Chat2Graph integrates with the TuGraph graph database to deliver a conversational interface for graph data exploration. Through pre-built connectors and a prompt-engineering layer, it translates user intents into valid graph queries, handles schema discovery, suggests optimizations, and executes queries in real time. Results can be rendered as tables, JSON, or network visualizations via a web UI. Developers can customize prompt templates, integrate custom plugins, or embed Chat2Graph in Python applications. It's ideal for rapid prototyping of graph-powered applications and enables domain experts to analyze relationships in social networks, recommendation systems, and knowledge graphs without writing manual Cypher syntax.

ePH-MAPF provides an efficient pipeline for computing collision-free paths for dozens to hundreds of agents on grid-based maps. It uses prioritized heuristics, incremental search techniques, and customizable cost metrics (Manhattan, Euclidean) to balance speed and solution quality. Users can select between different heuristic functions, integrate the library into Python-based robotics systems, and benchmark performance on standard MAPF scenarios. The codebase is modular and well-documented, enabling researchers and developers to extend it for dynamic obstacles or specialized environments.

AI-Short-Video-Engine orchestrates multiple AI modules in an end-to-end pipeline to transform user-defined text prompts into polished short videos. First, the system leverages large language models to generate a storyboard and script. Next, Stable Diffusion creates scene artwork, while bark provides realistic voice narration. The engine assembles images, text overlays, and audio into a cohesive video, adding transitions and background music automatically. Its plugin-based architecture allows customization of each stage: from swapping in alternative text-to-image or TTS models to adjusting video resolution and style templates. Deployed via Docker or native Python, it offers both CLI commands and RESTful API endpoints, enabling developers to integrate AI-driven video production into existing workflows seamlessly.

Azeerc-AI is a developer-focused framework that enables rapid construction of intelligent agents by orchestrating large language model (LLM) calls, tool integrations, and memory management. It provides a plugin architecture where you can register custom tools—such as web search, data fetchers, or internal APIs—then script complex, multi-step workflows. Built-in dynamic memory lets agents remember and retrieve past interactions. With minimal boilerplate, you can spin up conversational bots or task-specific agents, customize their behavior, and deploy them in any Python environment. Its extensible design fits use cases from customer support chatbots to automated research assistants.

Claude-Code-OpenAI transforms Anthropic’s Claude API into a drop-in replacement for OpenAI models in Python applications. After installing via pip and configuring your OPENAI_API_KEY and CLAUDE_API_KEY environment variables, you can use familiar methods like openai.ChatCompletion.create(), openai.Completion.create(), or openai.Embedding.create() with Claude model names (e.g., claude-2, claude-1.3). The library intercepts calls, routes them to the corresponding Claude endpoints, and normalizes responses to match OpenAI’s data structures. It supports real-time streaming, rich parameter mapping, error handling, and prompt templating. This allows teams to experiment with Claude and GPT models interchangeably without refactoring code, enabling rapid prototyping for chatbots, content generation, semantic search, and hybrid LLM workflows.

As a flexible Python library, LangChain-Taiga connects Taiga's RESTful API to the LangChain framework, creating an AI agent capable of understanding human language instructions to manage projects. Users can ask to list active user stories, prioritize backlog items, modify task details, and generate sprint summary reports all through natural language. It supports multiple LLM providers, customizable prompt templates, and can export results in various formats such as JSON or markdown. Developers and agile teams can integrate LangChain-Taiga into CI/CD pipelines, chatbots, or web dashboards. The modular design allows extension for custom workflows including automated status notifications, estimation predictions, and real-time collaboration insights.