donbr

❯ /lifesciences-graph-builder What known genes or pathways are implicated in minimizing Doxorubicin toxicity
while preserving its anti-tumor efficacy?

● I'll execute the Fuzzy-to-Fact protocol to answer this question about Doxorubicin toxicity mitigation. Let me start by loading the necessary MCP tools and beginning Phase 1 ANCHOR.

● Now let me load additional tools I'll need for the full protocol.

● Tools loaded. Starting the Fuzzy-to-Fact protocol now.

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."

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

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

AI Makerspace Video Deliverable Guide (2026 Edition)

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

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

1. The Setup: Anchor with a Diagram

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.

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:

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.

TreatOrHell FastAPI Application Architecture

Component Diagram

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

AIE OnRamp Session 1 - Visual Diagrams

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

1. Deployment & Implementation Architecture

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

	# # Advanced Retrieval with LangChain

	# Standard Library Imports
	import getpass
	import os
	from operator import itemgetter
	from uuid import uuid4

	# Third-Party Imports
	from dotenv import load_dotenv