---
type: entity
subtype: tool
title: "LangGraph"
description: "The orchestration framework wiring agents into a stateful graph of nodes and edges, used here to run agents in parallel and add human-in-the-loop."
resource: "https://www.langchain.com/langgraph"
tags: [agent-framework, orchestration, graphs, multi-agent, state-machine]
videos: [everything-you-thought-about-building-ai-agents-is-wrong, the-only-ai-tech-stack-you-need-in-2026, should-i-build-my-ai-agents-with-n8n-or-python, introducing-archon-the-revolutionary-operating-system-for-ai-coding, why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale, how-id-learn-ai-agents-fast-if-i-had-to-start-over-full-roadmap, ag-ui-just-released-the-new-wave-of-ai-agent-apps, google-anthropic-and-openais-guides-to-ai-agents-all-in-18-minutes, give-me-28-minutes-and-ill-completely-change-the-way-you-build-ai-agents, turn-any-website-into-llm-knowledge-in-seconds-evolved, build-an-army-of-ai-agents-on-autopilot-with-archon-heres-how, 10x-your-ai-agents-with-this-one-agent-architecture, openais-brand-new-agents-sdk-crash-course, introducing-archon-an-ai-agent-that-builds-ai-agents, coding-subagents-the-next-evolution-of-ai-ides, this-is-hands-down-the-best-way-to-build-ai-agents, how-to-dominate-with-ai-in-2025, revealing-my-complete-ai-agent-blueprint, qwen-just-casually-started-the-local-ai-revolution, llama-32-is-insane-but-does-it-beat-gpt-as-an-ai-agent, complete-guide-to-langserve-make-your-ai-agent-actually-useful, i-found-the-limits-of-the-most-popular-llms, the-most-profitable-ai-niche-no-one-is-talking-about, the-best-tool-for-ai-agent-workflows-langgraph-full-guide, the-only-ai-developer-roadmap-you-need-in-2024]
created: 2026-07-21
updated: 2026-07-21
---

# LangGraph

LangGraph is the framework Cole reaches for when a single agent is not enough and agents need to be wired together into a controllable workflow: "LangGraph is a library for building stateful multiactor applications with llms used to create agents and multi-agent workflows" [0:01:30]. Where Pydantic AI builds the individual agent, LangGraph orchestrates them: "Pydantic AI is how I create my individual agents. And then LangGraph is how I connect them together" [0:07:51]. The two pair so well because both treat agents as graphs, "Frameworks like Pydantic AI and LangGraph that literally focus on agents as graphs. Like that is the abstraction that they have" [0:28:03].

Its structure mirrors the same three-part discipline Cole applies to Pydantic AI: "just like with Pydantic AI you can think of LangGraph implementations as being three distinct parts" [0:20:56], the graph, the shared state, and the nodes-and-edges wiring. The payoff is explicit control over flow that a plain agent loop hides. Routing functions decide branches, and returning a list of nodes fans work out concurrently: "when we return a list in a router function like this in LangGraph that means that we're going to execute all of these nodes in parallel" [0:34:07], which is how he does [parallel agent execution](../../concepts/parallel-agent-execution.md). Because the graph is a compiled runnable, it visualizes and serves cleanly: "one of the beauties of using LangGraph is you can visualize your workflows without having to put in any extra work" [0:18:49], and LangServe wraps it as an API. LangGraph also carries strong long-term [memory](../../concepts/memory-systems.md) and [human-in-the-loop](../../concepts/human-in-the-loop.md) primitives, the latter being "something that LangGraph does so well" [0:29:03].

Cole values it for real low-level control rather than hand-holding: "it's an expressive and customizable agent workflow Builder and the very important thing with LangGraph here is they have lowlevel abstractions" [0:03:35]. It is one of the few tools he is confident is durable, "some tools like LangGraph is one good example that I definitely don't think is going anywhere" [0:20:30], and it powers Archon's own coding-agent workflow graph.

## Realizes

- [Graph-Based Agent Workflows](../../concepts/langgraph-nodes-edges-state.md) - Model an agent as a directed graph of nodes, edges, and shared state (LangGraph) with routing between steps, giving explicit control over branching, loops, and serving of complex agent workflows.
- [Agent Graphs](../../concepts/agent-graphs.md) - Modeling agents as stateful graphs of nodes and conditional edges with persisted checkpoints, as in LangGraph.
- [Agentic Workflow Engineering](../../concepts/agentic-workflow-engineering.md) - Designing repeatable AI workflows as engineered systems with defined steps, orchestration, and background execution rather than ad-hoc prompting.
- [Parallel Agent Execution](../../concepts/parallel-agent-execution.md) - Running multiple agents (or agent steps) concurrently to cut wall-clock time on a task.
- [Human in the Loop](../../concepts/human-in-the-loop.md) - Keeping a human as the approver and steerer of agentic work rather than fully automating, so AI augments engineers instead of replacing them.

## Works with

- [Pydantic AI](./pydantic-ai.md) - Cole's framework for the individual agents, structured around three parts: dependencies, the agent definition, and tools.
- [LangChain](./langchain.md) - The framework used in prior masterclass videos to build the Asana agent; its message objects and invoke/stream methods underpin the chatbot logic.
- [LangChain Deep Agents](./langchain-deep-agents.md) - LangChain's harness implementation for long-running (not just coding) agents that manages context by offloading to file-system tools.
- [LangServe](./langserve.md) - LangChain's library that wraps any chain or LangGraph runnable in a FastAPI web server via a single add_routes call, plus a RemoteRunnable client.
- [Archon](./archon.md) - Cole's free, open-source AI command center and harness builder for AI coding; started as an AI agent that builds other AI agents.
- [Claude 3.5 Sonnet](./claude-3-5-sonnet.md) - Anthropic's mid-2024 flagship model and the only LLM Cole could not break in his agentic stress test, making it his clear winner for multi-step tool-using workflows.

## Contrasts with

- [OpenAI Agents SDK](./openai-agents-sdk.md) - OpenAI's agent framework, which their guide leans on heavily with code examples.
- [CrewAI](./crewai.md) - Multi-agent framework named as one of the interchangeable backends AG-UI supports.
- [smolagents](./smolagents.md) - Hugging Face's minimal agent framework whose base agent executes actions through generated code, used here to build an entire agentic RAG workflow in very little code.

## Related

- [Memory Systems](../../concepts/memory-systems.md) - Giving agents durable short- and long-term memory so conversations, user context, and facts persist across sessions and users.
- [Subagents Pattern](../../concepts/subagents-pattern.md) - Delegating scoped tasks to isolated subagents to parallelize work and protect the main agent's context window.
- [Model Selection](../../concepts/model-selection.md) - Choosing the right LLM per task by trading off quality, speed, and cost, often routing easy requests to cheap models and hard ones to expensive models.

## Sources

- [THE BEST Tool for AI Agent Workflows - LangGraph FULL Guide](../../sources/the-best-tool-for-ai-agent-workflows-langgraph-full-guide.md) - "[0:01:30] LangGraph is a library for building stateful multiactor applications with llms used to create agents and multi-agent workflows"
- [The ONLY AI Tech Stack You Need in 2026](../../sources/the-only-ai-tech-stack-you-need-in-2026.md) - "[0:07:51] for multi-agents specifically, I love using LangGraph... Pydantic AI is how I create my individual agents. And then LangGraph is how I connect them together"
- [10x Your AI Agents with this ONE Agent Architecture](../../sources/10x-your-ai-agents-with-this-one-agent-architecture.md) - "[0:34:07] when we return a list in a router function like this in LangGraph that means that we're going to execute all of these nodes in parallel"
- [This is Hands Down the BEST Way to Build AI Agents](../../sources/this-is-hands-down-the-best-way-to-build-ai-agents.md) - "[0:03:35] it's an expressive and customizable agent workflow Builder and the very important thing with LangGraph here is they have lowlevel abstractions"
- [Give Me 28 Minutes and I'll Completely Change the Way You Build AI Agents](../../sources/give-me-28-minutes-and-ill-completely-change-the-way-you-build-ai-agents.md) - "[0:28:03] Frameworks like Pydantic AI and LangGraph that literally focus on agents as graphs. Like that is the abstraction that they have."
- [OpenAI's BRAND NEW Agents SDK (Crash Course)](../../sources/openais-brand-new-agents-sdk-crash-course.md) - "[0:29:03] human in the loop and that's something that LangGraph does so well"
- [Why is Everyone Missing This with AI Agents?! (Memory + Tools that Scale)](../../sources/why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale.md) - "[0:01:52] We'll be using LangGraph, which is one of my favorite agentic frameworks. and they have some awesome features for long-term memory that we'll be using."
