LibreAI / AI Agents / Llama Index
// agents · run-llama/llama_index

Llama Index

LlamaIndex is the document processing platform for AI

★ 52.4k stars⑂ 8.3k forksMITupdated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

Llama Index is an open-source ai agents project by run-llama (run-llama/llama_index on GitHub). It has 52,396 GitHub stars and 8,272 forks, is released under the MIT license, and was last updated on GitHub on 1 October 2026.

GitHub stars
52,396
Forks
8,272
License
MIT
Last updated on GitHub
1 October 2026
Category
AI Agents
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Pick a framework

    Application frameworks, agent runtimes, or voice stacks — choose by whether you are building apps, automations, or realtime voice.

  2. Install the SDK

    Python or TypeScript packages install with one command; most include quickstart templates.

  3. Define tools and memory

    Give the agent functions it can call and a memory layer so it stays coherent across sessions.

  4. Run and observe

    Launch the agent, watch its tool calls and reasoning traces, and iterate on prompts and tools.

// features

Why people love it

Tool use & function calling

Agents act on the world: APIs, databases, browsers, and shell commands.

Multi-agent orchestration

Crews and graphs of specialized agents collaborating on complex tasks.

Memory systems

Short and long-term memory so agents improve across sessions.

Observability

Traces of every decision and tool call — essential for debugging.

Voice & realtime

Low-latency stacks for conversational voice agents.

Model agnostic

Swap underlying LLMs without rewriting your agent logic.

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// run it

Run it yourself

  1. Step 1 — Clone the repo

    Download the source and move into the project folder.

    git clone https://github.com/run-llama/llama_index.git
    cd llama_index
  2. Step 2 — Create an environment

    Isolate dependencies so nothing clashes with your other projects.

    python -m venv .venv
    source .venv/bin/activate  # Windows: .venv\Scripts\activate
  3. Step 3 — Install and launch

    Install the requirements, then follow the README's launch command (it varies per project).

    pip install -r requirements.txt
// use cases

What you can build

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// faq

Questions, answered

Framework or finished agent?

Frameworks build your own; some repos here ship ready-made agents you can run directly.

Do agents need big models?

Tool-calling quality matters more than raw size — strong mid-size models work well.

Who maintains Llama Index?

It is an open-source project at run-llama/llama_index with 8,272 forks. Check the repo's contributors and recent commits to see how active maintenance is.

How do I keep agents safe?

Sandbox tool access, confirm destructive actions, and review traces — treat agents like junior employees with supervision.

What does it cost to run?

Self-hosted frameworks are free; you pay only for model inference (local or API).

Where do I start?

A quickstart template in the framework's docs — most get a first agent running in minutes.

// keep exploring

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// alternatives

Alternatives in AI Agents

The most-starred other AI Agents projects in the LibreAI catalogue — the realistic shortlist if Llama Index is not the right fit.

Get Llama Index

Free and open — grab the source, star the repo, and start creating.