π We just built our first real-time implementation of Graph Engineering, inspired by our experience building graph tooling used by 4,000+ developers.
Summary
GraphArc is a new open-source tool that transforms AI agent workflows into interactive, real-time graphs, enabling developers to visualize, inspect, debug, and control agent execution before approving actions.
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AI agents have never been so explainable until now, with GraphARC!
GraphArc is an open-source tool that visualizes AI agent workflows as interactive, real-time graphs, enabling users to inspect, debug, and approve agent actions before execution to make agentic AI more explainable and controllable.
Graph engineering ? Or we can say agents on steroids....
Introduces GraphARC, an MIT-licensed open-source tool that lets a model author agent graph topologies at runtime, with a deterministic admission gate for auditable execution, built on LangGraph and running locally via ollama or against cloud APIs.
@imryven: Graph Engineering became the default way every serious team builds agents now, and here's what people have already shipβ¦
Graph engineering has become a standard method for building AI agents, with frameworks like LangGraph, CrewAI, and AutoGen highlighted for their use in production by companies such as Uber and LinkedIn.
@akshay_pachaar: https://x.com/akshay_pachaar/status/2081089131808243999
Graph engineering is a new term for coordinating multiple AI agent loops using graphs of nodes (work units) and edges (control flow). The article explains the concept, its historical context (LangGraph, AutoGen, etc.), and the real challenges of designing such graphs.
@0xwhrrari: Anthropic engineers showed how to build agents that run for days with graph engineering "More than 30% of our code is aβ¦
Anthropic engineers shared insights on building long-running agents using graph engineering in a workshop, highlighting that over 30% of their code is written by agentic graphs to accelerate development.