Build and deploy enterprise-grade conversational agents with integrated RAG pipelines, workflow orchestration, multi-modal IO, and model-agnostic integrations (private and public LLMs). Designed for self-hosted production with vector stores and tooling integrations.
Agent framework for building tool-using applications on Qwen 3+ LLMs. Provides function calling, MCP, a Dockerized code interpreter, and RAG over documents up to 1M tokens; powers the Qwen Chat backend and a Chrome browser-assistant extension.
Awesome LLM Apps is a curated open-source repository collecting awesome LLM applications built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more, using models from OpenAI, Anthropic, Gemini, xAI, and open-source alternatives like Qwen or Llama that can run locally.
Self-hosted browser chat interface for interacting with local or remote LLMs. Supports multiple backends (Ollama, OpenAI-compatible endpoints, llama.cpp), RAG/document chat, plugins/actions, and Docker-based deployment — aimed at teams that need private, customizable LLM UIs.
AI Engineering Hub is a comprehensive GitHub repository offering in-depth tutorials and 93+ production-ready projects on LLMs, RAGs, AI agents, and real-world AI applications for all skill levels.
Turns any website into structured data or an API without code: record clicks once to capture lists and tables, or describe fields in plain language for AI extraction. Also crawls full sites, scrapes pages to Markdown, and runs filtered searches.
Routes LLM and agent decisions through semantic similarity instead of waiting for full generations, useful for intent routing, tool selection, guardrails, and multimodal handling.
Generates interactive codebase wikis from GitHub, GitLab, or Bitbucket repositories by analyzing structure, writing documentation, and creating diagrams for navigation.
Organizes reusable AI prompts as Markdown 'Patterns' you run from the CLI — summarize a video, extract claims, rate content. Switch among 20+ providers (OpenAI, Claude, Gemini, Ollama) and reach them via CLI, web UI, or REST API.
Lets AI agents place and answer business phone calls, holding spoken conversations to collect structured data, answer questions, and escalate to humans. Built on Azure Communication Services and Azure OpenAI, with RAG over your own documents.
Python framework for building and serving LLM agents in production: a unified event bus for real-time frontends and human-in-the-loop, fine-grained tool permissions, multi-tenant serving, and tool/code execution sandboxed via Docker or E2B.
Curated developer resources that demonstrate building RAG systems, multi-agent workflows, and memory-augmented AI using Oracle AI Database and OCI — includes end-to-end reference apps, notebooks, guides, and workshops for hands-on prototyping.