Nine specialized agents coordinate across the Open Biosciences platform. Each agent owns specific repos and has clear responsibilities.
| Field | Value |
|---|
| import os | |
| import requests | |
| import asyncio | |
| import nest_asyncio | |
| from typing import Annotated, Literal | |
| from typing_extensions import TypedDict | |
| from dotenv import load_dotenv | |
| # LangChain & LangGraph Imports | |
| from langchain_openai import ChatOpenAI |
| { | |
| "folders": [ | |
| { | |
| "name": "🏗️ architecture", | |
| "path": "biosciences-architecture" | |
| }, | |
| { | |
| "name": "🤖 deepagents", | |
| "path": "biosciences-deepagents" | |
| }, |
| # # 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 |
❯ /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.
This guide documents how to deploy Deep Agents to LangSmith cloud with a focus on workspace/file lifecycle - a critical topic for production deployments.
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."
based on running test script from https://github.com/donbr/agent-memory
uv run python -m agent_memoryAgent Memory - All Demos| 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 |
The Objective: Your video is an exercise in Developer Advocacy. It serves to prove two things:
Multiple AI agents (Claude Desktop, Claude Code, future agents) share write access to a Graphiti knowledge graph without coordinated protocols. This has resulted in:
graphiti_meta_knowledge vs intended 8-10.