📚 Wouldn't it be great to run ReAct, RAG, and Tree of Thoughts behind one API and compare them, instead of wrangling scattered per-paper implementations? This repo delivers exactly that, with all 35 patterns in one place. Title: FareedKhan-dev/all-agentic-architectures URL: 📦 Overview This is a Python library and a "living textbook" implementing 35 production-grade agentic AI patterns. Every architecture exposes the same .run(task) method and returns an identical result shape, so you can swap patterns without touching downstream code. ❓ Challenges Solved Agentic design patterns have been scattered across papers, each with its own implementation and conventions. The real value here is unifying them under a single interface so you can try them side by side. 💡 Core Idea & Approach The central idea is the "deterministic-picker discipline." ・Instead of handing scoring entirely to the LLM, it first has the LLM commit to categorical features like booleans and enums ・The final decision is then composed in Python logic This mitigates the flat-band pathology of LLM-as-Scorer, and it appears in 13 of the 35 architectures. 🎯 Coverage & Use Cases It spans eight families: reasoning and reflection (Reflection, Self-Discover), search (Tree of Thoughts, LATS), RAG (Corrective/Self/Adaptive/GraphRAG), memory (MemGPT, Voyager), tools and actions (ReAct, SWE-Agent), and multi-agent (Debate, STORM). Each pattern ships with an executed Jupyter notebook, giving reproducible references grounded in real LLM output. 📊 Highlights ・Built on LangGraph, with support for Nebius, OpenAI, Anthropic, Ollama and more, switchable via a single env var ・283 passing pytest tests ・On a 17-task benchmark it recently scored 33/42 correct (78%), with Reflection and Self-Consistency among the strongest #AIAgents# #LangGraph#

For an AI agent to answer "why did we make that decision?", you need connected memory — not flat chat logs 🕸️ This tool spins the whole thing up in one command. Title: Introducing Create Context Graph URL: 🕸️ Overview Create Context Graph is a Neo4j Labs CLI scaffolding tool that generates a full-stack AI agent app with graph-based memory in a single command. The generated app bundles a FastAPI backend, a Next.js frontend, an AI agent framework, and a Neo4j graph database. ❓ Challenges Solved AI agents are easy to build but still struggle with relationships and causality. ・Flat chat logs and vector stores can't answer structural questions like "why did we decide this?" or "what's blocking this work?" ・In short, agents lacked the sophisticated memory needed to capture relational context 💡 Methodology & How It Works ・It turns data into a "context graph" (a connected knowledge structure), organizing three memory types: chat history, vector content, and reasoning traces ・It uses the POLE+O entity model (Person, Organization, Location, Event, Object) layered with domain-specific types ・When agents decide, the reasoning chain is captured as DecisionTrace nodes with linked TraceStep components, creating queryable provenance ・It supports multiple frameworks (PydanticAI, LangGraph, Claude Agent SDK), 22 built-in domains, Linear/Claude Code/GitHub connectors, real-time reasoning-path visualization, and automatic secret redaction 🌍 Use Cases ・Developers querying issue dependencies and team workflows ・Personal development analytics from Claude Code session history ・Multi-tool correlation combining decisions, commits, and work items Making decision provenance queryable helps with agent explainability, debugging, and cross-team knowledge integration. #GraphRAG# #Neo4j#

The EU AI Act deadline is August 2, 2026, with penalties up to €15M. Here's a practical guide mapping abstract articles to concrete features ⚖️ Title: How LangSmith and LangChain OSS Help You Meet EU AI Act Requirements URL: ⚖️ Overview This post explains how to meet the EU AI Act's requirements for high-risk systems using LangSmith and LangChain OSS features, mapping the articles to implementations like tracing, evaluation, and human oversight. ❓ Challenges Solved The EU AI Act imposes strict requirements on high-risk AI. The deadline is August 2, 2026, with penalties up to €15M or 3% of global revenue. The hard part is knowing which capabilities actually satisfy the abstract articles. 💡 How It Works (articles → features) ・Observability and tracing (Article 12): end-to-end traces of LLM calls, tools, and reasoning steps; retention of 14 days (base) / 400 days (extended); EU data residency options ・Quality and safety (Article 15): online evaluators continuously score production traffic, with prebuilt evaluators for toxicity, hallucination, PII leakage, prompt injection, and more ・Human oversight (Article 14): LangGraph's interrupt for human-in-the-loop, with resume-from-exact-point recovery ・Risk management (Article 9): custom dashboards track risk scores and trigger alerts 🌍 Practical Starting Point Build in this order — tracing → production evaluations → human-in-the-loop — and choose EU, self-hosted, or BYOC deployment based on data residency needs. #EUAIAct# #AIGovernance#