LangChain, LlamaIndex, ZenML, Flowise, CrewAI - compared for teams building multi-step AI pipelines and agent workflows. Which framework is right depends entirely on what you're building.
An LLM call by itself is a single turn: input in, text out. Most real-world AI applications need more than that. They need to retrieve documents, call external APIs, pass results between multiple models, maintain memory across steps, and route decisions based on what the model returns. LLM orchestration is the layer that coordinates all of this.
Think of it as the workflow engine sitting above your LLM calls. A simple RAG pipeline is a form of orchestration: retrieve relevant chunks, inject them into a prompt, call the model, return the response. A multi-agent research system where one agent gathers sources, another validates them, and a third writes a summary is a more complex form of the same idea.
Frameworks exist because writing all this plumbing from scratch - tool routing, memory management, chain execution, error handling - is repetitive and error-prone. The question is which framework fits your use case.
| Framework | Best for | Multi-agent | Open source | Ease of use |
|---|---|---|---|---|
| LangChain Most popular | General pipelines, broad ecosystem | Yes | Yes | Medium |
| LlamaIndex | RAG-heavy / document workflows | Partial | Yes | Medium |
| CrewAI | Multi-agent team workflows | Yes (core focus) | Yes | Medium |
| Flowise | No-code visual LLM flows | Yes | Yes | Easy |
| ZenML | MLOps + LLM pipelines (production) | Partial | Yes | Medium-hard |
The most widely adopted LLM framework, with the largest community and ecosystem. LangChain provides abstractions for chains (sequences of LLM calls and tool uses), agents (LLMs that decide which tools to call), memory, and retrieval. LangGraph, its graph-based extension, handles complex stateful multi-agent workflows. The main criticism is that its abstractions can be opaque and hard to debug at scale - some teams use it for prototyping, then replace it with lighter custom code in production.
The go-to framework for document-heavy AI applications. Where LangChain is general-purpose, LlamaIndex is specialised: it has mature utilities for chunking strategies, embedding models, vector store integrations, and query engines. If your application is primarily about ingesting and querying large document collections - knowledge bases, codebases, legal documents - LlamaIndex handles this better than LangChain's generic retrieval abstractions. The two can be combined: LlamaIndex for retrieval, LangChain for the agent loop.
Built around the metaphor of a "crew" of specialised AI agents collaborating on a task. You define agents (each with a role, goal, and backstory), assign them tools, and define tasks that route between them. CrewAI handles the orchestration: which agent gets which task, how outputs are passed between agents, and how to handle failures. It's best when your use case genuinely requires multiple agents with distinct specialisations - a researcher, a fact-checker, and a writer, for example. Overkill for single-agent workflows.
A visual, drag-and-drop interface for building LLM pipelines. You connect nodes (LLM models, vector stores, tools, memory, retrieval) on a canvas rather than writing code. Ideal for teams where not everyone codes, or for rapid prototyping where you want to see the pipeline visually. Self-hostable on your own infrastructure. The visual constraint can become limiting for complex conditional logic - at that point, code-based frameworks are more expressive.
A MLOps framework that has added LLM pipeline support. Where other frameworks focus on the LLM interaction itself, ZenML focuses on the production infrastructure around it: pipeline versioning, experiment tracking, model registry, deployment, and monitoring. If your organisation already runs ML workloads and you need LLM pipelines to live inside that same operational context - reproducible, auditable, integrated with your existing MLOps tooling - ZenML is the natural fit. Steeper learning curve than the others.
For Claude-based agents, the Model Context Protocol provides an alternative - and often cleaner - approach to tool integration. Rather than using a framework's tool abstraction to connect your agent to external systems, MCP servers expose those systems through a standardised protocol that Claude understands natively.
This means your orchestration layer can be thinner: Claude handles tool selection and calling through MCP, and you focus on the workflow structure rather than the plumbing. LangChain and CrewAI both support MCP servers as tool sources, so they're complementary rather than competing with MCP-based approaches. See our MCP hub for available servers.
LLM orchestration is coordinating multiple AI components - models, tools, memory, retrieval systems - into a coherent multi-step workflow. Rather than calling an LLM once and returning the result, an orchestrated pipeline might retrieve context, call the LLM, use a tool, call the LLM again with the tool result, and format a final response. Frameworks like LangChain and CrewAI provide the plumbing to connect these pieces.
For simple one-shot prompts: raw API is fine. Once you need multi-step chains, tool use, memory, or multiple agents working together, a framework saves significant boilerplate. That said, LangChain in particular has a reputation for abstractions that can become hard to debug - some teams prefer thin wrappers or build their own lightweight chains rather than adopting a full framework.
LangChain is a general-purpose orchestration framework for building any kind of LLM application - chains, agents, tools, memory. LlamaIndex is specialised for RAG and document-heavy applications: it excels at ingesting, indexing, and querying large document collections. They can be used together - LlamaIndex handling retrieval, LangChain handling the broader agent loop.
CrewAI is purpose-built for multi-agent collaboration: you define a crew of agents, each with a role and set of tools, and a task structure that routes work between them. It's best when your workflow genuinely requires multiple agents with different specialisations - for example, a researcher agent, an analyst agent, and a writer agent working on a report together. For single-agent workflows, LangChain or a raw API call is simpler.