8 Best OpenAI Python Alternatives in 2026 (Open Source)
OpenAI Python — The official Python library for the OpenAI API.
These 8 open-source tools do the same job. They are ordered by how closely they match OpenAI Python, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| OpenAI Python(original) | 31.7k | +221 | 2026-09-30 |
| OpenLM | 368 | +-0 | 2023-05-19 |
| LiteLLM | 59.9k | +3,007 | 2026-09-30 |
| Go OpenAI | 10.8k | +28 | 2026-09-29 |
| OpenLLM | 12.5k | +53 | 2026-05-29 |
| Ollama | 182.0k | +2,511 | 2026-09-30 |
| Text Generation Inference | 10.9k | +12 | 2026-03-21 |
| BentoML | 8.9k | +52 | 2026-09-07 |
| Astra Assistant API | 207 | +-0 | 2025-08-18 |
1. OpenLM
OpenAI-compatible Python client that can call any LLM
What sets it apart: vs LiteLLM / AI SDK: minimalist OpenAI-compatible drop-in replacement — swap openlm for openai in imports and instantly access HuggingFace and Cohere with zero API changes
Best for: Switching between LLM providers without code changes; Multi-model comparison using OpenAI-compatible interface; Lightweight provider abstraction for Python projects
2. LiteLLM
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropi
What sets it apart: Unlike OpenRouter (hosted-only routing), LiteLLM is self-hostable and provides a full gateway with per-user spend tracking, virtual keys, and A2A/MCP protocol support — making it the enterprise LLM traffic controller
Best for: ML platform teams managing multi-provider LLM access with centralized cost tracking and auth; Developers switching between LLM providers without changing application code
3. Go OpenAI
OpenAI ChatGPT, GPT-5, GPT-Image-1, Whisper API clients for Go
What sets it apart: vs official OpenAI SDKs: the most popular Go client for OpenAI — comprehensive coverage of all API endpoints (chat, images, audio, embeddings) with idiomatic Go interfaces and streaming support
Best for: Go developers needing OpenAI API access in their applications; Backend services requiring typed OpenAI client in Go; Streaming chat implementations in Go-based systems
4. OpenLLM
Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
What sets it apart: Unlike Ollama which focuses on local/desktop usage, OpenLLM bridges local development and cloud production through unified BentoML tooling — providing the same CLI workflow from laptop to Kubernetes cluster with OpenAI API compatibility
Best for: Teams wanting the fastest path from model selection to OpenAI-compatible API endpoint; DevOps engineers deploying open-source LLMs to production with Docker/Kubernetes
5. Ollama
Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
What sets it apart: Unlike vLLM (production server focus) or LM Studio (GUI-first), Ollama is the simplest CLI-first tool for running local LLMs with one-command setup, an OpenAI-compatible API, and the largest ecosystem of 100+ community integrations.
Best for: Developers who want to run open-source LLMs locally with zero configuration; Privacy-sensitive use cases requiring fully offline LLM inference
6. Text Generation Inference
Large Language Model Text Generation Inference
What sets it apart: Battle-tested in production at Hugging Face (powers HuggingChat and Inference API) — now in maintenance mode with recommendation to use vLLM/SGLang, but remains the reference implementation for optimized LLM serving with the broadest hardware support
Best for: Production LLM serving with HuggingFace models at scale; Teams needing OpenAI-compatible API for open-source models
7. BentoML
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
What sets it apart: Unified model serving framework with Bento packaging — turn any model into a production API with automatic Docker, adaptive batching, and multi-model orchestration
Best for: Teams deploying ML/AI models as production APIs; Applications needing dynamic batching and GPU optimization; Multi-model inference pipelines (LLM + embedding + reranker)
8. Astra Assistant API
Drop in replacement for the OpenAI Assistants API
What sets it apart: Drop-in OpenAI Assistants API v2 replacement supporting 30+ LLM providers via LiteLLM, backed by AstraDB vector storage
Best for: openai-assistant-api-with-alternative-llms; multi-provider-assistant-apps; astradb-users