, as part of the discovery process, before any build work begins.
Do I need a LangChain agency or can a freelance consultant handle this?
Most RAG and agent projects, a specific pipeline, agent, or integration, are well within scope for a single experienced developer working directly with you. An agency's advantage shows up on large, multi-disciplinary product builds needing several specialists in parallel, not on the RAG/LangChain engineering itself.
How much does RAG framework development cost?
A framework selection consultation runs $500 to $1,500. A full LlamaIndex or LangChain build ranges from $8,000 to $30,000+ depending on complexity. A hybrid architecture combining both frameworks typically runs $15,000 to $40,000+. Contact for a fixed-price estimate specific to your use case.
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