Cache OpenAI chat completion calls with memoized client utilities.
npx skills add https://github.com/anhvth/speedy_utils --skill llm-integration
Please help me install this Agent Skill. Skill: llm-integration Source: https://github.com/anhvth/speedy_utils/tree/main/.github/skills/llm-integration Command: npx skills add https://github.com/anhvth/speedy_utils --skill llm-integration
Memoized OpenAI client utilities optimize chat completion calls by caching responses to reduce API usage.
Install speedy_utils and instantiate MOpenAI to begin memoizing chat completions.
High-intent search queries and answers about installing and using this skill.
To cache OpenAI chat completion calls in Python and reduce API costs, instantiate the MOpenAI client from this Skill to automatically memoize post requests and save latency.
Yes, you can transform chat messages between ChatML and ShareGPT formats using the transform_messages utility, which also supports text and simulated transcripts for varied workflows.
To memoize OpenAI API calls, you need the speedy_utils library for caching utilities and the official openai package for API access, ensuring proper client configuration.
Yes, this memoization approach supports asynchronous OpenAI clients through the MAsyncOpenAI class, allowing you to cache API responses in non-blocking workflows.
To prepare prompts for local LLM inference from chat formats, use the transform_messages utility to convert ChatML or ShareGPT histories into plain text or simulated transcripts.
NaimalArain13Build AI agents with the OpenAI Agents SDK and MCP server integration.
Clay-HHKDetect prompt injections and jailbreak attempts in LLM applications.
EarthmanWebImplement semantic vector search with AgentDB for document retrieval.
latestaiagentsImplement provider-level, response, semantic, and template caching for LLM applications.
