Ross ROSS = Recommend OSS · open-source software intelligence for agents

ogx-ai/ogx

Open GenAI Stack observed · 2026-08-28

github.com/ogx-ai/ogx · homepage · Python · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

86/100

  • Activity 99
  • Release rhythm 86
  • Longevity 57
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 7.0
  • age_days: 799
  • days_rel: 13
  • days_push: 7
  • n_releases_24m: 81

Full methodology

Adoption not part of the score

8416 stars · 1363 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

OGX (formerly Llama Stack) is an open-source, OpenAI-compatible agentic API server and Python library that bundles inference, vector stores, file storage, moderation, tool calling, and agent orchestration into one process. It works with any model provider (Ollama, vLLM, Bedrock, Azure, etc.) and can be deployed as an HTTP server or imported as a library.

Use cases

  • self-host an OpenAI-compatible API server for local models
  • swap between LLM providers without changing application code
  • build AI agents with tool calling and vector store search
  • point Claude Code or Codex at alternative model backends
  • run a drop-in replacement for the OpenAI API on my own infrastructure
  • add moderation and file storage to an AI app without sidecar services

When to choose

  • you want an OpenAI-compatible server you fully control and can run anywhere
  • you need inference, vector stores, moderation, and agent orchestration in a single process
  • you want model-agnostic routing across providers like Ollama, vLLM, Bedrock, or Azure
  • you prefer a Python library option instead of running a separate gateway

When to avoid

  • you only need a lightweight inference proxy or load balancer rather than a full stack
  • you are locked into a single vendor's managed API and don't need portability
  • you need a specialized high-throughput inference engine rather than an API layer over providers
  • your stack is non-Python and you only want the library mode rather than the server

Facets

framework · maturity active

llm-inference agent-framework rag api-framework http-server vector-database chatbot sdk large-language-models apis self-hosted developer-tools python self-hosted cross-platform cli openai-compatible llama-stack api-server pluggable-providers model-agnostic tool-calling moderation vector-stores ai-agents retrieval-augmented-generation docker

3 sources

Member repositories

RepositoryRoleHealth v2
ogx-ai/ogxmain86

For agents

markdown · JSON · MCP: product_card(name="ogx-ai/ogx")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem