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LangGraph
A framework for building multi-step, stateful AI agent workflows as explicit graphs of steps and decision points.
- Category Orchestration
- Used In 2 Cortex Services
Technology Overview
What Is LangGraph?
LangGraph is an open-source framework for defining AI agent logic as an explicit graph of steps, branches and state, which makes multi-step agent behavior easier to reason about, test and control compared to a single unstructured prompt loop.
At a Glance
Key Capabilities
- LangGraph supports stateful, multi-step agent workflows with conditional branching, retries and human-in-the-loop checkpoints, so an agent's decision path can be inspected and constrained rather than left fully open-ended.
How Cortex Uses It
Cortex Texas AI uses LangGraph, where appropriate, to design AI agents whose steps and escalation paths need to be explicit and auditable — consistent with our general preference for explainable automation over fully opaque agent behavior.
- Accuracy
- Latency
- Cost
- Privacy
- Integration
- Multimodal Needs
- Reasoning Depth
Connectivity
Integration Scenarios
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Common integration scenarios include multi-step business-process agents with defined approval or escalation points, and workflows that combine several tool calls with conditional logic between them.
Model Selection
The Right Model Depends on the Workload
LangGraph is one of several frameworks for structuring multi-step agent workflows; Cortex Texas AI selects the right tool, or a custom-built state machine, based on the workflow's complexity.
In Practice
Used In These Services
AI Agents & Agentic Systems
AI agents that take multi-step action, calling your tools and systems to complete tasks rather than just answering questions.
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