Agent-assisted electronics

Connect Claude, Codex, and AI agents to a real KiCad workflow

Let the agent guide requirements and coordinate tools while deterministic code owns validation, project state, native file generation, and manufacturing evidence.

Install agent skillsExplore AI for KiCad

AI coding agents are useful for electronics because they can read files, call tools, compare evidence, and keep a long workflow moving. But giving an agent unrestricted control of KiCad is not the same as building a reliable design system. The architecture around the model determines whether a result is reproducible and reviewable.

Four ways an AI agent can work with KiCad

Agent skills

A skill teaches Claude Code, Codex, or OpenCode how to run a bounded workflow and when to call engineering tools. Circuit Weaver installs portable skills that route agent conversations into the same Python validation and generation engine.

File-based automation

The agent reads requirements, structured design state, KiCad files, reports, and manufacturing outputs from the project. This is portable, version-control friendly, and easy to audit.

HTTP or Python tools

An API gives the agent explicit operations with typed inputs and outputs. Direct Python integration is useful for custom pipelines, CI, or higher-level hardware applications.

Model Context Protocol

An MCP server exposes named tools and resources to supported assistants. MCP can be valuable for live application control, project inspection, and integrations, but tool availability alone does not guarantee correct component data or safe design decisions.

Direct KiCad control vs. spec-first generation

Direct control is intuitive for edits inside an open KiCad session. A spec-first system is stronger when you need durable requirements, reproducible generation, validation, multiple downstream artifacts, or the ability to resume later. Circuit Weaver centers the workflow on DesignIR and native project artifacts, so the agent is not the only place that “knows” the design.

Recommended agent boundary: let the model plan, research, explain, and orchestrate. Require deterministic tools to parse, validate, mutate, and generate design files. Require human approval for high-impact electrical and fabrication decisions.

A safe KiCad agent loop

  1. Read project state and requirements. Do not infer the board solely from a screenshot or previous conversation.
  2. Plan a bounded change. Identify affected components, nets, constraints, and output files.
  3. Resolve evidence. Fetch the relevant datasheets and component metadata before changing the design.
  4. Apply the change atomically. Reject invalid mutations rather than partially corrupting the project.
  5. Run validation. Parse the exact output file and produce machine-readable findings.
  6. Show the evidence. Present diffs, blockers, confidence, and required human checks.

Install Circuit Weaver for supported agents

pip install circuit-weaver
circuit-weaver install-skills

# Claude Code
/circuit-weaver

# OpenAI Codex
$circuit-weaver

The same core can also run fully offline through circuit-weaver design-wizard. Your KiCad and DesignIR files remain the durable handoff regardless of which agent starts the workflow.