Model APIs, SDKs, and services that power AI products and internal tools.
Find the APIs and SDKs teams use to build and ship AI-powered products.
This category groups tools around the same problem space so you can see inputs, outputs, and control surfaces more clearly.
These are the most relevant tools in this category for quick comparison.
Use LangChain when one workflow needs to coordinate models, tools, and context.
A fast starting point for teams building AI features.
A good fit for writing, analysis, and long-context workflows.
Use it when front-end speed and developer experience matter.
Use GitHub MCP Server when an agent needs explicit GitHub context and scoped tool access.
Pipedream lets developers wire APIs and code into automation workflows quickly.
A practical checklist for docs, API, and platform teams making product documentation discoverable as skill.md, well-known skills, and MCP resources.
A practical setup guide for connecting your app to a model API without creating brittle code.
Cost problems usually start quietly. A few simple rules make them much easier to manage.
If you are still learning what AI is useful for, stay with finished apps. API choice only becomes relevant once AI has to fit inside your own system or repeat at scale.
Frameworks for orchestrating tool use, memory, planning, and multi-step agent behavior.
No-code and low-code systems for connecting apps, routing events, and shipping repeatable workflows.
Vector databases, semantic search, RAG infrastructure, and retrieval pipelines.