# containers/ramalama

RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers.

Repository: https://github.com/containers/ramalama
Canonical: https://ross.abutalabs.com/products/ramalama
Homepage: https://ramalama.ai
Language: Python
License: MIT
License Family: permissive
Topics: ai, containers, cuda, hip, inference-server, intel, llamacpp, llm, podman, vllm, hacktoberfest
Last push: 2026-08-24T10:37:31+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 55
- inputs: {"age_days": 770, "days_push": 9, "days_rel": 12, "gap_med": 7, "n_releases_24m": 66}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3018, forks 358 (observed 2026-08-28T04:07:38.376122+00:00)

## What it is
RamaLama is a Python CLI tool that simplifies serving and running AI/LLM models locally by leveraging OCI containers (Podman/Docker). It auto-detects host GPUs, pulls appropriate accelerated container images, and treats models like container images with familiar commands.

## Use cases
- serve llm models locally in containers
- run ai models without configuring host gpu dependencies
- chat with local llm models from the terminal
- expose an openai-compatible rest api for local models
- pull and manage ai models from multiple registries like container images
- run llm inference securely in rootless containers

## When to choose
- you want container-based, rootless, isolated local LLM serving
- you want to avoid manually installing GPU drivers and inference dependencies
- you already use Podman/Docker workflows and want the same patterns for AI models
- you need models from multiple transports including OCI registries

## When to avoid
- you need a full production model-serving platform with autoscaling and multi-node orchestration
- you prefer native (non-containerized) inference for maximum performance
- you need fine-grained training or fine-tuning of models
- your environment cannot run containers

## Facets
- artifact type: cli-tool
- maturity: active
- function: llm-inference, cli, rag, chatbot, container-runtime
- domain: large-language-models, artificial-intelligence, developer-tools
- platform: cli
- tags: podman, llamacpp, vllm, gpu, rootless-containers, oci-containers, model-serving, openai-compatible-api, containers, command-line, linux, macos, docker

## Member repositories
- containers/ramalama (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:38.376122+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:46:54.688100+00:00, confidence not recorded.
  - readme: https://github.com/containers/ramalama (fetched 2026-08-28T04:07:38.376122+00:00, sha c4870fe25d60)
  - homepage: https://ramalama.ai (fetched 2026-08-29T09:45:09.934723+00:00, sha df4099fbf45b)
  - registry_pypi: https://pypi.org/pypi/ramalama/json (fetched 2026-08-29T09:45:09.943846+00:00, sha ce1145f44536)
- Data as of 2026-08-30T08:39:29.467469+00:00.