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Quickstart

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

1. 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. Launch it and the built-in Designer will guide you through project setup with an onboarding wizard.

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

2. Create a Project​

lf init my-project
cd my-project

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

3. 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:14345

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

Use the Designer Web UI​

Open http://localhost:14345 in your browser to access the Designer — a visual interface for managing your entire project without touching the command line.

When you open a new project in the Designer, the onboarding wizard walks you through setup step by step:

  1. What are you building? — Describe your project's purpose
  2. Pick a model — Choose from available local models or connect an API
  3. Add data — Upload documents for RAG or skip for later
  4. Test it out — Send your first message and see it work

The wizard generates your llamafarm.yaml configuration automatically. After setup, you get full access to the Dashboard, Prompts editor, Data management, RAG configuration, Model settings, and Test interface.

See the Designer documentation for the full guide.

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

4. 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

5. 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

6. 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.

7. 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

8. Next Steps​

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