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<!doctype html>
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<meta name="description" content="Documentation for the Picado Labs open-source AI engineering stack: build-with-ai, Model Router, Terminal Agent, and AgentBench." />
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<p class="eyebrow">Documentation</p>
<h1>The Picado Labs stack, explained.</h1>
<p class="hero__lede">
Four open-source projects that turn AI coding from a prompt into a verified engineering loop: plan, route, execute, verify, and evaluate.
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<span>Docs map</span>
<strong>4 projects</strong>
</div>
<ol class="loop-list">
<li><span class="step">01</span><div><strong>build-with-ai</strong><small>Plan projects with context memory</small></div></li>
<li><span class="step">02</span><div><strong>Model Router</strong><small>Route prompts to the best model</small></div></li>
<li><span class="step">03</span><div><strong>Terminal Agent</strong><small>Execute and verify in a sandbox</small></div></li>
<li><span class="step">04</span><div><strong>AgentBench</strong><small>Benchmark agent quality</small></div></li>
</ol>
</div>
</div>
</section>
<section class="docs-layout">
<div class="wrap docs-shell">
<aside class="docs-toc" aria-label="Documentation contents">
<strong>Contents</strong>
<a href="#ecosystem">What is Picado Labs?</a>
<a href="#closed-loop">Closed-loop architecture</a>
<a href="#build-with-ai">build-with-ai</a>
<a href="#model-router">Model Router</a>
<a href="#terminal-agent">Terminal Agent</a>
<a href="#agent-bench">AgentBench</a>
<a href="#comparison">Why this stack?</a>
<a href="#quickstart">Quickstart</a>
</aside>
<div class="docs-content">
<section class="doc-section" id="ecosystem">
<p class="eyebrow">Overview</p>
<h2>What is Picado Labs?</h2>
<p>
Picado Labs is an open-source ecosystem for building autonomous AI software agents that can be inspected, routed, executed, verified, and benchmarked.
</p>
<div class="doc-grid doc-grid--3">
<article class="doc-card">
<span class="tag">Problem</span>
<h3>Prompt-only agents drift.</h3>
<p>Long chats lose context, model choice is often fixed, and success is too often accepted from the model's own claim.</p>
</article>
<article class="doc-card">
<span class="tag">Approach</span>
<h3>Make the loop explicit.</h3>
<p>Picado Labs separates planning, routing, execution, verification, and evaluation into focused tools.</p>
</article>
<article class="doc-card">
<span class="tag">Outcome</span>
<h3>Proof before ship.</h3>
<p>Agents must pass objective checks, run in bounded environments, and improve through benchmark feedback.</p>
</article>
</div>
</section>
<section class="doc-section" id="closed-loop">
<p class="eyebrow">Architecture</p>
<h2>Closed-loop AI engineering pipeline</h2>
<p>
The stack is designed as a feedback loop. Each stage produces a concrete artifact for the next stage: plan, selected model, code changes, verification results, and benchmark telemetry.
</p>
<pre class="diagram"><code>[ Software Goal / Spec ]
|
v
01 PLAN build-with-ai
|
v
02 ROUTE Model Router
|
v
03 EXECUTE Terminal Agent
|
v
04 VERIFY Tests + AST validation
| |
PASS FAIL ----> Repair / rollback ----+
| |
v |
05 EVALUATE AgentBench |
| |
+---- telemetry improves router/agent -+</code></pre>
</section>
<section class="doc-section project-doc" id="build-with-ai">
<div class="doc-title-row">
<div>
<p class="eyebrow">01 / Plan</p>
<h2>build-with-ai</h2>
</div>
<div class="doc-title-links">
<a class="text-link" href="https://github.com/PicadoLabs/build-with-ai" target="_blank" rel="noopener">Repository</a>
<span class="link-sep">•</span>
<a class="text-link" href="https://github.com/PicadoLabs/build-with-ai/issues" target="_blank" rel="noopener">Issues</a>
</div>
</div>
<p>
<strong>What is this?</strong> A zero-API CLI prompt architect (15+ contributors • 1.2k downloads). It helps a developer turn a software idea into ordered implementation phases, reusable decisions, and copy-ready prompts for any AI assistant.
</p>
<div class="doc-grid">
<article class="doc-card">
<h3>How it works</h3>
<p>It creates a local `.buildwithai/` workspace, stores project context, tracks completed phases, and injects past decisions into future prompts.</p>
</article>
<article class="doc-card">
<h3>Best for</h3>
<p>Project setup, architecture planning, reducing chat drift, and keeping AI-assisted development structured without API keys.</p>
</article>
</div>
<pre class="diagram"><code>Developer goal
|
v
build-with-ai init
|
+--> project template
+--> .buildwithai/context.json
+--> .buildwithai/state.json
|
v
next prompt -> AI assistant -> developer records decisions -> next phase</code></pre>
<pre><code>npx build-with-ai init
npx build-with-ai next
npx build-with-ai done
npx build-with-ai export</code></pre>
</section>
<section class="doc-section project-doc" id="model-router">
<div class="doc-title-row">
<div>
<p class="eyebrow">02 / Route</p>
<h2>Model Router</h2>
</div>
<div class="doc-title-links">
<a class="text-link" href="https://github.com/PicadoLabs/ai-model-router" target="_blank" rel="noopener">Repository</a>
<span class="link-sep">•</span>
<a class="text-link" href="https://github.com/PicadoLabs/ai-model-router/issues" target="_blank" rel="noopener">Issues</a>
</div>
</div>
<p>
<strong>What is this?</strong> An explainable AI traffic controller (5+ contributors • 2.2k downloads) that routes each task to the best local or cloud model using quality, cost, latency, capability, and reliability signals.
</p>
<div class="doc-grid">
<article class="doc-card">
<h3>How it works</h3>
<p>It extracts task features, removes models that cannot satisfy hard requirements, then uses Pareto scoring to select the best candidate in under 3ms.</p>
</article>
<article class="doc-card">
<h3>Best for</h3>
<p>Reducing model cost, using local Ollama when possible, routing hard reasoning tasks to stronger models, and auditing routing decisions.</p>
</article>
</div>
<pre class="diagram"><code>Incoming prompt
|
v
Feature extraction: task type, complexity, context size
|
v
Hard filters: context window, tools, vision, coding, reasoning
|
v
Pareto scoring: quality + cost + latency + reliability
|
v
Selected model -> response + telemetry</code></pre>
<pre><code>pip install model-router-cli
modelrouter doctor
modelrouter route "Write an optimized async task worker in Python"
modelrouter ui --open-browser</code></pre>
</section>
<section class="doc-section project-doc" id="terminal-agent">
<div class="doc-title-row">
<div>
<p class="eyebrow">03 / Execute + Verify</p>
<h2>Terminal Agent</h2>
</div>
<div class="doc-title-links">
<a class="text-link" href="https://github.com/PicadoLabs/terminal-agent" target="_blank" rel="noopener">Repository</a>
<span class="link-sep">•</span>
<a class="text-link" href="https://github.com/PicadoLabs/terminal-agent/issues" target="_blank" rel="noopener">Issues</a>
</div>
</div>
<p>
<strong>What is this?</strong> A verify-first autonomous coding agent (2+ contributors • 440+ downloads) that edits files, runs commands, tests work, classifies failures, and can roll back checkpoints.
</p>
<div class="doc-grid">
<article class="doc-card">
<h3>How it works</h3>
<p>It observes the task and workspace, plans a bounded implementation, acts through typed tools, runs verification, then repairs or rolls back when checks fail.</p>
</article>
<article class="doc-card">
<h3>Best for</h3>
<p>Agentic coding tasks where success must be backed by tests, process limits, secret redaction, and repeatable recovery behavior.</p>
</article>
</div>
<pre class="diagram"><code>Task + repository
|
v
Observe -> Plan -> Act with engineering tools
|
v
Run in LocalSandbox or DockerSandbox
|
v
IndependentVerifier executes tests
|
+--> pass: proof of done
+--> fail: classify failure -> repair plan -> retry or rollback</code></pre>
<pre><code>pip install terminal-agent-cli
terminal-agent doctor
terminal-agent run "Implement JWT auth middleware with pytest tests"
terminal-agent resume</code></pre>
</section>
<section class="doc-section project-doc" id="agent-bench">
<div class="doc-title-row">
<div>
<p class="eyebrow">04 / Evaluate</p>
<h2>AgentBench</h2>
</div>
<div class="doc-title-links">
<a class="text-link" href="https://github.com/PicadoLabs/agent-bench" target="_blank" rel="noopener">Repository</a>
<span class="link-sep">•</span>
<a class="text-link" href="https://github.com/PicadoLabs/agent-bench/issues" target="_blank" rel="noopener">Issues</a>
</div>
</div>
<p>
<strong>What is this?</strong> A provider-agnostic benchmark platform (3+ contributors) for measuring coding agents on realistic Python and Node.js software-engineering tasks.
</p>
<div class="doc-grid">
<article class="doc-card">
<h3>How it works</h3>
<p>It runs agents against standardized tasks, validates output with tests, classifies failures, and calculates a 5D SWE score.</p>
</article>
<article class="doc-card">
<h3>Best for</h3>
<p>Comparing agents, calibrating model choices, evaluating PR-fix workflows, and building confidence before deployment.</p>
</article>
</div>
<pre class="diagram"><code>Benchmark task
|
v
Clean sandbox -> agent under test -> code edits
|
v
Independent test suite
|
v
Score = correctness + pass rate + quality + efficiency + repair
|
v
Dashboard, telemetry, leaderboard, CI gate</code></pre>
<pre><code>python agentbench.py doctor
python agentbench.py init
python agentbench.py list
python agentbench.py run --benchmark fix-rate-limiter --provider ollama</code></pre>
</section>
<section class="doc-section" id="comparison">
<p class="eyebrow">Why this stack?</p>
<h2>Picado Labs replaces AI self-reporting with engineering evidence.</h2>
<div class="comparison-table" role="table" aria-label="Picado Labs comparison">
<div role="row"><strong role="cell">Need</strong><strong role="cell">Typical AI wrapper</strong><strong role="cell">Picado Labs</strong></div>
<div role="row"><span role="cell">Planning</span><span role="cell">One long prompt</span><span role="cell">Structured phases with persistent context</span></div>
<div role="row"><span role="cell">Model choice</span><span role="cell">Fixed provider</span><span role="cell">Explainable local/cloud routing</span></div>
<div role="row"><span role="cell">Execution</span><span role="cell">Direct host commands</span><span role="cell">Bounded process sandbox and policies</span></div>
<div role="row"><span role="cell">Verification</span><span role="cell">Model says it is done</span><span role="cell">Independent tests and validation</span></div>
<div role="row"><span role="cell">Evaluation</span><span role="cell">Subjective demos</span><span role="cell">Repeatable benchmark tasks and 5D scoring</span></div>
</div>
</section>
<section class="doc-section" id="quickstart">
<p class="eyebrow">Quickstart</p>
<h2>Run the ecosystem path.</h2>
<p>
Start with the planning primitive, then add routing, execution, and evaluation as your workflow becomes more automated.
</p>
<pre><code># 1. Plan
npx build-with-ai init
npx build-with-ai next
# 2. Route
python backend/app/cli/main.py route "Build a secure token bucket rate limiter"
# 3. Execute + verify
terminal-agent run "Implement token bucket rate limiter with pytest tests"
# 4. Evaluate
python agentbench.py run --benchmark fix-rate-limiter --provider ollama</code></pre>
</section>
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<span class="footer__title">Picado Labs</span>
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<p class="footer__tagline">Deterministic, verify-first software systems for autonomous AI agents.</p>
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Founder and Maintainer <strong><a href="https://github.com/Kaap10" target="_blank" rel="noopener">@Vardhman Gupta</a></strong>
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<strong>Projects</strong>
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<a href="https://github.com/PicadoLabs/ai-model-router" target="_blank" rel="noopener">Model Router</a>
<a href="https://github.com/PicadoLabs/terminal-agent" target="_blank" rel="noopener">Terminal Agent</a>
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<span>Open source infrastructure under Apache 2.0 License.</span>
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