gigachat-skills

Uses the public deepagents-gigachat HarnessProfile package to run Deep Agents or deepagents-code with GigaChat-specific prompts, tool descriptions, and middleware. Use when building Deep Agents workflows on top of GigaChat or when configuring model/provider profiles for deepagents.

Routes work across the GigaChat ecosystem: official gigachat SDK, langchain-gigachat, gpt2giga, and deepagents-gigachat. Use when deciding which layer to choose, how capabilities differ, which limitations are smoke-covered or source-backed, and which skill or reference path to use next.

Uses the official GigaChat Python SDK for text generation: sync and async chat, streaming, message history, model choice, session caching, and token counting. Use when writing Python code directly against the gigachat package without LangChain or proxy compatibility layers.

Uses the official GigaChat Python SDK for file storage, multimodal inputs, and text embeddings. Use when uploading documents or images, downloading generated assets, or building retrieval systems with embeddings directly on top of the gigachat package.

Uses function calling with the official GigaChat Python SDK: custom functions, built-in functions like text2image, function_call modes, response parsing, and returning tool results back to the model. Use when building agents or tool-use flows directly on top of gigachat.

Configures access to GigaChat API: registration, authorization key, OAuth scope, SSL certificates, environment variables, and common connectivity failures. Use when setting up GigaChat access for SDK, LangChain, or gpt2giga, or when fixing auth, TLS, or scope issues.

Runs and configures the gpt2giga proxy that maps OpenAI API and Anthropic Messages API requests to GigaChat. Use when plugging GigaChat into existing OpenAI-compatible or Anthropic-compatible applications without rewriting the client integration.

Uses GigaChat through LangChain primitives: chat models, embeddings, message conversion, streaming, tool binding, and RAG workflows. Use when building LangChain chains, agents, or retrieval pipelines on top of GigaChat instead of calling the raw SDK directly.