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Quickstart

Get LlamaFarm installed, ingest a dataset, and run your first RAG-powered chat in minutes.

1. Prerequisites​

  • Ollama — Local model runtime (or any OpenAI-compatible provider)

2. Install LlamaFarm​

Option A: Desktop App (Easiest)​

Download the all-in-one desktop application:

PlatformDownload
Mac (Universal)⬇️ Download
Windows⬇️ Download
Linux (x86_64)⬇️ Download
Linux (arm64)⬇️ Download

The desktop app bundles everything you need—no additional installation required.

Option B: CLI Installation​

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

Manual Download:

Download the lf binary directly from the releases page:

PlatformBinary
macOS (Apple Silicon)lf-darwin-arm64
macOS (Intel)lf-darwin-amd64
Linux (x64)lf-linux-amd64
Linux (arm64)lf-linux-arm64
Windows (x86_64)lf-windows-amd64.exe

After downloading, make it executable and add to your PATH:

chmod +x lf-darwin-arm64
sudo mv lf-darwin-arm64 /usr/local/bin/lf

Verify installation:

lf --help

3. Configure Your Runtime (Ollama)​

For best RAG results with longer documents, increase the Ollama context window:

  1. Open the Ollama app
  2. Navigate to Settings → Advanced
  3. Adjust the context window size (recommended: 32K+ for documents)

Pull a model if you haven't already:

ollama pull llama3.2
ollama pull nomic-embed-text  # For embeddings

4. Create a Project​

lf init my-project
cd my-project

This creates llamafarm.yaml with default runtime, prompts, and RAG configuration.

5. Start LlamaFarm​

lf start

This command:

  • Starts the API server and Universal Runtime natively
  • Opens the interactive chat TUI
  • Launches the Designer web UI at http://localhost:8000

Hit Ctrl+C to exit the chat UI when you're done.

Use the Designer Web UI

Prefer a visual interface? Open http://localhost:8000 in your browser to access the Designer—manage projects, upload datasets, configure models, and test prompts without touching the command line.

See the Designer documentation for details.

Running Services Manually​

For development, you can run services individually:

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

npm install -g nx
nx init --useDotNxInstallation --interactive=false

# Start both services
nx dev

# Or in separate terminals:
nx start server           # Terminal 1
nx start universal-runtime # Terminal 2

6. Chat with Your Project​

# Interactive chat (opens TUI)
lf chat

# One-off message
lf chat "What can you do?"

Useful options:

  • --no-rag — Bypass retrieval, hit the model directly
  • --database, --retrieval-strategy — Override RAG behavior
  • --curl — Print the equivalent curl command

7. Create and Populate a Dataset​

# Create a dataset
lf datasets create -s pdf_ingest -b main_db research-notes

# Upload documents (supports globs/directories); auto-processes by default
lf datasets upload research-notes ./examples/fda_rag/files/*.pdf
# For batching without processing:
# lf datasets upload research-notes ./examples/fda_rag/files/*.pdf --no-process

8. Process Documents​

lf datasets process research-notes    # Only needed if you skipped auto-processing

This sends documents through the RAG pipeline—parsing, chunking, embedding, and indexing.

For large PDFs, processing may take a few minutes. The CLI shows progress indicators.

9. Query with RAG​

lf rag query --database main_db "What are the key findings?"

Useful flags:

  • --top-k 10 — Number of results
  • --filter "file_type:pdf" — Metadata filtering
  • --include-metadata — Show document sources
  • --include-score — Show relevance scores

10. Next Steps​

Need help? Chat with us on Discord or open a discussion.