Deterministic, verify-first infrastructure for autonomous AI software engineering.
Plan → Route → Execute → Verify → Evaluate
Picado Labs builds open-source infrastructure that splits AI-driven software development into deterministic, observable stages.
Instead of a monolithic AI prompt, we divide the engineering lifecycle into four specialized, verifiable tools:
| Project | Stage | The Problem | The Picado Edge |
|---|---|---|---|
| build-with-ai | Plan | Devs struggle to orchestrate AI for large, complex architectures. | Zero-API CLI that guides you step-by-step using your existing ChatGPT/Claude interface. |
| AI Model Router | Route | Balancing LLM costs, rate limits, and reasoning capabilities is hard. | 100% Local & Explainable routing with a visual "Traffic Control Room" UI. |
| Terminal Agent | Execute | Autonomous agents frequently break builds without strict verification. | Verification-first sandboxing with built-in state checkpoints and instant rollback. |
| AgentBench | Evaluate | Measuring agent performance objectively is heavy and provider-locked. | Instant GitHub PR Ingestion to create provider-agnostic benchmarks in seconds. |
# 1. Plan your project architecture
npx build-with-ai init
# 2. Route prompts to the optimal model
python backend/app/cli/main.py route "Build a secure token bucket rate limiter"
# 3. Execute and verify the code
terminal-agent run "Implement token bucket rate limiter with pytest tests"
# 4. Evaluate the agent's performance
python agentbench.py run --benchmark fix-rate-limiter --provider ollamaSee individual repository readmes for full setup instructions.
Open source under Apache 2.0. PRs are welcome!
Built by Vardhman (Kap10) — X (Twitter) | LinkedIn | picadolabs.me
Open source. Practical AI infrastructure. Built to be verified.