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Deep Research Multi-Agent System Documentation

Based on verified PydanticAI documentation retrieved via the qdrant-docs MCP server, here's a comprehensive analysis of your deep research code:

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Overview

This code implements a programmatic multi-agent workflow pattern—one of the four complexity levels supported by PydanticAI for multi-agent applications. The system orchestrates three specialized agents to perform deep research through a plan-execute-analyze pipeline.

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TreatOrHell FastAPI Application Architecture

Component Diagram

graph TB
    subgraph "Client Layer"
        User[👤 User/Client]
        Browser[🌐 Web Browser]
    end

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AIE OnRamp Session 1 - Visual Diagrams

This document provides visual diagrams to help understand the development workflow and architecture covered in Session 1.

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1. Deployment & Implementation Architecture

This diagram shows the tools, services, and how they connect in your development pipeline.

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You are reviewing my .claude.json cleanup tooling.

Context:

• Python script: cleanup_claude_json.py (backs up ~/.claude.json, analyzes projects, and removes entries whose directories no longer exist, with a dry-run/execute flag).
• Strategy doc: CLAUDE_JSON_CLEANUP_STRATEGY.md (describes goals, risks, and a conservative cleanup process).

Tasks (be brief and concrete):

1. In 3–5 sentences, restate the core goal of this script + strategy and the main safety mechanisms (backups, dry-run, scope of deletions).
2. Evaluate the approach against best practices for:

• config/safety (backups, rollback, blast radius),

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GitHub Repository Research Prompt

You are analyzing a GitHub repository as a software architect and systems researcher.

Critical rules

1. Do not assume technologies or patterns based on the repo name, description, or my prior comments.
   • Instead, infer everything from:
     • README*, docs/, pyproject.toml / package.json, Dockerfile*, compose*, etc.
2. Separate facts from inferences:

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🚀 Executive Summary

A Spec-Driven Operating Model for Human + AI Engineering Teams Using GitHub

AI Engineering in 2025 requires more than prompting or code generation—it requires a repeatable, spec-driven system that aligns humans and AI agents on what to build and why before any code is written.

GitHub’s Spec Kit provides a lightweight, practical foundation for this: a standardized workflow that uses structured specifications to guide AI agents, reduce rework, and eliminate “vibe coding.”

This bootcamp framework extends that foundation into a three-phase operating model, helping future AI architecture & engineering leaders create teams where humans and AI work together effectively, predictably, and safely across a GitHub Organization.

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Here’s a concise analysis of the markdown‑confluence GitHub organisation (and its tooling), why it appears to have waned in activity, and alternative tools/approaches you might evaluate.

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1. What the project was

• The mono-repo at [markdown-confluence/markdown-confluence] described itself as “a collection of tools to convert and publish your Markdown files to Confluence (using Atlassian Document Format – ADF)”. ([GitHub][1])

• It included components like:

• an npm CLI (@markdown-confluence/lib) for converting Markdown → ADF. ([GitHub][1])

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🎨 Breakout Room Visual Guide

Frontend Development & Deployment Workflow

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📋 Complete Workflow Overview

graph TB
    Start([👋 Start: Breakout Room Session]) --> Phase1[💡 Phase 1: Planning & Design]

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Santosh - Assignment 15 - A2A

🧭 TL;DR Feedback – Simplifying & Aligning with A2A Client Patterns

Great job — your code works and shows deep understanding of streaming, chunk parsing, and A2A context handling. To simplify and align with the official A2A client patterns (like test_client.py), focus on these refinements:

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🔹 1. Separate Concerns

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The LangChain Provider Switching Pattern

How to Switch Between LLM Providers in 20 Lines of Code

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🎯 The Core Pattern

The essential truth about switching LLM providers in LangChain: