Warranted

Make AI research agents accountable — give every conclusion a traceable argument graph.

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The problem

AI coding agents converge because compilers and tests provide objective failure signals. Research has no equivalent — conclusions live in natural language with no external verifier, so agents routinely declare work complete with no way to know what's missing.

Warranted fills that gap. It gives AI agents a persistent argument graph where every Claim requires Grounds and a Warrant, contradictions are recorded as Rebuttals rather than erased, and status can only advance after passing a logic check (compile). The result is research reasoning that is auditable, reproducible, and verifiable — not just plausible-sounding.

Good fit for: paper reproduction, hypothesis verification, multi-step scientific reasoning, research transparency.

Related MCP server: browse-ai

Documentation

New here? Read in this order:

Release history: CHANGELOG.md

Setup with Claude Code

1. Clone and configure

Install Bun (>= 1.0.0), then:

git clone https://github.com/yqi96/warranted
cd warranted

# Optional: enable LLM logic review
cp review.json.example review.json
# Edit review.json and fill in apiKey

2. Register with marketplace (once per machine)

claude plugin marketplace add $(pwd)

3. Install the plugin in your project

cd your-project
claude plugin install warranted@warranted --scope local

On launch, toulmin-researcher becomes the primary agent and the MCP server starts automatically.

When LLM review is enabled, node definitions are reviewed on creation and compile_arguments runs a full logic-chain audit.

Hitting install or version issues? See known-working versions for a verified dependency snapshot.

Visualizer

From the warranted directory:

bun run viz

Open http://localhost:3456 in your browser.

Interaction

The ⬚ / ✥ buttons in the toolbar switch between box-select and pan mode.

Selection as context

The visualizer server tracks the current selection. Once the plugin is running, every message you send to Claude automatically includes the selected nodes as context — no need to describe which nodes you mean, just select and ask.

Agents

Skills