> ## Documentation Index > Fetch the complete documentation index at: https://labs.prompthon.io/llms.txt > Use this file to discover all available pages before exploring further. # Framework Comparison: API Directly, LlamaIndex, LangChain, LangGraph, and CrewAI

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## Summary AI frameworks package repeatable LLM application work: prompts, tool calls, retrieval, state, routing, retries, and observability. They exist because the first demo is often simple, but the second and third feature quickly create the same engineering problems again. A practical default for beginners is: **start with the smallest thing that solves the job, then adopt a framework when the project needs structure that is no longer easy to maintain manually.** ## Why It Matters A direct API call is enough when the task is a single prompt, a small script, or a prototype where you can see every step. A framework starts to make sense when the project needs: * **memory** across turns or sessions * **retrieval** from documents, databases, or private knowledge bases * **tool use** such as search, database queries, calendar actions, or code * **stateful control flow** with branches, loops, retries, or checkpoints * **collaboration patterns** where several roles or agents divide the work * **observability** so failures can be traced and improved A common over-engineering pattern is adopting a framework before the project needs one. That can turn a small learning project into dependency management, new vocabulary, and more difficult debugging. The right question is not "which framework is best?" It is: > Which part of my current project is becoming repetitive, fragile, or hard to > reason about? ## Mental Model Think of framework choice like choosing between writing a script, using an app starter kit, or using a workflow engine: | Option | Everyday analogy | What it means | | - | - | - | | **No framework** | Writing a checklist by hand | You call the LLM API directly and write the prompt, state handling, tool calls, and error handling yourself. This is best when the workflow is small and visible. | | **LlamaIndex** | A library catalog and reference desk | It helps ingest, index, search, and answer questions over documents. Use it when the main job is connecting an LLM to private data. | | **LangChain** | An application starter kit for LLM apps | It gives reusable pieces for models, prompts, tools, agents, streaming, and integrations. Use it when you are building a broader LLM application with tools or multiple steps. | | **LangGraph** | A workflow engine or state machine | It makes steps, branches, loops, checkpoints, and recovery explicit. Use it when control flow matters more than quick setup. | | **CrewAI** | A small project team with assigned roles | It models agents as role-based collaborators with tasks. Use it when the problem naturally fits a planner/researcher/writer/reviewer style of division. | ```mermaid theme={null} flowchart TD Start["What is the job?"] --> Q1{"One small prompt or script?"} Q1 -- Yes --> NoFW["No framework"] Q1 -- No --> Q2{"Mostly document search or QA?"} Q2 -- Yes --> LI["LlamaIndex"] Q2 -- No --> Q3{"Building a tool-using LLM app?"} Q3 -- Yes --> LC["LangChain"] Q3 -- No --> Q4{"Need branches, loops, checkpoints, or recovery?"} Q4 -- Yes --> LG["LangGraph"] Q4 -- No --> Q5{"Does the work map to named roles?"} Q5 -- Yes --> CA["CrewAI"] Q5 -- No --> NoFW ``` ## Comparison Table | Dimension | No framework | LlamaIndex | LangChain | LangGraph | CrewAI | | - | - | - | - | - | - | | **Purpose** | Direct LLM calls with manual control | Data ingestion, indexing, retrieval, and document QA | General LLM app and agent development | Stateful graph orchestration | Role-based multi-agent task execution | | **Complexity** | Low | Low to moderate | Moderate | Moderate to high | Moderate | | **Best use cases** | Learning, demos, one-off scripts, simple assistants | RAG, knowledge bases, document search, internal QA | Chatbots with tools, agent apps, reusable integrations | Long-running workflows, loops, checkpoints, human review, recovery | Research, writing, analysis, or operations tasks split across roles | | **What it saves you from writing** | No framework layer; you implement the pieces directly | Loaders, indexes, retrievers, query flows | Model/tool/prompt wiring, agent harnesses, integrations | State transitions, branching, persistence, resumability | Role/task setup and delegation patterns | | **When to avoid** | When state, retrieval, or tools are growing | When documents are not central | When a direct API call is enough | When the workflow is linear and short | When one agent or a plain workflow is enough | ## Scenario Walkthroughs ### Scenario 1: Simple chatbot * **No framework**: store the message list, call the model, and return the answer. This is usually enough for a prototype. * **LangChain**: becomes useful when the chatbot needs tool calls, structured outputs, streaming, tracing, or reusable prompt/model wiring. * **Verdict**: start without a framework. Move to LangChain when the chatbot becomes an application rather than a single chat loop. ### Scenario 2: Document QA over internal PDFs * **No framework**: parse files, chunk text, create embeddings, store vectors, retrieve relevant chunks, assemble context, and handle citations yourself. * **LlamaIndex**: provides a focused path for loading documents, building an index, and querying over private data. * **LangChain**: can also build retrieval flows, especially when retrieval is one part of a broader tool-using agent. * **Verdict**: choose LlamaIndex when document retrieval is the core product; choose LangChain when retrieval is one tool inside a larger agent app. ### Scenario 3: Multi-step research workflow * **LangGraph**: model the process as explicit nodes such as plan, search, synthesize, review, and revise. Use checkpoints when the workflow may need to pause, resume, or recover. * **CrewAI**: model the process as roles such as planner, researcher, writer, and editor, each with a task. * **Verdict**: choose CrewAI for a fast role-based prototype; choose LangGraph when execution control and state are the main risks. ## Useful Defaults * Use **no framework** for the first working version unless retrieval, tools, or state already dominate the problem. * Use **LlamaIndex** when the question is "how do I make this data searchable and answerable by an LLM?" * Use **LangChain** when the question is "how do I build an LLM application that connects models, prompts, tools, and integrations?" * Use **LangGraph** when the question is "how do I control and recover a multi-step process?" * Use **CrewAI** when the question is "how do I divide this work across named agent roles?" * If a task is deterministic, use a normal function or workflow. Add an agent only when it provides useful coordination, tool use, or decision-making. ## Citations * [LangChain documentation](https://docs.langchain.com/oss/python/langchain/overview) * [LlamaIndex documentation](https://developers.llamaindex.ai/python/framework/) * [LangGraph documentation](https://docs.langchain.com/oss/python/langgraph/overview) * [CrewAI documentation](https://docs.crewai.com/) * Current official readings are listed in `external_readings`. ## Reading Extensions * [Agent Frameworks](/ecosystem/agent-frameworks): intermediate-level comparison of conversation-first, graph-first, skill-first, and engineering-first frameworks. * [Agent Runtime Building Blocks](/patterns/agent-runtime-building-blocks): the runtime primitives that frameworks wrap. * [Ecosystem Overview](/ecosystem): the full ecosystem lane. ## Update Log * 2026-06-02: Revised into a beginner decision guide with current official links, clearer mental models, and less API-specific guidance. * 2026-05-04: Initial draft for beginner-oriented framework comparison. 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