# Tooling Enterprise developers increasingly rely on agent orchestration frameworks to build, deploy, and manage complex LLM-powered applications. These frameworks let you: * Chain LLM calls for multi-step reasoning, * Connect to private data sources like databases and vector stores, * Monitor performance and output quality. These frameworks are flexible and model-agnostic, allowing connections to any **OpenAI-compatible** endpoint. This lets developers switch between open-source or commercial LLMs to optimize cost, performance, or capabilities. You can connect any frameworks that supports OpenAI-compatible endpoints. Here are quickstart guides to help you get started with [LangChain](/integration-examples/tooling/langchain.md) or [Langfuse](/integration-examples/tooling/langfuse.md). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.cortecs.ai/integration-examples/tooling.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.