ClickClack is a production-grade, self-hostable team chat platform built for people, bots, and agents. The backend is a single Go binary serving a REST API with WebSocket real-time events, supporting both SQLite and PostgreSQL as interchangeable storage backends. The frontend is a Svelte 5 single-page application embedded into the Go binary, with an optional Electron desktop client and a TypeScript SDK for bot development. The system provides workspaces, channels, threads, direct messages, file uploads, full-text search, reactions, pinned messages, slash commands, event subscriptions, and push notifications (Web Push and Pushover). Authentication supports magic links, passwords (Argon2id), GitHub OAuth, and Cloudflare Access JWT. The architecture emphasizes security with SSRF-safe callback dialers, strict cookie namespacing, rate limiting, and bot scope enforcement. The codebase is notable for its dual-database store pattern with shared contract tests, a real-time event hub with cursor-based recovery, and a comprehensive test suite including unit, integration, and Playwright end-to-end tests. It is not a data research project; it is a fully featured application designed for self-hosting or cloud deployment.
n8n is a production-grade workflow automation and AI agent platform. In production, it runs as a Node.js Express server with a Vue 3 frontend, executing user-defined workflows composed of nodes (triggers, actions, LLM calls). Workflows can be triggered manually, via webhooks, schedules, or events, and the execution engine handles concurrency, retries, errors, and data passing between nodes. The platform supports AI agents through an SDK that manages LLM calls, tool execution, memory, conversation loops, and MCP (Model Context Protocol) integration, enabling autonomous multi-step agents. Architecturally, the monorepo is organized into backend (packages/cli, packages/core, @n8n/engine), frontend (packages/frontend/editor-ui), database layer (@n8n/db with TypeORM), AI/agent packages (@n8n/agents, @n8n/ai-utilities, @n8n/instance-ai), and a vast node/credential library (packages/nodes-base, @n8n/nodes-langchain). Dependency injection is handled by @n8n/di, controllers are decorated with @RestController, and database models use a forked TypeORM. The system supports multiple database backends (PostgreSQL, SQLite) and can scale with Redis and worker processes. Key data flows: Workflow definitions are persisted in the database with version history. Execution data (inputs, outputs, binary data) is stored for monitoring and replay. The AI agent runtime uses a persistent message store for conversation memory, supports checkpoint/restore for long-running agents, and integrates with external LLMs (OpenAI, Anthropic, etc.) via a unified model factory. The platform exposes REST APIs for CRUD operations on workflows, credentials, users, and executions, as well as WebSocket push for real-time UI updates. External dependencies include a database, optional Redis for scaling, and numerous third-party services via nodes and credentials.
Integuru is a Python application that reverse-engineers API requests from browser traffic to generate runnable integration code. It takes a HAR file (HTTP archive) containing browser network requests and cookie data, then uses LLMs to analyze these requests to identify which endpoint performs a user-specified action. The system builds a dependency graph to determine what prerequisite requests are needed to gather dynamic parameters (like IDs, tokens), and finally generates Python code that can reproduce the action by making the necessary HTTP requests in the correct order. The architecture uses LangGraph for workflow orchestration, with specialized LLM-powered agents handling different aspects of the analysis (URL identification, dynamic parameter extraction, dependency resolution).
This repository is a curated, community-maintained 'awesome list' cataloging third-party software, plugins, frameworks, and tools that integrate with DeepSeek's API and models. It contains no application source code, data pipelines, or executable components. The repository's sole purpose is documentation and discovery: it provides a categorized index (applications, AI agent frameworks, RAG frameworks, IDE plugins, browser extensions, etc.) with links, logos, and brief descriptions. Each integration is documented in the docs/ directory with setup instructions, configuration snippets, and multilingual translations. While not a software system itself, it serves as a vital reference for developers building on DeepSeek, and the consistent per-integration documentation template is a reusable pattern for similar curations. The repository is actively maintained with frequent contributions and translations, though the value depends entirely on the listed projects remaining current and accessible.