A curated index of the tools developers actually use to build with AI. No models — just the tooling around them: frameworks, vector databases, inference, observability, agents and more.
Run models on your laptop, serve open models at production throughput, or route requests across many providers through one API. These tools cover local runtimes, inference engines and gateways.
Run open models on your own machine with one command.
Ollama bundles model weights, a runtime and an OpenAI-compatible REST API behind a single CLI. It is the easiest way to develop against local models — no API keys, no rate limits, full privacy — which makes it a favorite for local development and testing.
High-throughput inference server for production.
vLLM is the go-to engine for serving open models at scale. PagedAttention and continuous batching deliver many times the throughput of naive serving, behind a drop-in OpenAI-compatible API. Used across the industry for self-hosted inference.
One API client and proxy for 100+ providers.
LiteLLM translates the OpenAI completion format to every major provider, so switching models is a one-line change instead of a rewrite. Its proxy server adds centralized API keys, budgets, rate limits and spend tracking for your whole team.
A single account and API for every model.
OpenRouter routes requests to OpenAI, Anthropic, Google, Meta, Mistral and hundreds of other models behind one OpenAI-compatible API, with unified pay-as-you-go billing and automatic fallback between providers when one is down.
LLM frameworks & SDKs →Vector databases →AI coding tools →LLM observability & evals →Data & agent tooling →
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