Webhound Agent: A full research assistant with a workspace

An AI research assistant powered by the best deep research money can buy.

The problem

Most deep research products decide for you when to stop. And since they're subscription-based, the less work they do per query, the more they profit.

What we built

Webhound is a research agent where you set a dollar budget and it keeps searching until it's used. $5 or $100 — every dollar goes into deeper search, more sources, and real verification. The output is either a cited research report or a structured dataset with source URLs per cell.

What's new

Today we're launching Webhound Agent — a full research assistant with a workspace.

• Start research from chat. Describe what you need, the agent proposes a session with an editable budget and model. You confirm before anything runs.
• Multi-step pipelines. Chain steps together — "research X, then extract Y, then write a recommendation" — and they run automatically in sequence.
• Analyze your existing work. Ask the agent about any report or dataset. It reads the full document and answers with citations.
• Organize everything in folders. Nested workspace with drag-and-drop, working directories, and folder-scoped context.
• The agent remembers you. It learns your research interests, budget preferences, and working patterns across conversations.

How it works under the hood

Research sessions run a plan-execute-verify loop. The planner decides what to investigate, the executor searches the web and writes a cited report, and the verifier checks every claim against its sources. When the verifier finds gaps, it sends the work back. This cycle repeats until the budget runs out.

Pricing

Pay-as-you-go. No subscriptions. $5 free credits to start. See real example reports at different budgets: https://webhound.ai/examples