AG2 Adaptive Research Team
A Streamlit app that blends agent teamwork with agent-enabled routing and fallback, built entirely on AG2..
Every project is indexed by its architecture pattern first — then the stack that powers it. Find AI agents, RAG pipelines, MCP servers and voice systems by how they're built, not just what they're called.
In-depth analysis of frontier model announcements, academic conference review benchmarks, and framework releases.
AAAI-27 Phase 1 results are expected on September 24. Anyone else waiting for the decision?
I built a conversational AI that doesn't generate a single token — it selects from 400 pre-written responses using TypeSafe's Jev, a non-generative model that returns probabilistic judgments...
Recently unsealed court documents in the New York Times ' case against OpenAI and Microsoft are pretty damning. The companies' own documentation warned that it was starting a "doom loop" that...
Adding MCP Tools to Reachy Mini.
Projects receiving highest aggregate builder interest across vector retrieval, FastMCP protocols, and autonomous swarms.
A Streamlit app that blends agent teamwork with agent-enabled routing and fallback, built entirely on AG2..
Learn how to build a governance layer that enforces deterministic policies on AI agents, preventing dangerous actions before they execute..
The AI Competitor Intelligence Agent Team is a powerful competitor analysis tool powered by Firecrawl and Agno's AI Agent framework. This app helps businesses analyze their competitors by extracting structured data from competitor...
A powerful business consultant powered by Google's Agent Development Kit that provides comprehensive market analysis, strategic planning, and actionable business recommendations with real-time web research.
From single-file starter agents to multi-agent swarms, hybrid vector RAG pipelines, and FastMCP servers.
Beginner-friendly, single-file agents demonstrating fundamental tool-calling and prompting patterns.
Multi-agent systems, autonomous teams, hierarchical swarms, and complex workflow orchestrations.
Retrieval Augmented Generation architectures, hybrid vector-BM25 search, reranking, and contextual retrieval.
Implementations of the Model Context Protocol (MCP) connecting agents to external servers and local tools.
Modular agent capabilities, structured reasoning protocols, reflection loops, and evaluation suites.
Conversational interfaces grounded in databases, PDFs, documents, media streams, and external APIs.
Low-latency, real-time speech-to-speech agents with streaming audio, VAD, and bidirectional WebSockets.
Agents that dynamically render interactive frontend components, artifacts, and personalized user interfaces.
Continuous background workers, proactive monitoring loops, scheduled automations, and event-driven triggers.
Stateful architectures leveraging short-term working memory, semantic episodic memory, and long-term vector stores.
Tools for prompt optimization, structured schema enforcement, response caching, and evaluation metrics.
Structured tutorials exploring OpenAI Agents SDK, Google ADK, Smolagents, LangGraph, and CrewAI.
LLM and reinforcement learning agents playing interactive games, chess, and simulation environments.
Guides and pipelines for fine-tuning open-source LLMs using LoRA, QLoRA, PEFT, and custom datasets.
Curated open-source implementations demonstrating notable patterns in agent delegation, tool calling, and retrieval.
AI-powered web scraping using ScrapeGraphAI - extract structured data from websites using natural language prompts. This agent runs locally with the open-source scrapegraphai library.
A Streamlit application that allows you to explore and analyze GitHub repositories using natural language queries through the Model Context Protocol (MCP).
A high-precision document question-answering system combining BM25 keyword matching with vector embeddings and Claude 3.5 Sonnet via RAGLite.
A powerful research assistant that leverages OpenAI's Agents SDK and Firecrawl's deep research capabilities to perform comprehensive web research on any topic and any question.
An OpenAI SDK powered customer support agent application that delivers voice-powered responses to questions about your knowledge base using OpenAI's GPT-4o and TTS capabilities. The system crawls through documentation websites with...
An agentic Retrieval-Augmented Generation system that exposes step-by-step reasoning and thought processes in real time using Agno, Gemini, and OpenAI over dynamic web sources.
Model Context Protocol tool servers & clients
Cyclic graph orchestration & multi-agent loops
Conversational agent swarms & teamwork
Interactive AI web apps & data interfaces
Dense vector retrieval & hybrid search indices
Private, on-premise local agent execution
Multi-step autonomous web investigation and synthesis
Context-aware ticket triage and automated resolution
Hybrid RAG retrieval across corporate filings and knowledge
Sub-500ms streaming speech-to-speech agent pipelines
Automated refactoring, code review, and CLI assistance
Standardized JSON-RPC tool dispatch and sandboxing
Every project is organized along a single discovery path — Projects → Architecture Patterns → Stack — from open-source GitHub extraction to visual architecture synthesis and market demand signals.
Find open-source AI projects across multiple curated sources and GitHub topic indexes.
Inspect architectural diagrams, execution patterns, technologies, and production limitations.
Evaluate state graphs vs. conversational swarms vs. FastMCP tool protocols side-by-side.
Follow weekly repository star velocity, fork growth, and emerging category shifts.
Observe aggregate developer demand signals and high-interest problem spaces.
Leverage validated open-source architecture patterns to engineer original software.
Deep-dive technical guides, protocol breakdowns, and empirical benchmark reports.
Weekly intelligence report tracking velocity across 197 open-source AI repositories. Key findings include an 84% surge in FastMCP server implementations and a shift toward hybrid dense-sparse RAG retrieval.
An architectural comparison of leading open-source AI agent frameworks in 2026. Learn when to use state graphs, multi-agent swarms, or minimal tool loops.
A practical guide to deciding between Retrieval-Augmented Generation (RAG) and model fine-tuning (LoRA/PEFT) for enterprise LLM applications.
Inspect original reference implementations inspired by common open-source architecture patterns: Hybrid Retrieval BM25 fusion, ReAct tool execution loops, and FastMCP server templates.