AI SDKs

openai-python31.3k★

This is the official SDK. It's the library that turns API calls into clean Python code. Instead of writing raw HTTP requests, you call `client.chat.completions.create` and get structured responses back. What's free: The library itself is free. MIT license (technically Apache 2.0). Install with `pip install openai` and you're coding in 30 seconds. The library costs nothing. The SDK is well-designed. Typed responses, async support, streaming, function calling, vision. Every API feature is available with clean Python interfaces. Actively maintained, usually updated within days of new API features launching. The catch: the library is free but OpenAI's API is not. GPT-4o runs $2.50-10/million tokens depending on the model. A chatbot handling 1,000 conversations/day could cost $50-500/mo easily. And this SDK only works with OpenAI's API. If you want to switch providers later, you need to refactor. Consider using the OpenAI-compatible API format that many providers support, or a framework like LangChain that abstracts the provider.

python-genai3.9k★

python-genai is Google's official Python SDK for talking to Gemini models. One pip install, one API key, and you are making calls to Gemini 2.5 Flash, Pro, and the rest of the lineup. It supports both the free Gemini Developer API and enterprise Vertex AI deployments with the same client interface. The SDK covers text generation, image generation, file uploads, function calling, and async/sync clients. Setup is minimal: "pip install google-genai" and set your API key. Compared to OpenAI's Python SDK, the developer experience is similar, but Google's free tier is more generous. You get a real free tier for Gemini Flash that handles most prototyping and small-scale production. If you are building AI features and not married to OpenAI, this is worth evaluating. Gemini's context windows are massive (up to 1M tokens on some models), and the pricing on Flash is aggressive. The SDK itself is clean and well-documented. For teams already on Google Cloud, the Vertex AI path gives you enterprise controls without changing your code. The catch: the SDK is young and Google has a history of deprecating developer products. The ecosystem of third-party tools, tutorials, and community support is still smaller than OpenAI's. You are betting on Google's commitment to this API surface.

anthropic-sdk-python3.8k★

The Anthropic Python SDK is the official way to build with Claude: agents, content pipelines, code assistants. It handles authentication, streaming, tool use, message formatting, and all the API plumbing so you write application logic instead of HTTP requests. It's free to install and use. The SDK itself costs nothing. You pay for the Claude API calls you make through it. Streaming, tool use, vision, and batch processing are all supported out of the box. Type hints are excellent, which matters when you're building complex agent flows. The catch: this is the SDK, not the API. Your costs come from Anthropic's API pricing. Claude 3.5 Sonnet runs about $3/$15 per million input/output tokens. The SDK is tightly coupled to Anthropic's models. If you want multi-provider support, you'll need something like LangChain or LiteLLM on top. And breaking changes between SDK versions happen, so pin your version.

pymilvus1.4k★

Pymilvus is the Python SDK for Milvus, a vector database purpose-built for that. Vector databases store data as mathematical representations (embeddings) so your app can find things by similarity rather than exact keyword matches. This is specifically the Python client library, not the database itself. You use it to connect to a Milvus instance, insert vectors, build indexes, and run similarity searches. The API covers everything: collection management, partitioning, hybrid search (combining vector similarity with traditional filters), and bulk data operations. pymilvus is free under Apache 2.0. Milvus itself is also free to self-host. Zilliz Cloud (the managed version from the Milvus creators) has a free tier with 2 collections and 1M vectors, then starts at ~$65/mo for production workloads. The catch: this is a client SDK, not a standalone tool. You need a running Milvus instance to connect to, and Milvus has real operational complexity. It requires etcd, MinIO, and message queues in production. If you just want to experiment with vector search, Chroma is dramatically simpler to get started with. For production vector search without the ops headache, Qdrant or Pinecone's managed service are worth evaluating.