{"id":49453491,"url":"https://github.com/ogx-ai/ogx","last_synced_at":"2026-05-14T00:06:11.944Z","repository":{"id":249841766,"uuid":"820145382","full_name":"ogx-ai/ogx","owner":"ogx-ai","description":"Composable building blocks to build LLM 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-04-30T04:01:03.676Z","updated_at":"2026-05-01T00:01:14.188Z","avatar_url":"https://github.com/ogx-ai.png","language":"Python","funding_links":[],"categories":["Python","🤖 AI \u0026 Machine Learning"],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003eOGX\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://pypi.org/project/ogx/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/ogx?logo=pypi\" alt=\"PyPI Version\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/ogx/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/dm/ogx\" alt=\"PyPI Downloads\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://hub.docker.com/u/ogx\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ogx/distribution-starter?logo=docker\" alt=\"Docker Hub Pulls\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/ogx-ai/ogx/blob/main/LICENSE\"\u003e\u003cimg src=\"https://img.shields.io/pypi/l/ogx.svg\" alt=\"License\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://discord.gg/ZAFjsrcw\"\u003e\u003cimg src=\"https://img.shields.io/discord/1257833999603335178?color=5865F2\u0026logo=discord\u0026logoColor=ffffff\" alt=\"Discord\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/ogx-ai/ogx/actions/workflows/unit-tests.yml?query=branch%3Amain\"\u003e\u003cimg src=\"https://github.com/ogx-ai/ogx/actions/workflows/unit-tests.yml/badge.svg?branch=main\" alt=\"Unit Tests\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/ogx-ai/ogx/actions/workflows/integration-tests.yml?query=branch%3Amain\"\u003e\u003cimg src=\"https://github.com/ogx-ai/ogx/actions/workflows/integration-tests.yml/badge.svg?branch=main\" alt=\"Integration Tests\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://ogx-ai.github.io/docs/api-openai/conformance\"\u003e\u003cimg src=\"https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fraw.githubusercontent.com%2Fogx-ai%2Fogx%2Fmain%2Fdocs%2Fstatic%2Fopenai-coverage.json\u0026query=%24.summary.conformance.score\u0026suffix=%25\u0026label=OpenResponses%20Conformance\u0026color=brightgreen\" alt=\"OpenResponses Conformance\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://deepwiki.com/ogx-ai/ogx\"\u003e\u003cimg src=\"https://deepwiki.com/badge.svg\" alt=\"Ask DeepWiki\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n[**Quick Start**](https://ogx-ai.github.io/docs/getting_started/quickstart) | [**Documentation**](https://ogx-ai.github.io/docs) | [**OpenAI API Compatibility**](https://ogx-ai.github.io/docs/api-openai) | [**Discord**](https://discord.gg/ZAFjsrcw)\n\n\u003e [!IMPORTANT]\n\u003e **Llama Stack is now OGX.** The name changed, and so did the mission — model-agnostic, multi-SDK, production-grade. [Read the full announcement →](https://ogx-ai.github.io/blog/from-llama-stack-to-ogx)\n\n**Open-source agentic API server for building AI applications. OpenAI-compatible. Any model, any infrastructure.**\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"docs/static/img/architecture-animated.svg\" alt=\"OGX Architecture\" width=\"100%\"\u003e\n\u003c/p\u003e\n\nOGX is a drop-in replacement for the OpenAI API that you can run anywhere — your laptop, your datacenter, or the cloud. Use any OpenAI-compatible client or agentic framework. Swap between Llama, GPT, Gemini, Mistral, or any model without changing your application code.\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8321/v1\", api_key=\"fake\")\nresponse = client.chat.completions.create(\n    model=\"llama-3.3-70b\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n)\n```\n\n## What you get\n\n- **Chat Completions \u0026 Embeddings** — standard `/v1/chat/completions`, `/v1/completions`, and `/v1/embeddings` endpoints, compatible with any OpenAI client\n- **Responses API** — server-side agentic orchestration with tool calling, MCP server integration, and built-in file search (RAG) in a single API call ([learn more](https://ogx-ai.github.io/docs/api-openai))\n- **Vector Stores \u0026 Files** — `/v1/vector_stores` and `/v1/files` for managed document storage and search\n- **Batches** — `/v1/batches` for offline batch processing\n- **[Open Responses](https://www.openresponses.org/) conformant** — the Responses API implementation passes the Open Responses conformance test suite\n- **Multi-SDK support** — use the [Anthropic SDK](https://docs.anthropic.com/en/api/messages) (`/v1/messages`) or [Google GenAI SDK](https://ai.google.dev/gemini-api/docs/interactions) (`/v1alpha/interactions`) natively alongside the OpenAI API\n\n## Use any model, use any infrastructure\n\nOGX has a pluggable provider architecture. Develop locally with Ollama, deploy to production with vLLM, or connect to a managed service — the API stays the same.\n\nSee the [provider documentation](https://ogx-ai.github.io/docs/providers) for the full list.\n\n## Get started\n\nInstall and run a OGX server:\n\n```bash\n# One-line install\ncurl -LsSf https://github.com/ogx-ai/ogx/raw/main/scripts/install.sh | bash\n\n# Or install via uv\nuv pip install ogx[starter]\n\n# Start the server (uses the starter distribution with Ollama)\nuv run ogx stack run starter\n```\n\nThen connect with any OpenAI, Anthropic, or Google GenAI client — [Python](https://github.com/openai/openai-python), [TypeScript](https://github.com/openai/openai-node), [curl](https://platform.openai.com/docs/api-reference), or any framework that speaks these APIs.\n\nSee the [Quick Start guide](https://ogx-ai.github.io/docs/getting_started/quickstart) for detailed setup.\n\n## Resources\n\n- [Documentation](https://ogx-ai.github.io/docs) — full reference\n- [OpenAI API Compatibility](https://ogx-ai.github.io/docs/api-openai) — endpoint coverage and provider matrix\n- [Getting Started Notebook](./docs/getting_started.ipynb) — text and vision inference walkthrough\n- [Contributing](CONTRIBUTING.md) — how to contribute\n\n**Client SDKs:**\n\n|  Language |  SDK | Package |\n| :----: | :----: | :----: |\n| Python |  [ogx-client-python](https://github.com/ogx-ai/ogx-client-python) | [![PyPI version](https://img.shields.io/pypi/v/ogx_client.svg)](https://pypi.org/project/ogx_client/) |\n| TypeScript   | [ogx-client-typescript](https://github.com/ogx-ai/ogx-client-typescript) | [![NPM version](https://img.shields.io/npm/v/ogx-client.svg)](https://npmjs.org/package/ogx-client) |\n\n## Community\n\nWe hold regular community calls every Thursday at 09:00 AM PST — see the [Community Event on Discord](https://discord.gg/ZAFjsrcw) for details.\n\n[![Star History Chart](https://api.star-history.com/svg?repos=ogx-ai/ogx\u0026type=Date)](https://www.star-history.com/#ogx-ai/ogx\u0026Date)\n\nThanks to all our amazing contributors!\n\n\u003ca href=\"https://github.com/ogx-ai/ogx/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=ogx-ai/ogx\" alt=\"OGX contributors\" /\u003e\n\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fogx-ai%2Fogx","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fogx-ai%2Fogx","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fogx-ai%2Fogx/lists"}