LangChain vs LlamaIndex vs CrewAI vs AutoGen: A Comparison That Includes Context Architecture

Quick facts

LangChain, LlamaIndex, CrewAI, and AutoGen are the four most-adopted AI agent frameworks in enterprise environments. LangChain leads on orchestration flexibility and MCP maturity, LlamaIndex on retrieval, CrewAI on role-based multi-agent coordination, and AutoGen on human-in-the-loop workflows. The right choice depends on where orchestration complexity sits and which framework integrates cleanest with your context architecture.

McKinsey’s State of AI 2025, based on 1,993 respondents across 105 countries, reports that 62% of organizations are experimenting with AI agents and 23% are scaling agentic systems in at least one function. The AI agent frameworks compared question is no longer theoretical for most engineering teams. It is the decision that shapes everything downstream: what you can observe, how you debug failures, how much you pay per run, and how locked in you are when the next framework version breaks your production pipeline.

Most comparisons evaluate the same dimensions and reach the same verdicts. LangChain is flexible and broad. LlamaIndex is strong for retrieval. CrewAI handles multi-agent coordination. AutoGen suits conversational workflows. Those verdicts are accurate and incomplete. The dimension that most consistently determines whether an agent works reliably in production does not appear in any of those feature tables.

This article covers the standard comparison and the dimension the standard comparison misses.

AI agent frameworks compared: at a glance

LangChain

LangChain is the most widely adopted AI agent framework. Its core abstraction, chains and graphs of LLM calls, tool invocations, and memory retrievals, covers a broad range of agent architectures without locking teams into a specific pattern.

LangGraph, LangChain’s graph-based orchestration layer, handles stateful multi-step workflows where nodes pass context between each other, branch conditionally, or loop back based on intermediate results, including agents that decompose business questions into parallel subtasks and synthesize a final answer. Maintained integrations exist for most major data platforms, vector stores, external APIs, observability tools, and authentication providers. The ecosystem is the largest of the four.

Where does LangChain fit?

Complex multi-step reasoning, heterogeneous tool integration requirements, and teams that expect to iterate on agent architecture over time. The flexibility that makes LangChain powerful also introduces configuration overhead; simpler, well-scoped use cases can typically be served with a leaner framework.

How does LangChain handle context layer integration?

LangChain is built around external retrieval. Its retriever abstraction plugs into external context sources, and MCP support allows agents to query a governed context layer via standardized interfaces. Of the four frameworks, LangChain requires the least custom engineering to connect an enterprise context layer and is the most reliable path to governed context integration in production.

LlamaIndex

LlamaIndex is purpose-built for retrieval and synthesis over large corpora. Its index abstractions, including vector stores, knowledge graphs, and structured data tables, are optimized for returning the most relevant context from a heterogeneous data source.

Where LangChain asks “how do I orchestrate a series of LLM calls,” LlamaIndex asks “how do I make the right information retrievable.” The two are complementary more often than alternatives. Many production architectures use LlamaIndex as the retrieval layer inside a LangChain-orchestrated agent. LlamaIndex’s query pipeline gives developers fine-grained control over hybrid search, metadata filtering, reranking, multi-document synthesis, and structured output parsing.

Where does LlamaIndex fit?

Agents whose primary task is answering questions over large document sets, internal code repositories, structured data tables, or enterprise knowledge bases. When the answer quality problem traces back to retrieval (wrong documents, suboptimal reranking, weak hybrid search across heterogeneous sources), LlamaIndex’s query pipeline exposes the specific levers to fix it.

How does LlamaIndex handle context layer integration?

Strong for retrieval-based context. MCP support in LlamaIndex currently covers retrieval connectors (querying vector stores, document indexes, and structured data sources via standardized interfaces) but does not yet extend to governed semantic layers. Canonical metric definitions, business glossaries, and organizational context memory require external infrastructure. Whether the agent knows what “ARR” means in your business depends entirely on whether that definition exists in a governed layer outside LlamaIndex.

CrewAI

CrewAI’s model is role-based. You define a crew of agents, each with a role and tool set, and assign them tasks to execute collaboratively. Agents work in sequence or in parallel, passing outputs between them under the coordination of a shared task definition. The role-based model maps naturally onto enterprise workflows where different functions own different parts of a process.

CrewAI supports shared memory within a crew that persists between task steps. MCP support is actively in development: connectors exist for select tools but the integration surface is narrower than LangChain’s and not yet stable enough to treat as a production dependency without a fallback. Unlike LangChain’s production-grade MCP integration, CrewAI’s is best treated as emerging infrastructure for now.

Where does CrewAI fit?

Workflows that map onto specialized agents handling distinct functions in sequence or in parallel, and where role boundaries can be defined clearly in advance.

How does CrewAI handle context layer integration?

Within a crew, CrewAI’s shared memory provides reasonable context continuity. The gap appears when multiple crews need to share governed definitions, such as when the procurement crew and the finance crew both reason about ‘cost center’ and those definitions need to be consistent across outputs. A shared external context layer handles this; it is outside what CrewAI currently provides natively. This is a specific case of the multi-agent memory silo problem that shows up whenever multiple agent systems share overlapping data.

AutoGen

AutoGen, developed by Microsoft Research, models multi-agent interaction as structured conversation. Agents communicate by exchanging messages, each capable of generating responses, executing code, calling tools, or requesting human input. The model suits workflows where human judgment needs to enter the loop: an agent can pause mid-task, present its reasoning, solicit a correction, and resume with the updated context.

Where does AutoGen fit?

Human-in-the-loop agents, research and exploration workflows, and scenarios where the agent needs to reason transparently and incorporate human corrections mid-task.

How does AutoGen handle context layer integration?

Flexible enough to integrate with external context sources, but retriever abstractions and MCP support are less mature than LangChain’s. Teams that need governed enterprise context should plan for more custom integration work than the other three frameworks require.

What dimension does every feature table leave out?

None of these frameworks knows what “ARR” means in your business, which table in your data warehouse holds the canonical revenue figure, or which metric version your governance rules treat as authoritative.

Handling enterprise organizational context is outside the scope of what these frameworks are designed to do. It is the engineer’s problem, and it is the one that most determines whether an agent produces correct answers reliably in production. A LangChain survey of more than 1,300 practitioners found unreliable performance to be the single biggest obstacle to scaling agentic AI, cited by 32% of teams (LangChain State of AI Agents Survey, 2025). Framework selection did not appear in the top four blockers.

Snowflake’s engineering team found that adding an organizational ontology to an agent receiving semantic views improved answer accuracy by 20% and reduced tool calls by roughly 39%, compared to a best-practices baseline without the ontology. The framework was unchanged. The context layer was what improved.

Adding an organizational ontology to an agent receiving semantic views improved answer accuracy by 20% and reduced tool calls by roughly 39% (Snowflake Engineering, March 2026). The framework was unchanged. The context layer was what improved.

The question that most affects production outcomes is: “What is my context architecture, and which framework integrates most cleanly with the context infrastructure I’m building?” Framework selection shapes the orchestration pattern. Context architecture determines whether the agent produces correct answers in production.

How to choose: four scenarios

The right framework depends on where the complexity in your use case actually sits.

The framework decision is the more reversible of the two. Context architecture is foundational, and getting it right before the framework is chosen is what separates teams that scale from teams that rebuild.

How does a framework-agnostic context layer connect?

Whichever framework you choose, the enterprise context layer connects externally via MCP, direct API, or a retriever abstraction rather than being embedded in the framework. The context layer needs to be governed, versioned, and shared across multiple agents and frameworks. Embedding it inside any single framework recreates the context silo problem at the infrastructure level.

LangChain offers the most mature MCP integration and the most straightforward path to connecting an external governed context layer. LlamaIndex integrates well for retrieval-based context. CrewAI and AutoGen require more custom work at the context integration layer but impose no architectural barriers to it.

How Atlan approaches framework-agnostic context

The challenge

Enterprises pick a framework and embed context inside its native memory abstractions. When the framework changes (new version, new team, new use case), the context has to be rebuilt from scratch. Meanwhile, each new framework the organization adopts recreates definitions, lineage, and governance rules its earlier deployments already had. The framework choice becomes a context lock-in, and the cost compounds with every new agent.

The approach

Context Engineering Studio bootstraps the enterprise context layer from existing data signals: SQL history, BI dashboards, lineage, and business glossaries. The Atlan MCP server exposes that governed context through standardized interfaces any framework can consume. Whether the agent runs on LangChain, LlamaIndex, CrewAI, AutoGen, or the next framework the team adopts, it draws from the same definitions, the same lineage, the same governance rules. Context agents sit on top of the layer to simulate, evaluate, and improve context quality over time.

The outcome

Framework decisions become reversible. A team that starts on LangChain can later add CrewAI for a multi-agent use case, or substitute LlamaIndex for a retrieval-heavy agent, without rebuilding the context underneath. The context layer persists across framework versions and agent generations, so the investment compounds even when the orchestration choice changes.

How enterprises decouple context from the framework

Workday

Workday’s analytics team found that their revenue analysis agent could not answer a single foundational question until they built a shared language between people and AI. The translation layer lives outside any single framework: it is embedded in the data catalog and exposed through MCP, so every agent Workday builds next draws from it.

Workday builds AI-ready semantic layers with Atlan's context infrastructure

"We built a revenue analysis agent and it couldn't answer one question. We started to realize we were missing this translation layer. All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan's MCP server."

Joe DosSantos, VP Enterprise Data & Analytics

Workday

DigiKey

DigiKey treats the context layer as operating infrastructure that sits above any specific framework. Metadata feeds discovery, AI governance, data quality, and an MCP server delivering context to AI models — the framework underneath can change without breaking the work done to build the context layer in the first place.

DigiKey activates metadata as framework-agnostic context infrastructure

"Atlan is much more than a catalog of catalogs. It's more of a context operating system… Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models."

Sridher Arumugham, Chief Data & Analytics Officer

DigiKey

Why the framework decision matters less than the context decision

The four frameworks covered here are all real engineering tools with real strengths and real tradeoffs. The decision between them matters. It shapes how the team thinks about orchestration, how the system is debugged, and how agents coordinate when they need to. None of that is trivial.

It is also not the decision that determines whether the agent produces correct answers in production. That decision sits one layer below: in the context architecture the agent queries before it reasons. Teams that treat the framework as the primary choice and the context layer as a configuration detail consistently find themselves rebuilding the layer when the framework changes, or worse, running multiple frameworks on top of parallel isolated context stores that disagree with each other.

The teams that scale treat the context layer as framework-agnostic infrastructure. The framework is chosen for the job in front of them; the context persists across whatever comes next.

FAQs about AI agent frameworks

When should you use LlamaIndex inside a LangChain agent rather than as a standalone framework?

Use LlamaIndex as a standalone framework when the primary task is answering questions over large document corpora and the workflow is linear enough that LangChain’s orchestration overhead adds no value. Use LlamaIndex inside a LangChain agent when the retrieval problem is complex (hybrid search, reranking, multi-index routing) but the agent also needs to decompose tasks, coordinate tool calls across multiple systems, or manage stateful workflows. The two are complementary more often than they are alternatives. Many production architectures treat LlamaIndex as the retrieval layer and LangChain as the orchestration layer sitting above it.

What is the difference between LangChain and CrewAI?

LangChain is a general-purpose orchestration framework for agents with complex reasoning chains and broad tool integration. CrewAI is a multi-agent framework organized around role-based crews, where each agent has a defined role, tool set, task scope, and handoff protocol. The two are complementary: some architectures use LangChain as the foundation with CrewAI’s role abstractions on top.

What is the difference between LlamaIndex and LangChain for agent development?

LlamaIndex is a retrieval and indexing framework optimized for making large data corpora queryable. LangChain is an orchestration framework for agents that take multi-step actions using tools. LlamaIndex handles “find the right information”; LangChain handles “decide what to do with it.” Many production architectures use both, with LlamaIndex as the retrieval layer inside a LangChain-orchestrated agent.

How do you migrate from one framework to another without rebuilding your context layer?

The key is keeping the context layer external to the framework from the start. When the enterprise context layer is embedded inside a specific framework’s memory abstractions, migrating frameworks means rebuilding context from scratch. When it connects externally via MCP or API, the context layer persists across framework versions and migrations. The agent’s orchestration logic changes; the context it runs on does not.

What does partial MCP support mean in practice for LlamaIndex and AutoGen?

For LlamaIndex, partial MCP support means connectors exist for retrieval operations: querying vector stores, document indexes, and structured data sources via standardized interfaces. What is not yet covered is governed semantic layers. For AutoGen, partial support means MCP integration exists but the retriever abstractions are less mature than LangChain’s and require more custom engineering to connect to an external governed context layer reliably.

Is the framework choice a lock-in decision?

Only if the context layer is embedded inside it. Framework-native memory stores and retrievers create lock-in by holding organizational knowledge that has to be rebuilt when the framework changes. A context layer that connects through MCP or API persists across framework migrations. The framework choice becomes a reversible engineering decision rather than a strategic one.

Sources

1. LangChain Business Breakdown and Founding Story, Contrary Research (February 2025)
2. 50+ Key AI Agent Statistics and Adoption Trends, index.dev (2025)
3. The 5 Best RAG Evaluation Tools in 2025, Braintrust (2025)
4. State of AI Agents Survey, LangChain (2025)
5. Agent Context Layer for Trustworthy Data Agents, Snowflake Engineering (March 2026)