---
name: gigachat-navigation
description: >-
  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.
---

# GigaChat Navigation

Use this skill first when the user is unsure which GigaChat integration layer fits the task.

## What this skill covers

- choosing between `gigachat`, `langchain-gigachat`, `gpt2giga`, and `deepagents-gigachat`
- mapping user intent to the right skill
- understanding smoke-covered capabilities and limitations
- choosing the right implementation skill next

## Workflow

1. Classify the task by integration style, not by API buzzwords.
2. If the user is writing a native Python integration, prefer `gigachat`.
3. If the app already uses LangChain primitives, use `langchain-gigachat`.
4. If the client must keep an OpenAI-compatible or Anthropic-compatible SDK, use `gpt2giga`.
5. If the app is a Deep Agents or `deepagents-code` harness using GigaChat, use `deepagents-gigachat`.
6. If more than one layer could work, choose the simplest source-backed path.
7. For files, keep one request limited to one modality.
8. Prefer the simplest smoke-covered or source-backed path described in the relevant skill and references.

## Read these references as needed

- For routing logic: `references/decision-rules.md`
- For smoke-covered feature coverage: `references/feature-matrix.md`

## Default output

- name the recommended layer explicitly
- state why the other layers are not the default
- point to the next implementation skill or reference file

## Boundaries

- This skill helps choose and navigate.
- It does not replace the implementation skills for SDK chat, files, LangChain, or proxy work.