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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The production framework for Predictive and Generative AI. Serve any model as an API in one line, with OpenAI/Anthropic/Ollama-compatible endpoints, a built-in chat UI, and native MCP.
| Date | Stars |
|---|---|
| 2026-07-24 | 294 |
| 2026-07-25 | 294 |
| 2026-07-28 | 295 |
| 2026-07-30 | 295 |
| 2026-07-31 | 295 |
| 2026-08-02 | 295 |
| 2026-08-05 | 296 |
| 2026-08-06 | 296 |
Today
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This week
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Momentum
16.0
growth rate 0.34%/day
<p align="center">
<a href="https://flama.dev"><img src="https://raw.githubusercontent.com/vortico/flama/master/.github/logo.png" alt='Flama'></a>
</p>
<p align="center">
<em>Light up your models</em> 🔥
</p>
<p align="center">
<a href="https://github.com/vortico/flama/actions/workflows/ci_production.yaml">
<img src="https://github.com/vortico/flama/actions/workflows/ci_production.yaml/badge.svg" alt="Production workflow status">
</a>
<a href="https://pypi.org/project/flama/">
<img src="https://img.shields.io/pypi/v/flama?logo=PyPI&logoColor=white" alt="Package version">
</a>
<a href="https://pypi.org/project/flama/">
<img src="https://img.shields.io/pypi/pyversions/flama?logo=Python&logoColor=white" alt="PyPI - Python Version">
</a>
<a href="https://pepy.tech/project/flama">
<img src="https://img.shields.io/pepy/dt/flama?logo=python&logoColor=white&label=downloads" alt="Downloads">
</a>
<a href="https://flama.dev/docs/">
<img src="https://img.shields.io/badge/docs-flama.dev-E25822" alt="Documentation">
</a>
<a href="https://github.com/vortico/flama/discussions">
<img src="https://img.shields.io/github/discussions/vortico/flama?logo=github&logoColor=white" alt="GitHub Discussions">
</a>
</p>
---
# Flama
**The production framework for Predictive and Generative AI.**
Turn any model into a production API in a single line of code. Serve predictive and
generative models on a Rust-powered core, and expose your tools to AI agents over the
Model Context Protocol (MCP).
Flama is the **F**ramework for **L**ightweight **A**pplications, artificial intelligence
**M**odels, and **A**utomation. It packages a model from any of the mainstream frameworks
into a single portable format (the `.flm` file), so every model looks the same to your API
no matter where it came from, and serves it over HTTP in seconds.
<p align="center">
<img src="https://raw.githubusercontent.com/vortico/flama/master/.github/assets/chat.gif" alt="The streaming chat UI that ships with every model served by Flama" width="100%">
</p>
- 📦 **Any framework, one format.** Package scikit-learn, TensorFlow, PyTorch, or an LLM into a single portable `.flm` artifact.
- ⬇️ **Models on demand.** Download and package any model from the HuggingFace Hub with one command.
- 🤖 **Generative AI serving.** Serve LLMs with OpenAI-, Anthropic-, and Ollama-compatible endpoints, side by side.
- 💬 **Chatbot out of the box.** Every served model ships a polished streaming chat UI at `/chat/`, with Markdown, LaTeX, and Mermaid.
- 🔌 **Native MCP.** Expose tools, resources, and prompts to AI agents with a single decorator, schemas derived from your type hints.
- ⚡ **Rust-powered core.** Routing, JSON encoding, request parsing, and compression compiled to native code, shipped as plain wheels.
- 🚀 **Production-ready first.** Go from a packaged model to a running service over the CLI, in Python, with a spec file, or inside a container.
## Installation
Flama is published on PyPI and ships native wheels for every supported Python version
(3.10 to 3.14) on Linux, macOS, and Windows. No Rust toolchain required.
```commandline
pip install flama
```
Schema, database, and LLM support are optional extras, so you install only what you need:
```commandline
pip install "flama[pydantic]" # schema validation (also: typesystem, marshmallow)
pip install "flama[database]" # SQLAlchemy-backed resources
pip install "flama[llm]" # generative AI serving (vLLM on Linux, MLX on Apple Silicon)
pip install "flama[full]" # everything
```
See the [installation docs](https://flama.dev/docs/getting-started/installation/) for details.
## Quickstart: serve an LLM
From zero to a production API with a built-in chat UI in three commands, no Python code
required:
```commandline
pip install "flama[llm,pydantic]"
# 1. Download and package a model from HuggingFace into a portable .fExcerpt of 10,289 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cc9521b9c74a022a, topic:inference, topic:model-serving, topic:llm-serving
matched fp:cc9521b9c74a022a, topic:mcp, topic:model-context-protocol, readme:model context protocol