Comprehensive 回應快取 Tools for Every Need

Get access to 回應快取 solutions that address multiple requirements. One-stop resources for streamlined workflows.

回應快取

  • Steel is a production-ready framework for LLM agents, offering memory, tools integration, caching, and observability for apps.
    0
    0
    What is Steel?
    Steel is a developer-centric framework designed to accelerate the creation and operation of LLM-powered agents in production environments. It offers provider-agnostic connectors for major model APIs, an in-memory and persistent memory store, built-in tool invocation patterns, automatic caching of responses, and detailed tracing for observability. Developers can define complex agent workflows, integrate custom tools (e.g., search, database queries, and external APIs), and handle streaming outputs. Steel abstracts the complexity of orchestration, allowing teams to focus on business logic and rapidly iterate on AI-driven applications.
  • GAMA Genstar Plugin integrates generative AI models into GAMA simulations for automatic agent behavior and scenario generation.
    0
    0
    What is GAMA Genstar Plugin?
    GAMA Genstar Plugin adds generative AI capabilities to the GAMA platform by providing connectors to OpenAI, local LLMs, and custom model endpoints. Users define prompts and pipelines in GAML to generate agent decisions, environment descriptions, or scenario parameters on the fly. The plugin supports synchronous and asynchronous API calls, caching of responses, and parameter tuning. It simplifies the integration of natural language models into large-scale simulations, reducing manual scripting and fostering richer, adaptive agent behaviors.
  • An HTTP proxy for AI agent API calls enabling streaming, caching, logging, and customizable request parameters.
    0
    0
    What is MCP Agent Proxy?
    MCP Agent Proxy acts as a middleware service between your applications and the OpenAI API. It transparently forwards ChatCompletion and Embedding calls, handles streaming responses to clients, caches results to improve performance and reduce costs, logs request and response metadata for debugging, and allows on-the-fly customization of API parameters. Developers can integrate it into existing agent frameworks to simplify multi-channel processing and maintain a single managed endpoint for all AI interactions.
Featured