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Chiplab is an intelligent platform built for AI-assisted embedded development. Connect your coding agent once, and from then on you just describe what you want in plain language, “test this firmware on an STM32,” “check if this build works on the nRF52840”, and your agent handles the rest: uploading your binary, running it on a chip-accurate virtual board, and reporting back the results. Every supported board matches its physical counterpart down to the binary, peripherals, and interrupt timing, so what you see in Chiplab reflects what you’d see on real hardware.

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What Chiplab does

Chiplab exposes its capabilities to your agent over MCP. You don’t call these directly; your agent discovers and uses them automatically based on what you ask it to do. Two tools are live today; more are on the way.

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Available now

  • ask: Query Chiplab’s knowledge base with any natural-language question about the platform, its tools, or firmware development patterns.
  • run: Execute firmware on a virtual chip via Renode or QEMU, and get back the run result, console output, and pass/fail status.
ℹ️ Coming soon: Today run covers single-chip execution and console output. Peripheral support, with full fault and panic traces is on the way.

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Upcoming

  • build: Compile remotely against any production toolchain (GCC, LLVM, IAR, Keil, HighTec, Renesas).
  • test: Run unit and integration tests on virtual hardware, with coverage and regression tracking.
  • bench: Profile code and recommend the optimal board across timing, memory, power, and cost — whether you’re picking a chip before writing a line of firmware, or benchmarking code you’ve already built.
  • simulate: Full-system simulation against real-world conditions — motion, sensors, environment — through partner physics engines like BeamNG, dSPACE, and NVIDIA.
  • generate: AI-driven inputs for AI-driven workflows — fuzz cases, synthetic sensor frames, edge-case stimuli.
  • automate: The orchestrator. Describe what you want built, and Chiplab picks the chip, chains build → run → test → bench → simulate → ask, and drives the loop.
See the full parameter reference for each tool in MCP Tools Reference.

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Knowledge corpus

Every simulation run deposits observed behavior into Chiplab’s knowledge corpus, indexed by chip family, failure pattern, and board configuration. Chiplab never collects your firmware code or binaries as part of this, only the chip-level behavior observed during the run. The corpus is shared, so your agent’s answers can draw on patterns observed across the platform, making each simulation more useful over time.

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Supported agents

Chiplab works with any MCP-capable coding agent, with first-class setup guides for Cursor, OpenCode, Claude Code. All three connect to Chiplab over HTTP using the MCP protocol. Authentication is handled through a browser-based OAuth 2.0 flow, no manual token management required.

Quickstart

Run your first simulation in under five minutes.

Connect an Agent

Agent-specific setup guides for Cursor, OpenCode, and Claude Code.

MCP Tools Reference

Full reference for the ask, run, and the tools on the way.