This repo shows examples of applications built on top of Llama Stack. Starting Llama 3.1 you can build agentic applications capable of:
breaking a task down and performing multi-step reasoning.
using tools to perform some actions
built-in: the model has built-in knowledge of tools like search or code interpreter
zero-shot: the model can learn to call tools using previously unseen, in-context tool definitions
providing system level safety protections using models like Llama Guard.
Note
The Llama Stack API is still evolving and may change. Feel free to build and experiment, but please don't rely on its stability just yet!
An agentic app requires a few components:
ability to run inference on the underlying Llama series of models
ability to run safety checks using the Llama Guard series of models
ability to execute tools, including a code execution environment, and loop using the model's multi-step reasoning process
All of these components are now offered by a single Llama Stack Distribution. The Llama Stack defines and standardizes these components and many others that are needed to make building Generative AI applications smoother. Various implementations of these APIs are then assembled together via a Llama Stack Distribution.
To get started with Llama Stack Apps, you'll need to:
Install prerequisites
Start a Llama Stack server
Connect your client agentic app to Llama Stack server
Once started, you can then just point your agentic app to the URL for this server (e.g. http://localhost:5000).
Python Packages
We recommend creating an isolated conda Python environment.
# Create and activate a virtual environmentENV=stack conda create -n $ENV python=3.10cd <path-to-llama-stack-apps-repo>conda activate $ENV# Install dependenciespip install -r requirements.txt
This will install all dependencies required to (1) Build and start a Llama Stack server (2) Connect your client app to Llama Stack server.
Please see our llama-stack repo's Developer Guide for setting up a Llama Stack distribution and running server to serve API endpoints. You should have a server endpoint for building your client apps.
Once your your server started, you should have see outputs --
... Serving POST /agentic_system/session/delete Serving POST /agentic_system/session/get Serving POST /agentic_system/step/get Serving POST /agentic_system/turn/get Serving GET /telemetry/get_trace Serving POST /telemetry/log_event Listening on :::5000 INFO: Started server process [587053] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
We have built sample demo scripts for interating with the Stack server.
With the server running, you may run to test out an simple Agent
python -m examples.agents.hello localhost 5000
You will see outputs of the form --
> created agents with agent_id=d050201b-0ca1-4abd-8eee-3cba2b8c0fbc
User> Hello
shield_call> No Violation
inference> How can I assist you today?
shield_call> No Violation
User> Which players played in the winning team of the NBA western conference semifinals of 2024, please use tools
shield_call> No Violation
inference> brave_search.call(query="NBA Western Conference Semifinals 2024 winning team players")
tool_execution> Tool:brave_search Args:{'query': 'NBA Western Conference Semifinals 2024 winning team players'}
tool_execution> Tool:brave_search Response:{"query": "NBA Western Conference Semifinals 2024 winning team players", "top_k": [{"title": "2024 NBA Western Conference Semifinals - Mavericks vs. Thunder | Basketball-Reference.com", "url": "https://www.basketball-reference.com/playoffs/2024-nba-western-conference-semifinals-mavericks-vs-thunder.html", "description": "Summary and statistics for the <strong>2024</strong> <strong>NBA</strong> <strong>Western</strong> <strong>Conference</strong> <strong>Semifinals</strong> - Mavericks vs. Thunder", "type": "search_result"}, {"title": "2024 NBA playoffs - Wikipedia", "url": "https://en.wikipedia.org/wiki/2024_NBA_playoffs", "description": "Aged 20 years and 96 days old, ... youngest <strong>player</strong> <strong>in</strong> <strong>NBA</strong> history to record 10+ points and 15+ rebounds in a playoff game, coming during game 6 of the Maverick's <strong>Western</strong> <strong>Conference</strong> <strong>Semifinal</strong> <strong>win</strong> against the Thunder on May 18. The Timberwolves overcame a 20u2013point deficit to <strong>win</strong> game 7 against the Nuggets, the largest game 7 comeback in <strong>NBA</strong> playoffs history. With the defending champion Nuggets losing to the Minnesota Timberwolves, the <strong>2024</strong> playoffs marked ...", "type": "search_result"}, {"title": "2024 NBA Playoffs | Official Bracket, Schedule and Series Matchups", "url": "https://www.nba.com/playoffs/2024", "description": "The official site of the <strong>2024</strong> <strong>NBA</strong> Playoffs. Latest news, schedules, matchups, highlights, bracket and more.", "type": "search_result"}]}
shield_call> No Violation
inference> The players who played in the winning team of the NBA Western Conference Semifinals of 2024 are not specified in the search results provided. However, the search results suggest that the Mavericks played against the Thunder in the Western Conference Semifinals, and the Mavericks won the series.
shield_call> No ViolationNow that the Stack server is setup, the next thing would be to run an agentic app using Agents APIs.
We have built sample scripts, notebooks and a UI chat interface ( using Gradio ! ) to help you get started.
Start an app (local) and interact with it by running the following command:
PYTHONPATH=. python examples/agent_store/app.py localhost 5000
This will start a mesop app and you can go to localhost:7860 to play with the chat interface.
Optionally, you can setup API keys for custom tools:
WolframAlpha: store in WOLFRAM_ALPHA_API_KEY environment variable
Brave Search: store in BRAVE_SEARCH_API_KEY environment variable
You may see other ways of interating in Agent Store README.md
NOTE: Ensure that Stack server is still running.
cd <path-to-llama-agentic-system>conda activate $ENVllama stack run <name> # If not already startedPYTHONPATH=. python -m examples.agents.rag_with_memory_bank localhost 5000
You should see outputs to stdout of the form --
Environment: ipython Tools: brave_search, wolfram_alpha, photogen Cutting Knowledge Date: December 2023 Today Date: 23 July 2024 User> I am planning a trip to Switzerland, what are the top 3 places to visit?Final Llama Guard response shield_type=<BuiltinShield.llama_guard: 'llama_guard'> is_violation=False violation_type=None violation_return_message=None Ran PromptGuardShield and got Scores: Embedded: 0.9999765157699585, Malicious: 1.1110752893728204e-05 StepType.shield_call> No Violation role='user' content='I am planning a trip to Switzerland, what are the top 3 places to visit?'StepType.inference> Switzerland is a beautiful country with a rich history, culture, and natural beauty. Here are three must-visit places to add to your itinerary: ....
Tip You can optionally do
--disable-safetyin the scripts to avoid running safety shields all the time.
Feel free to reach out if you have questions.
Check out our client SDKs for connecting to Llama Stack server, you can choose from python, node, swift, and kotlin programming languages to quickly build your applications.
Note
While you can run the apps using venv, installation of a distribution requires conda.
# Create and activate a virtual environmentpython3 -m venv venvsource venv/bin/activate
# Create and activate a virtual environmentpython -m venv venv venvScriptsactivate # For Command Prompt# or.venvScriptsActivate.ps1 # For PowerShell# orsource venvScriptsactivate # For Git
The instructions thereafter (including pip install -r requirements.txt for installing the dependencies) remain the same.
