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Autollama

Autollama is a JavaScript-first RAG framework that applies contextual retrieval to document chunks and visually compares the original and context-enriched text before embedding.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Autollama — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Autollama when developing a document-based RAG pipeline and you want to review contextualized chunks before embedding them.

Who it is for

  • Developers building document-ingestion and RAG workflows.
  • Teams that want to inspect contextual changes to chunks before embedding.

Documented uses

  • Add contextual information to document chunks before embedding.
  • Compare original and enriched chunks visually.
  • Initialize and manage a RAG project through a CLI or Docker Compose setup.
Before you choose

Requirements & limits

  • The documented npm/CLI prerequisites include Node.js 16+, npm 7+, an OpenAI API key and a recommendation of 4 GB or more RAM.
  • The Docker route requires Docker 20+ with Docker Compose; 8 GB or more RAM is recommended for the full containerized setup.
  • The README describes local development with SQLite and embedded services, hybrid local/cloud services, and a PostgreSQL production mode.
  • The repository displays an MIT license badge.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Initialize and start an Autollama project with the CLI

Set up the project and start its services using the documented npm/CLI path.

Before you start

  • Node.js 16 or later
  • npm 7 or later
  • At least 4 GB RAM recommended
  • An OpenAI API key

Steps

  1. Install the CLI with the command below.

    Documented command
    npm install -g autollama

  2. Initialize a project with autollama init my-rag-project.

  3. Start the framework with autollama start.

  4. Open http://localhost:8080 to reach the interface described by the README.

Expected outcome

A CLI-initialized project with services started; the README directs users to the local address to upload documents.

Guide limitations

  • The README lists an OpenAI API key as a prerequisite for npm/CLI installation.
  • The steps reflect the repository instructions and are not execution tests.
Documentation 1 ↗
Source-based practical guide

Start the Docker Compose setup

Launch Autollama's documented Docker Compose setup from a local clone.

Before you start

  • Docker 20 or later with Docker Compose
  • At least 8 GB RAM recommended for the full containerized setup

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/autollama/autollama.git

  2. Change into the repository directory with cd autollama.

  3. Start the containers with the command below.

    Documented command
    docker compose up -d

Expected outcome

The README's Docker Compose startup command has been issued for the local repository.

Guide limitations

  • The README recommends 8 GB or more of RAM for the full containerized setup.
  • These are documented instructions, not a verified deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
autollama/autollama · Official homepage title · retained in its source language
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