Orqen

Orqen acts as an optimization layer for language model interactions, designed to reduce operational costs and improve response accuracy without requiring changes to existing application code. Key features include: * Dynamic chat history compression * Intelligent tool schema filtering * Trimming of large tool outputs * Circuit breaking for agent loops This platform positions itself between your application and various language model providers, such as OpenAI, Anthropic, Bedrock, and Gemini. It analyzes each request to identify and remove unnecessary context, including redundant tools, schemas, and stale history. By sending only the essential information to the language model, Orqen significantly reduces the 'input token tax,' leading to substantial cost savings and more focused model responses. Orqen is ideal for engineering teams and developers whose language model expenditures are increasing, and who seek a direct way to optimize these costs. It's particularly beneficial for applications involving complex agents that frequently generate verbose outputs or carry extensive conversational histories. The system operates as a native proxy, ensuring minimal overhead (around 300ms) and maintaining the integrity of critical data points like IDs, URLs, and constraints. Integration is straightforward, often requiring just a few lines of code to implement. This allows teams to continue using their preferred SDKs (e.g., Anthropic SDK, OpenAI SDK) while gaining immediate efficiency improvements. Orqen also incorporates automatic provider caching and intelligent model routing to further enhance performance and cost-effectiveness.