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LlamaIndex

Connects LLM agents to your data with connectors, indexes, query engines and event-driven workflows

Install with pip

pip install llama-index

Copied from the project's docs at developers.llamaindex.ai/python/framework/getting_started/installation

Why you'd want it

The LlamaIndex framework is a Python toolkit for context augmentation: data connectors ingest APIs, PDFs, docs and SQL, indexes structure the data, and query and chat engines retrieve context for the model. Agents can call a RAG pipeline as one tool among many, and Workflows add event-driven, multi-step processes with branching, retries and human-in-the-loop review. You can start with the llama-index starter package or install llama-index-core plus only the integrations you need from over 300 packages.

Best for

  • RAG over private documents
  • Agents that query your own data
  • Pairing with LlamaParse for hard documents

Use it when

Your agent's main job is answering questions or taking actions over your own documents and data sources.

Skip it if

You want a framework that is its maintainer's main focus: the README says the company's primary focus has shifted to LlamaParse, while the OSS framework stays available.

Editor's note

The README says LlamaIndex has shifted its primary focus to the LlamaParse platform; the open-source framework remains available.

Related tools

Same category or shared tags, by drop score.

  1. Rank 9

    LangChain

    Framework

    Provides one interface and a large integration library for models, embeddings, vector stores and tools

    • score 81
    • 148k stars
    • pushed
    • langchain-core==1.6.10 on
    • MIT

    Install with pip

    pip install -U langchain
    Use it when, skip it if, stats for LangChain

    Why you'd want it

    LangChain is a framework for building agents and LLM apps from interoperable components, with a large library of integrations for model providers, tools, vector stores and retrievers. Its standard model interface lets you swap models as you experiment without rebuilding the app. An equivalent JS/TS library, LangChain.js, is available, and it pairs with LangGraph for lower-level orchestration and LangSmith for evals and tracing.

    Best for

    • Apps that need many model or data integrations
    • Switching between model providers
    • Prototyping LLM apps in Python or JS

    Use it when

    You want ready-made integrations for models, vector stores and retrievers, and a single interface for switching providers.

    Skip it if

    You need fine-grained control over agent flow and state; the README points to LangGraph for advanced customization and agent orchestration.

    Stats

    Stars
    147,836
    Last push
    Release
    langchain-core==1.6.10,
    License
    MIT
    Pricing
    Open source
    Drop score
    81 of 100

    Full stat block for LangChain

  2. Rank 15

    Mastra

    Framework

    Builds TypeScript agents and graph-based workflows with memory, RAG, evals and model routing

    • score 69
    • 28.8k stars
    • pushed
    • @mastra/core@1.77.0 on
    • Apache-2.0

    Create a project with npm

    npm create mastra@latest
    Use it when, skip it if, stats for Mastra

    Why you'd want it

    Mastra is a TypeScript framework for agents and workflows that plugs into React, Next.js and Node apps or runs as a standalone server. It connects to 40+ model providers through one interface, has a graph-based workflow engine with .then(), .branch() and .parallel(), and can suspend an agent or workflow for human input and resume from stored state. It also covers conversation history, RAG, observational memory, MCP server authoring, evals and observability.

    Best for

    • TypeScript and Next.js teams
    • Agents with human approval steps
    • Apps that need memory and RAG in one stack

    Use it when

    You want agents, workflows, memory and evals in one TypeScript framework instead of assembling separate libraries.

    Skip it if

    You work in Python. Features in ee/ directories (for example auth) need a Mastra enterprise license for production use.

    Stats

    Stars
    28,824
    Last push
    Release
    @mastra/core@1.77.0,
    License
    Apache-2.0
    Pricing
    Open source
    Drop score
    69 of 100

    Full stat block for Mastra

  3. Rank 25

    CrewAI

    Framework

    Orchestrates teams of role-based agents (Crews) and event-driven workflows (Flows) in Python

    • score 63
    • 59.7k stars
    • pushed
    • 1.15.28 on
    • MIT

    Install the CLI with uv

    uv tool install crewai
    Use it when, skip it if, stats for CrewAI

    Why you'd want it

    CrewAI is a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration. Crews let role-based agents collaborate and delegate tasks autonomously, while Flows give event-driven control with state management and conditional branching, and the two can be combined. The crewai CLI scaffolds a project where agents and tasks are defined in JSONC files and run with crewai run.

    Best for

    • Multi-agent teams with defined roles
    • Business process automation
    • Mixing autonomous agents with deterministic steps

    Use it when

    You want several specialized agents with clear roles and tasks, wrapped in deterministic, event-driven workflows in regular Python.

    Skip it if

    You are not on Python (it requires Python 3.10 to 3.13). It collects anonymous telemetry by default (disable with OTEL_SDK_DISABLED=true), and the managed control plane, CrewAI AMP, is a separate commercial product.

    Stats

    Stars
    59,696
    Last push
    Release
    1.15.28,
    License
    MIT
    Pricing
    Open source
    Drop score
    63 of 100

    Full stat block for CrewAI

For agents

The same entry, as files an agent can read without scraping this page. 

# read one entry
curl -s https://agenttooldrop.com/tools/llamaindex.md

# list the MCP servers in the catalog
curl -s https://agenttooldrop.com/tools.json \
  | jq -r '.tools[] | select(.category=="mcp-server") | .slug'