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Expose vehicle diagnostics and CAN-bus logic to AI agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Canopy" yet — see the docs or source repo.
Use Canopy’s vehicle-diagnostic capabilities and available CAN-bus information to summarize likely causes and next troubleshooting steps.
A set of fault hypotheses, related signals, and a troubleshooting order.
Use Canopy as an MCP tool to design a LangGraph-based vehicle diagnostic workflow and explain how each step coordinates.
A step-by-step agent workflow description.
A developer wants to connect vehicle diagnostics logic to an AI assistant so the model can reason over CAN-bus domain knowledge. Canopy provides agent-callable tools for that.
A researcher or team needs to evaluate a diagnostic agent and include human review in the loop. Canopy includes a human-in-the-loop eval harness for this kind of validation.
When a task needs multiple coordinated steps, Canopy’s LangGraph orchestration layer can organize diagnosis, reasoning, and evaluation.
Canopy is an MCP server that exposes vehicle-diagnostic and CAN-bus domain logic as agent tools, with a LangGraph orchestration layer and a human-in-the-loop eval harness.
It is best suited for developers, researchers, and platform/ops teams building, orchestrating, or evaluating vehicle-diagnostics AI workflows.
The provided information does not include installation steps, runtime, or dependency requirements; see the source repository.
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