gpt4free (g4f) is a large-scale, community-driven Python library that aggregates dozens of free AI providers (LLMs, image, audio, video) into a unified, OpenAI-compatible interface. It provides multiple access modes: a Python client library (sync/async), a FastAPI-based API server, a Flask web GUI, a CLI tool, a Model Context Protocol (MCP) server for tool calling, a browser extension, a Discord bot, an Android app, and a Go launcher. The architecture is provider-based: each AI service (e.g., ChatGPT, Gemini, DeepSeek, Copilot, Grok, Perplexity) is wrapped in an adapter class implementing a common `AsyncGeneratorProvider` interface. The system handles authentication via cookies, OAuth flows, HAR files, and browser automation (CDP/nodriver), and supports streaming, tool/function calling, image generation, audio synthesis, and web search. It is primarily an application for consuming free AI models, not a research or data pipeline project, though it includes some data-oriented utilities like token optimization and usage tracking.
DS2API is a production-grade API proxy/gateway that translates DeepSeek's web chat API into OpenAI, Claude, Gemini, and Ollama-compatible endpoints. It provides a full-featured web UI for administration, account management, proxy configuration, and API testing. The system handles authentication (JWT, API keys, managed accounts), streaming responses with tool call detection, and multi-protocol request normalization. Architecturally, it is a Go backend with a React frontend (Vite + Tailwind). The backend is modular: protocol-specific handlers (OpenAI chat/responses, Claude messages, Gemini generate, Ollama chat) share a common prompt compatibility layer that normalizes requests to DeepSeek's internal format. A custom DeepSeek client manages authentication, PoW challenges, session lifecycle, and streaming. The SSE parsing infrastructure, tool call sieve, account pool, and config store are reusable components with extensive test coverage. Key data flows: incoming API requests are parsed and normalized to a unified prompt representation (role-tagged messages, tool descriptions, file references). The prompt is sent to DeepSeek's web chat API via a streaming or non-streaming completion runtime. Responses are parsed from SSE chunks, transformed (thinking extraction, tool call normalization, citation handling), and streamed back in the original protocol format. The system supports retry logic, account switching, and current input file injection for large contexts. External dependencies include DeepSeek's web chat API (reverse-engineered), Go standard library, chi router, utls for TLS fingerprinting, Node.js for Vercel streaming and WebUI build, and React/Vite for the frontend. The project is actively maintained with CI/CD pipelines, Docker images, and Vercel deployment support.
DeepSeek Monitor Windows is a Tauri 2 desktop application for monitoring DeepSeek API usage on Windows. It provides a React/TypeScript frontend with a Rust backend, offering dashboard views for account balance, monthly spending, per-model token usage (V4 Flash and Pro), and a 7-day consumption trend chart. The application stores API keys and usage tokens in a local config file and supports automatic refresh via system tray integration. It is a self-contained monolith that communicates with DeepSeek's official balance endpoint and internal platform usage API through the Rust backend. The frontend manages state via React hooks and uses lucide-react for icons. The architecture is clean with clear separation between IPC commands (in lib.rs) and UI components (in main.tsx). No data science or research components are present.