Carl

Carl helps an AI agent search and compare used items on Facebook Marketplace. It saves the listings, photos, and research behind the agent's conclusions so the work can be checked and continued later.

Carl is designed primarily for agents through MCP. Its CLI runs background work and shows activity.

The flow

1. Search — Search Marketplace and save what it returned.

2. Collect details — Fetch listing pages and photos. Previously collected pages and images are reused instead of downloaded again.

3. Organize the search — Combine related searches, such as several product names or price ranges, into one workspace without duplicate listings.

4. See what is known — View the newest known price, availability, description, photos, and prior research for each listing. Available listings are shown by default.

5. Evaluate promising listings — A product guide is a reusable checklist of what the agent should determine. Carl gives Claude that guide together with the listing text and photos. Claude can identify the product, research published specifications, compare them with the seller's claims, assess visible condition and missing parts, and suggest questions or checks for pickup. The report says what is known, what is uncertain, and which listing evidence it used.

6. Refresh and revisit — Rerun the searches, see which listings are new or materially changed, and reuse earlier analysis when the evidence still applies.

Related MCP server: BopMarket MCP Server

Features

• Search several phrases or price ranges and review their combined results.

• See a current listing view assembled from the newest useful search, listing-page, photo, and research data while retaining its sources.

• Available-only results by default, with explicit controls for pending, sold, unavailable, and unknown listings.

• Use different evaluation guides for different kinds of products in one workspace.

• Keep shortlists and decisions so another session or agent can continue the review.

• Refresh searches without discarding history, highlight meaningful changes, and avoid downloading unchanged pages and photos again.

• Continue long-running searches, downloads, and evaluations across restarts, with automatic retries for temporary failures.

• Build explicit, ordered network paths from VPN and proxy layers. Carl includes integrations for Proton and Mullvad through WireGuard, plus Decodo and Bright Data proxies, and records the exact path used for every request.

Running Carl

Carl requires Python 3.13 or newer. From a checkout:

uv sync --all-groups
uv run carl init
uv run carl locations

At least one network path must be configured before live collection. carl locations shows the user-specific configuration, database, and image directories.

Keep one worker process running whenever queued collection or analysis should progress:

uv run carl monitor --work

This runs background work and displays its progress. Use uv run carl work to run it without the dashboard. MCP servers intentionally do not start background work themselves.

Configure an MCP client to launch:

uv run carl mcp

The MCP interface guides agents through the workflow above and allows them to inspect the underlying evidence when necessary.

Development

mise run check
mise run pre-commit

The project is under active development. See the architecture notes and review workspace design for the detailed model.

Carl is available under the MIT or Apache-2.0 license.