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Deep Agents Cloud Deployment Guide

This guide documents how to deploy Deep Agents to LangSmith cloud with a focus on workspace/file lifecycle - a critical topic for production deployments.

Overview

Deep Agents CAN be deployed to LangSmith cloud. From the official LangChain documentation:

"Deep agents applications can be deployed via LangSmith Deployment and monitored with LangSmith Observability."

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Agent Memory - All demos

based on running test script from https://github.com/donbr/agent-memory

uv run python -m agent_memory

Agent Memory - All Demos

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LangGraph memory mechanisms (mechanism × persistence)

1. Execution / short-term memory (checkpointers)

Mechanism	Purpose	In-memory / local	DB-persisted	Notes
MemorySaver	Persist graph state per step (thread continuity)	✅ Yes	❌ No	Notebook/dev only; lost on process restart
PostgresSaver	Durable execution state	❌ No	✅ Postgres	Required for resumable workflows, HITL, failures
(others)	(Future / custom savers)	—	—	Checkpointer interface is pluggable

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AI Makerspace Video Deliverable Guide (2026 Edition)

The Objective: Your video is an exercise in Developer Advocacy. It serves to prove two things:

1. Functionality: You actually built the application.
2. Grokking: You deeply understand the concepts and can teach them to a teammate.

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1. The Setup: Anchor with a Diagram

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Multi-Agent Graph Coordination Architecture

Problem Statement

Multiple AI agents (Claude Desktop, Claude Code, future agents) share write access to a Graphiti knowledge graph without coordinated protocols. This has resulted in:

• Namespace pollution: 97 episodes in graphiti_meta_knowledge vs intended 8-10.
• Duplicate episodes: Lack of idempotency checks.
• Inconsistent content categorization: Different agents, different rules.
• Loss of namespace semantic integrity.

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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: