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Initialize LangGraph SDA Workflow

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Linked challenge: Autonomous Space Domain Awareness (SDA) Agent with LangGraph and LastMile AI

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Linked challenge

Autonomous Space Domain Awareness (SDA) Agent with LangGraph and LastMile AI

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Using LangChain and LangGraph, define a state machine for an SDA monitoring system. Create nodes for 'Ingest_TLE', 'Physics_Check', and 'Report_Generator'. The state should store the current orbital elements and a history of detected anomalies. Use LastMile AI's model via ChatOpenAI-compatible wrappers to perform the reasoning in the 'Physics_Check' node.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Autonomous Space Domain Awareness (SDA) Agent with LangGraph and LastMile AI

Inspired by the Space Force's 'RG-XX neighborhood watch' satellites and the call for a 'Planetary Neural Network,' this challenge tasks you with building a multi-agent system for autonomous orbital monitoring. Using LangChain and LangGraph, you will orchestrate a workflow where specific agents ingest Two-Line Element (TLE) sets, simulate orbital trajectories using SGP4, and utilize LLMs to identify non-cooperative maneuvers or potential conjunction events. The system must process disparate telemetry sources and generate actionable 'Space Situational Awareness' reports for satellite operators. You will integrate LastMile AI models to perform high-reasoning tasks on orbital state vectors, identifying anomalies that deviate from predicted Keplerian physics. The architecture should leverage stateful workflows to track satellite 'life cycles' and maintain a persistent memory of historical maneuver patterns to distinguish between routine station-keeping and adversarial proximity operations.

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