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Proxy OpenAI/Anthropic APIs to GigaChat

Connect existing OpenAI-style or Anthropic-style clients to GigaChat with portable guidance for the gpt2giga compatibility proxy.

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Works with openaianthropic
NeuralDeep
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88
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out of 100
Updated 4 months ago
Source checked Oct 17, 2026
Version 1.0.0
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Why it matters

Seamlessly migrate your applications from OpenAI or Anthropic APIs to GigaChat without requiring any code modifications. This asset acts as a direct proxy, ensuring compatibility and simplifying your transition to a new AI platform.

Outcomes

What it gets done

01

Map OpenAI API calls to GigaChat endpoints.

02

Proxy Anthropic API requests to GigaChat.

03

Enable code-free migration between AI models.

04

Maintain existing application logic during platform transition.

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/gpt2giga | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

A portable skill for running and configuring the gpt2giga proxy to map OpenAI API and Anthropic Messages API requests to GigaChat. It treats the proxy as a compatibility layer without promising byte-for-byte API equivalence. Use it when an existing client must preserve its OpenAI-style or Anthropic-style API while connecting to GigaChat. Choose the native SDK or framework skills when those integration layers fit the task.

What it does

GPT2Giga Proxy is a portable skill for running and configuring the gpt2giga compatibility proxy. It guides work that maps OpenAI API and Anthropic Messages API requests to GigaChat. The intended use is connecting an existing application that already expects one of those API styles to GigaChat through the proxy. Compatibility is the central boundary: gpt2giga is a compatibility layer, with no promise of byte-for-byte equivalence to the OpenAI or Anthropic APIs. Treat API compatibility as a requirement to evaluate for the application rather than an assumption of identical behavior.

When to use - and when NOT to

Choose this skill when preserving the client's existing API style is part of the task. For a direct Python integration, use the official gigachat SDK skills instead. The gigachat-sdk-chat skill covers chat, streaming, message history, model selection, and token counting; gigachat-sdk-functions covers native tool and function calling. These are separate integration paths, rather than capabilities supplied by the proxy skill.

When the application is built around LangChain primitives, langchain-gigachat is the relevant route for chat models, tools, structured output, embeddings, and RAG. For Deep Agents or deepagents-code harness profiles, deepagents-gigachat addresses that environment. Use gigachat-navigation first when choosing between these layers is itself the unresolved task.

Access setup usually precedes implementation-specific work. The gigachat-setup skill handles credentials, OAuth scope selection, TLS certificates, environment variables, and connectivity troubleshooting. Route those prerequisites there before using proxy guidance. This keeps the compatibility decision separate from account access and from selecting a native SDK or framework integration.

Inputs and outputs

The reusable instruction package consists of a SKILL.md file and a references/ folder. SKILL.md supplies trigger logic, workflow, and default behavior; references/ provides detailed guidance loaded on demand. Keep the folder intact so these two parts remain together when installing or adapting it.

Copy the skill folder into the location where the coding environment loads project or team skills. There is no universal installation path across products. If the environment lacks native skill-folder support, adapt the instruction file and references into its project instruction mechanism. Repository-level instruction files, custom modes, and team-level shared prompts are among the supported adaptation patterns.

Guidance uses evidence labels: smoke-covered identifies lightweight script coverage, source-backed identifies upstream documentation or source support, caution marks advice unsafe as a default, and inference marks recommendations without a hard guarantee. For a configured local proxy, the supplied live smoke command is:

python verification/gpt2giga_openai_http.py --live --json

The live option runs against configured services; the lightweight checks otherwise avoid live API calls. Use this evidence to assess the selected integration, while keeping the skill package as reusable instructions for the coding environment.

Source README

GigaChat Skills

Portable skill folders for AI coding agents that need to write code against the GigaChat API and the GigaChain set of solutions.

This repository is intentionally tool-agnostic. Different agent products expose reusable instructions in different ways:

  • some support repository or team-level skills
  • some rely on project instruction files
  • some use custom modes, prompts, or MCP-connected workflows

This repository packages the GigaChat knowledge as self-contained skill folders so it can be adapted to any of those environments.

What is included

  • gigachat-navigation
  • gigachat-setup
  • gigachat-sdk-chat
  • gigachat-sdk-functions
  • gigachat-sdk-files-embeddings
  • langchain-gigachat
  • gpt2giga
  • deepagents-gigachat

Each folder contains:

  • SKILL.md for trigger logic, workflow, and default behavior
  • references/ for detailed guidance loaded on demand

The repository includes lightweight verification smoke scripts under verification/ for claims summarized in the feature matrix. It does not include workspace-specific state files or a full benchmark suite; the skill folders remain the reusable instruction layer.

Skills

gigachat-navigation

Choose the right integration layer:

  • native gigachat SDK
  • langchain-gigachat
  • gpt2giga
  • deepagents-gigachat

Use this first when the agent should decide which path fits the task.

gigachat-setup

Use for:

  • credentials
  • OAuth scope selection
  • TLS certificates
  • environment variables
  • connectivity troubleshooting
gigachat-sdk-chat

Use for direct Python integration with the official gigachat SDK:

  • chat
  • streaming
  • message history
  • model selection
  • token counting
gigachat-sdk-functions

Use for native tool and function calling with the official SDK:

  • function schemas
  • tool selection
  • second-turn tool result flow
  • built-in function caveats
gigachat-sdk-files-embeddings

Use for:

  • file upload and lifecycle
  • file-backed prompts
  • multimodal constraints
  • direct embeddings
langchain-gigachat

Use for LangChain-based work with GigaChat:

  • chat models
  • tools
  • structured output
  • embeddings
  • RAG
gpt2giga

Use when an existing client must keep an OpenAI-style or Anthropic-style API and talk to GigaChat through the compatibility proxy.

deepagents-gigachat

Use when building or configuring Deep Agents / deepagents-code with GigaChat harness profiles:

  • profile package installation
  • provider keys and auto-registration
  • deepagents-code model config
  • profile behavior and benchmark caveats

Verification smoke scripts

The verification/ folder contains lightweight scripts referenced by gigachat-navigation/references/feature-matrix.md. They are not a full benchmark suite. By default they avoid live API calls; pass --live when credentials or a local gpt2giga proxy are configured.

Examples:

python verification/gigachat_chat.py --live --json
python verification/langchain_chat.py --live --json
python verification/gpt2giga_openai_http.py --live --json

See verification/LIVE_TESTS.md for the latest public-safe live smoke evidence.

How to use this repository

There is no universal installation path across agent products.

Instead, use the repository in the way your agent environment expects reusable instructions to be provided:

  1. Copy one or more skill folders into the place where your agent loads project or team skills.
  2. If your tool does not support native skill folders, adapt the contents of SKILL.md and references/ into that tool's project instruction mechanism.
  3. Keep each skill folder intact so the workflow guidance and references stay together.

Adapting to different agent tools

Typical patterns across modern coding agents include:

  • repository-level instruction files
  • reusable skill or module folders
  • custom modes with tool selection and long-form instructions
  • team-level shared prompts or agent bundles

This repository maps best to tools that support reusable instruction folders directly. For tools that do not, the content is still usable as a structured source of project guidance.

Confidence labels

The skills use three confidence labels:

  • smoke-covered: covered by a lightweight script in verification/; run with --live to reproduce against configured services
  • source-backed: supported by upstream documentation, release notes, or source code
  • caution: supported by evidence but unsafe as a default recommendation
  • inference: useful recommendation, but not a hard guarantee

Important cross-skill rule

For GigaChat file workflows, keep one request limited to one file modality.

Allowed:

  • image + image
  • audio + audio
  • document + document

Avoid:

  • image + audio
  • image + pdf
  • audio + pdf

If a user task spans multiple modalities, split the work into separate requests and combine results in application code.

Notes

  • gpt2giga is documented as a compatibility layer, not as a byte-for-byte replacement for OpenAI or Anthropic APIs.
  • langchain-gigachat is for LangChain-native workflows; use the native SDK skills when LangChain is not needed.
  • deepagents-gigachat is for Deep Agents harness/profile behavior; use it only when Deep Agents or deepagents-code is in scope.
  • gigachat-setup should usually come before implementation-specific skills.

Skills in this collection

  • deepagents-gigachat: 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 wor... - SKILL.md
  • gigachat-navigation: 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 limi... - SKILL.md
  • gigachat-sdk-chat: 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... - SKILL.md
  • gigachat-sdk-files-embeddings: 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 w... - SKILL.md
  • gigachat-sdk-functions: 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 mode... - spark gigachat-sdk-functions
  • gigachat-setup: 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 S... - SKILL.md
  • gpt2giga: 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 appli... - spark gpt2giga
  • langchain-gigachat: Uses GigaChat through LangChain primitives: chat models, embeddings, message conversion, streaming, tool binding, and RAG workflows. Use when building LangChain chains, agents, or retrieval pipelin... - SKILL.md

FAQ

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