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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.
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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 Framework for Lightweight Applications, artificial intelligence
Models, and Automation. 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.
.flm artifact./chat/, with Markdown, LaTeX, and Mermaid.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.
pip install flama
Schema, database, and LLM support are optional extras, so you install only what you need:
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 for details.
From zero to a production API with a built-in chat UI in three commands, no Python code required:
pip install "flama[llm,pydantic]"
# 1. Download and package a model from HuggingFace into a portable .flm
flama get --family llm --source huggingface mlx-community/gemma-4-E2B-it-qat-4bit
# 2. Try it straight from your terminal
echo "What is Flama?" | flama model mlx-community_gemma-4-E2B-it-qat-4bit.flm stream --system "Be concise."
# 3. Serve it over HTTP
flama serve --model file=mlx-community_gemma-4-E2B-it-qat-4bit.flm,url=/,name=gemma
That is it: a full HTTP API, a streaming chat interface at
http://127.0.0.1:8000/chat/, and multi-dialect endpoints.
The same .flm file runs on vLLM (Linux with CUDA) or MLX (Apple Silicon), with
Flama selecting the backend at load time.
You do not even need a server to try a model. Pipe a prompt into flama model ... stream
and the response streams straight into your shell:
A single model can serve multiple wire protocols simultaneously, so existing OpenAI, Anthropic, and Ollama clients work without code changes, just point them at your server.
| Dialect | Prefix | Representative routes |
|---|---|---|
| Native | (none) | /query/, /stream/, /chat/ |
| OpenAI | /openai | /v1/chat/completions, /v1/completions, /v1/responses, /v1/models |
| Anthropic | /anthropic | /v1/messages, /v1/models |
| Ollama | /ollama | /api/chat, /api/generate, /api/tags |
Learn more in the Generative AI docs.
The same workflow serves classic ML models. Package a model trained in any mainstream framework:
import flama
from sklearn.neural_network import MLPClassifier
model = MLPClassifier(activation="tanh", hidden_layer_sizes=(10,))
# ... training ...
flama.dump(model, "model.flm")
Or fetch one straight from the Hub, then serve it:
flama get --family ml --source huggingface scikit-learn/Fish-Weight
flama serve --model file=scikit-learn_Fish-Weight.flm,url=/model,name=fish
Learn more in the Predictive AI docs.
Flama ships native, first-class support for the Model Context Protocol. Declare a capability with a single decorator, mount the server, and Flama derives the JSON Schema from your type hints and serves it over a stateless protocol:
from flama import Flama
app = Flama()
app.mcp.add_server("/mcp/tools/", "tools")
@app.mcp.tool("add", description="Add two integers", mcp="tools")
def add(a: int, b: int) -> int:
return a + b
Any MCP-capable client (Claude, Cursor, VS Code Copilot, or a custom agent) can discover and invoke it. Tasks, Elicitation, and MCP Apps are included. Learn more in the MCP docs.
Flama is also a complete toolkit for building production APIs:
Components, the base of the plugin ecosystem.flama upgrade codemods that rewrite imports and renamed symbols across major versions.A curated, documentation-aligned set of runnable examples lives in vortico/flama-examples, covering fundamentals, the CLI, advanced topics, predictive AI, generative AI, and domain-driven design.
Visit https://flama.dev/docs/ for the full documentation, including the quickstart and the CLI guide.
Drop skill.md into your AI coding assistant and let it
build Flama apps with full framework knowledge.
This project is absolutely open to contributions, so if you have a nice idea, please read our contributing docs before submitting a pull request. Questions and ideas are welcome in GitHub Discussions.
If you find Flama useful for building robust Machine Learning and Generative AI APIs, the best way to support our work is to give us a ⭐ on GitHub, it is the best fuel for our development efforts. You can also follow Vortico for updates.
Flama is released under the Apache 2.0 license.