Composable building blocks to build Llama Apps https://llama-stack.readthedocs.io
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Author: m-misiura <mmisiura@redhat.com>
Date:   Mon Mar 24 14:33:50 2025 +0000

    🚧 working on the config file so that it is inheriting from pydantic base models

commit 0546379f817e37bca030247b48c72ce84899a766
Author: m-misiura <mmisiura@redhat.com>
Date:   Mon Mar 24 09:14:31 2025 +0000

    🚧 dealing with ruff checks

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Author: m-misiura <mmisiura@redhat.com>
Date:   Mon Mar 24 09:03:18 2025 +0000

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Author: m-misiura <mmisiura@redhat.com>
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Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 17:09:02 2025 +0000

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commit 69e8ddc2f8a4e13cecbab30272fd7d685d7864ec
Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 16:57:28 2025 +0000

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Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 14:01:03 2025 +0000

    🚧 fixing mypy errors in `__init__.py`

commit 4d2e3b55c4102ed75d997c8189847bbc5524cb2c
Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 12:58:06 2025 +0000

    🚧 ensuring routing_tables.py do not fail the ci

commit c0cc7b4b09ef50d5ec95fdb0a916c7ed228bf366
Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 12:09:24 2025 +0000

    🐛 fixing linter problems

commit 115a50211b604feb4106275204fe7f863da865f6
Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 11:47:04 2025 +0000

    🐛 fixing ruff errors

commit 29b5bfaabc77a35ea036b57f75fded711228dbbf
Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 11:33:31 2025 +0000

    🎨 automatic ruff fixes

commit 7c5a334c7d4649c2fc297993f89791c1e5643e5b
Author: m-misiura <mmisiura@redhat.com>
Date:   Fri Mar 21 11:15:02 2025 +0000

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    commit e671aae5bcd4ea57d601ee73c9e3adf5e223e830
    Merge: b0dd9a4f 9114bef4
    Author: Mac Misiura <82826099+m-misiura@users.noreply.github.com>
    Date:   Fri Mar 21 09:45:08 2025 +0000

        Merge branch 'meta-llama:main' into feat_fms_remote_safety_provider

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    Author: m-misiura <mmisiura@redhat.com>
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        📝 updated `provider_id`

    commit 4c8906c1a4e960968b93251d09d5e5735db15026
    Author: m-misiura <mmisiura@redhat.com>
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    Author: m-misiura <mmisiura@redhat.com>
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    Author: m-misiura <mmisiura@redhat.com>
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    Author: m-misiura <mmisiura@redhat.com>
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        🎉 added a new remote safety provider compatible with FMS Orchestrator API and Detectors API

        Signed-off-by: m-misiura <mmisiura@redhat.com>
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.github ci: Enforce concurrency to reduce CI loads (#1738) 2025-03-20 22:28:47 -04:00
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rfcs chore: remove straggler references to llama-models (#1345) 2025-03-01 14:26:03 -08:00
scripts feat(server): add attribute based access control for resources (#1703) 2025-03-19 21:28:52 -07:00
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Llama Stack

PyPI version PyPI - Downloads License Discord Unit Tests Integration Tests

Quick Start | Documentation | Colab Notebook

Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides

  • Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
  • Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
  • Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
  • Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
  • Standalone applications as examples for how to build production-grade AI applications with Llama Stack.
Llama Stack

Llama Stack Benefits

  • Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
  • Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
  • Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.

By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications.

API Providers

Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.

API Provider Builder Environments Agents Inference Memory Safety Telemetry
Meta Reference Single Node
SambaNova Hosted
Cerebras Hosted
Fireworks Hosted
AWS Bedrock Hosted
Together Hosted
Groq Hosted
Ollama Single Node
TGI Hosted and Single Node
NVIDIA NIM Hosted and Single Node
Chroma Single Node
PG Vector Single Node
PyTorch ExecuTorch On-device iOS
vLLM Hosted and Single Node
OpenAI Hosted
Anthropic Hosted
Gemini Hosted

Distributions

A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider implementations for each API component. Distributions make it easy to get started with a specific deployment scenario - you can begin with a local development setup (eg. ollama) and seamlessly transition to production (eg. Fireworks) without changing your application code. Here are some of the distributions we support:

Distribution Llama Stack Docker Start This Distribution
Meta Reference llamastack/distribution-meta-reference-gpu Guide
Meta Reference Quantized llamastack/distribution-meta-reference-quantized-gpu Guide
SambaNova llamastack/distribution-sambanova Guide
Cerebras llamastack/distribution-cerebras Guide
Ollama llamastack/distribution-ollama Guide
TGI llamastack/distribution-tgi Guide
Together llamastack/distribution-together Guide
Fireworks llamastack/distribution-fireworks Guide
vLLM llamastack/distribution-remote-vllm Guide

Documentation

Please checkout our Documentation page for more details.

Llama Stack Client SDKs

Language Client SDK Package
Python llama-stack-client-python PyPI version
Swift llama-stack-client-swift Swift Package Index
Typescript llama-stack-client-typescript NPM version
Kotlin llama-stack-client-kotlin Maven version

Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from python, typescript, swift, and kotlin programming languages to quickly build your applications.

You can find more example scripts with client SDKs to talk with the Llama Stack server in our llama-stack-apps repo.