BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.
Open source
24 alternativesFlama
Flama serves predictive and generative AI models as APIs, with compatible endpoints, a chat interface, and MCP support.
About Flama
Flama is an open source framework that packages predictive and generative AI models into portable .flm files and serves them over HTTP. It works with scikit-learn, TensorFlow, PyTorch, and LLMs, including models from Hugging Face Hub, with OpenAI-, Anthropic-, and Ollama-compatible endpoints, a chat UI at /chat/, and MCP support for tools, resources, and prompts.
Schema validation, database resources, and LLM serving require optional installs. LLM serving uses vLLM on Linux and MLX on Apple Silicon, and base installation supports Python 3.10 to 3.14 on Linux, macOS, and Windows without a Rust toolchain.
Key features
- Package scikit-learn, TensorFlow, PyTorch, LLM models into .flm files
- Download and package Hugging Face Hub models via command
- Serve LLMs through OpenAI, Anthropic, Ollama-compatible endpoints
- Provide streaming chat UI at /chat/ with Markdown, LaTeX, Mermaid
- Expose tools, resources, prompts over MCP via decorator
- Serve predictive models with metadata and predict endpoints plus OpenAPI schema
Best for: Developers who need to serve predictive models and LLMs as HTTP APIs.
Good for
- Handles predictive and generative models with the same packaging workflow
- Installs on Linux, macOS, and Windows for Python 3.10 to 3.14 without Rust toolchain
- Provides OpenAPI schema, Swagger UI, and streaming chat UI for served models
Watch out for
- Schema validation, database, and LLM serving require separate optional installs
- Generative serving backend is platform-specific: vLLM on Linux, MLX on Apple Silicon
Flama at a glance
- Website
- github.com/vortico/flama
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Flama FAQ
How do I serve a predictive model?
Package it to a .flm file with flama.dump or flama get, then run flama serve pointing at the file. Metadata is at GET /model/ and inference is at POST /model/predict/.
How do I serve an LLM?
Use flama get with --family llm to download and package a Hugging Face model into a .flm file, then run flama serve with that file to provide an HTTP API and chat UI.
What is needed to install Flama?
Install from PyPI with pip install flama for Python 3.10 to 3.14 on Linux, macOS, or Windows. Add extras such as pydantic, database, or llm for those functions.
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