LlamaFarm - Edge AI for Everyone

Enterprise AI capabilities on your own hardware. No cloud required.

LlamaFarm is an open-source AI platform that runs entirely on your hardware. Build RAG applications, train custom classifiers, detect anomalies, and run document processing—all locally with complete privacy.

• 🔒 Complete Privacy — Your data never leaves your device
• 💰 No API Costs — Use open-source models without per-token fees
• 🌐 Offline Capable — Works without internet once models are downloaded
• ⚡ Hardware Optimized — Automatic GPU/NPU acceleration on Apple Silicon, NVIDIA, and AMD

Desktop App Downloads

Get started instantly — no command line required:

Platform	Download
Mac (Universal)	Download
Windows	Download
Linux (x86_64)	Download
Linux (ARM64)	Download

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What Can You Build?

Capability	Description
RAG (Retrieval-Augmented Generation)	Ingest PDFs, docs, CSVs and query them with AI
Custom Classifiers	Train text classifiers with 8-16 examples using SetFit
Anomaly Detection	Detect outliers in logs, metrics, or transactions
OCR & Document Extraction	Extract text and structured data from images and PDFs
Named Entity Recognition	Find people, organizations, and locations
Multi-Model Runtime	Switch between Ollama, OpenAI, vLLM, or local GGUF models

Video demo (90 seconds): https://youtu.be/W7MHGyN0MdQ

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Quickstart

Option 1: Desktop App

Download the desktop app above and run it. No additional setup required.

Option 2: CLI + Development Mode

1. Install the CLI

   macOS / Linux:

   curl -fsSL https://raw.githubusercontent.com/llama-farm/llamafarm/main/install.sh | bash

   Windows (PowerShell):

   irm https://raw.githubusercontent.com/llama-farm/llamafarm/main/install.ps1 | iex

   Or download directly from releases.

2. Create and run a project

   lf init my-project      # Generates llamafarm.yaml
   lf start                # Starts services and opens Designer UI

3. Chat with your AI

   lf chat                           # Interactive chat
   lf chat "Hello, LlamaFarm!"       # One-off message

The Designer web interface is available at http://localhost:8000.

Option 3: Development from Source

git clone https://github.com/llama-farm/llamafarm.git
cd llamafarm

# Install Nx globally and initialize the workspace
npm install -g nx
nx init --useDotNxInstallation --interactive=false  # Required on first clone

# Start all services (run each in a separate terminal)
nx start server           # FastAPI server (port 8000)
nx start rag              # RAG worker for document processing
nx start universal-runtime # ML models, OCR, embeddings (port 11540)

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Architecture

LlamaFarm consists of three main services:

Service	Port	Purpose
Server	8000	FastAPI REST API, Designer web UI, project management
RAG Worker	-	Celery worker for async document processing
Universal Runtime	11540	ML model inference, embeddings, OCR, anomaly detection

All configuration lives in llamafarm.yaml—no scattered settings or hidden defaults.

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Runtime Options

Universal Runtime (Recommended)

The Universal Runtime provides access to HuggingFace models plus specialized ML capabilities:

• Text Generation - Any HuggingFace text model
• Embeddings - sentence-transformers and other embedding models
• OCR - Text extraction from images/PDFs (Surya, EasyOCR, PaddleOCR, Tesseract)
• Document Extraction - Forms, invoices, receipts via vision models
• Text Classification - Pre-trained or custom models via SetFit
• Named Entity Recognition - Extract people, organizations, locations
• Reranking - Cross-encoder models for improved RAG quality
• Anomaly Detection - Isolation Forest, One-Class SVM, Local Outlier Factor, Autoencoders

runtime:
  models:
    default:
      provider: universal
      model: Qwen/Qwen2.5-1.5B-Instruct
      base_url: http://127.0.0.1:11540/v1

Ollama

Simple setup for GGUF models with CPU/GPU acceleration:

runtime:
  models:
    default:
      provider: ollama
      model: qwen3:8b
      base_url: http://localhost:11434/v1

OpenAI-Compatible

Works with vLLM, Together, Mistral API, or any OpenAI-compatible endpoint:

runtime:
  models:
    default:
      provider: openai
      model: gpt-4o
      base_url: https://api.openai.com/v1
      api_key: ${OPENAI_API_KEY}

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Core Workflows

CLI Commands

Task	Command
Initialize project	lf init my-project
Start services	lf start
Interactive chat	lf chat
One-off message	lf chat "Your question"
List models	lf models list
Use specific model	lf chat --model powerful "Question"
Create dataset	lf datasets create -s pdf_ingest -b main_db research
Upload files (auto-process by default)	lf datasets upload research ./docs/*.pdf
Process dataset (if you skipped auto-process)	lf datasets process research
Query RAG	lf rag query --database main_db "Your query"
Check RAG health	lf rag health

RAG Pipeline

1. Create a dataset linked to a processing strategy and database
2. Upload files (PDF, DOCX, Markdown, TXT) — processing runs automatically unless you pass --no-process
3. Process manually only when you intentionally skipped auto-processing (e.g., large batches)
4. Query using semantic search with optional metadata filtering

lf datasets create -s default -b main_db research
lf datasets upload research ./papers/*.pdf                 # auto-processes by default
# For large batches:
# lf datasets upload research ./papers/*.pdf --no-process
# lf datasets process research
lf rag query --database main_db "What are the key findings?"

Designer Web UI

The Designer at http://localhost:8000 provides:

• Visual dataset management with drag-and-drop uploads
• Interactive configuration editor with live validation
• Integrated chat with RAG context
• Switch between visual and YAML editing modes

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Configuration

llamafarm.yaml is the source of truth for each project:

version: v1
name: my-assistant
namespace: default

# Multi-model configuration
runtime:
  default_model: fast

  models:
    fast:
      description: "Fast local model"
      provider: universal
      model: Qwen/Qwen2.5-1.5B-Instruct
      base_url: http://127.0.0.1:11540/v1

    powerful:
      description: "More capable model"
      provider: universal
      model: Qwen/Qwen2.5-7B-Instruct
      base_url: http://127.0.0.1:11540/v1

# System prompts
prompts:
  - name: default
    messages:
      - role: system
        content: You are a helpful assistant.

# RAG configuration
rag:
  databases:
    - name: main_db
      type: ChromaStore
      default_embedding_strategy: default_embeddings
      default_retrieval_strategy: semantic_search
      embedding_strategies:
        - name: default_embeddings
          type: UniversalEmbedder
          config:
            model: sentence-transformers/all-MiniLM-L6-v2
            base_url: http://127.0.0.1:11540/v1
      retrieval_strategies:
        - name: semantic_search
          type: BasicSimilarityStrategy
          config:
            top_k: 5

  data_processing_strategies:
    - name: default
      parsers:
        - type: PDFParser_LlamaIndex
          config:
            chunk_size: 1000
            chunk_overlap: 100
        - type: MarkdownParser_Python
          config:
            chunk_size: 1000
      extractors: []

# Dataset definitions
datasets:
  - name: research
    data_processing_strategy: default
    database: main_db

Environment Variable Substitution

Use ${VAR} syntax to inject secrets from .env files:

runtime:
  models:
    openai:
      api_key: ${OPENAI_API_KEY}
      # With default: ${OPENAI_API_KEY:-sk-default}
      # From specific file: ${file:.env.production:API_KEY}

See the Configuration Guide for complete reference.

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REST API

LlamaFarm provides an OpenAI-compatible REST API:

Chat Completions

curl -X POST http://localhost:8000/v1/projects/default/my-project/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Hello"}],
    "stream": false,
    "rag_enabled": true
  }'

RAG Query

curl -X POST http://localhost:8000/v1/projects/default/my-project/rag/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the requirements?",
    "database": "main_db",
    "top_k": 5
  }'

See the API Reference for all endpoints.

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Specialized ML Capabilities

The Universal Runtime provides endpoints beyond chat:

OCR & Document Extraction

curl -X POST http://localhost:8000/v1/vision/ocr \
  -F "file=@document.pdf" \
  -F "model=surya"

Anomaly Detection

# Train on normal data
curl -X POST http://localhost:8000/v1/ml/anomaly/fit \
  -H "Content-Type: application/json" \
  -d '{"model": "sensor-detector", "backend": "isolation_forest", "data": [[22.1], [23.5], ...]}'

# Detect anomalies
curl -X POST http://localhost:8000/v1/ml/anomaly/detect \
  -H "Content-Type: application/json" \
  -d '{"model": "sensor-detector", "data": [[22.0], [100.0], [23.0]], "threshold": 0.5}'

Text Classification & NER

See the Models Guide for complete documentation.

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Examples

Example	Description	Location
FDA Letters Assistant	Multi-PDF ingestion, regulatory queries	examples/fda_rag/
Raleigh Planning Helper	Large ordinance documents, geospatial queries	examples/gov_rag/
OCR & Document Processing	Image text extraction, form parsing	examples/ocr_and_document/

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Development & Testing

# Python server tests
cd server && uv sync && uv run --group test python -m pytest

# CLI tests
cd cli && go test ./...

# RAG tests
cd rag && uv sync && uv run pytest tests/

# Universal Runtime tests
cd runtimes/universal && uv sync && uv run pytest tests/

# Build docs
nx build docs

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Extensibility

• Add runtimes by implementing provider support and updating schema
• Add vector stores by implementing store backends (Chroma, Qdrant, etc.)
• Add parsers for new file formats (PDF, DOCX, HTML, CSV, etc.)
• Add extractors for custom metadata extraction
• Add CLI commands under cli/cmd/

See the Extending Guide for step-by-step instructions.

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Community & Support

• Discord - Chat with the team and community
• GitHub Issues - Bug reports and feature requests
• Discussions - Ideas and proposals
• Contributing Guide - Code style and contribution process

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License

Licensed under the Apache 2.0 License. See CREDITS for acknowledgments.

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Build locally. Deploy anywhere. Own your AI.