Budget-controlled research depth
Start a research task with a budget, and Webhound keeps following leads until the budget is used. This makes depth an explicit control rather than a fixed package.
Webhound
Webhound is a deep research platform that produces cited reports, datasets, and claim traces from a budget you set before the run. It works through the web app, API, and MCP clients, and is designed for agent-driven research as well as manual review.
What Webhound does
Webhound is a deep research product that turns a question and a budget into cited research work. It is built to keep investigating until the budget runs out, rather than stopping after the first pass or top search results.
The product is positioned for both agent-driven and human-driven workflows. Agents can call it through MCP or the API and receive structured JSON, while users can work in the web app to read, verify, steer, and publish research results.
Core capabilities
Multiple output and workflow modes
Choose between Reports, Datasets, Chains, and Ask depending on whether you need analysis, structured rows, multi-step workflows, or follow-up questions in a workspace.
Source-linked claims and traces
Every report claim links back to the source page and the tool call behind it, with supporting quotes, confidence, and traces available for review.
Exportable human and machine-readable outputs
Export cited reports to Word or HTML, and return datasets as CSV, JSON, or Excel. Claims can also be consumed as structured JSON by agents.
API-first workflow support
Use the API to start research, poll session status, read documents, inspect claims and sources, publish results, or build multi-step chains.
Agent and workspace access
Run the same engine through MCP clients or the web UI. The UI is intended for reading, verifying, steering mid-flight, and sharing results.
Common ways teams use Webhound
Write a decision-ready report
Use a Report when you need cited analysis, comparison, explanation, or a recommendation. The guide frames reports as the right shape for a conclusion you will read.
Build a sourced dataset
Use a Dataset when you need a table of companies, products, leads, or other entities that you can sort, reuse, or enrich later. Each row carries a source trail for its values.
Map a market or competitive move
Use Webhound to compare vendors, competitors, pricing pages, changelogs, and customer feedback when a simple search result is not enough to surface the real differences.
Check a claim before you act
Use it to verify claims that depend on multiple sources, buried documents, or conflicting evidence, such as capacity, compliance, policy, or financial statements.
Run a multi-step research workflow
Use Chains when one step should feed the next, such as researching a topic and then extracting structured fields from the resulting material.
Pros and Cons
Pros
- It produces sourced outputs rather than an uncited summary.
- It supports both long-form reports and structured datasets.
- Claims include evidence, source URLs, quotes, confidence, and traces.
- It can be used from an API, MCP clients, or the web UI.
- The pricing model is usage-based rather than subscription-based.
Cons
- The product is budget-based, so depth depends on how much you allocate to a run.
- The source set does not fully document every supported integration or destination in detail.
FAQ
Can I use Webhound from both an agent and the web app?
Webhound is designed to run as a research engine for an agent or through its UI. The docs show both MCP/API use and an interactive workspace for starting, reading, and sharing runs.
How do I start a research run?
The guide says you can start with a Report or Dataset, define the target, boundaries, deliverable, and proof standard, then choose Plan or One-Shot before launching the run.
What outputs does Webhound produce?
Reports return cited long-form analysis with sources, claims, and working documents. Datasets return tables with sourced values, and claims can also be returned as machine-readable JSON with evidence and confidence.
How does pricing work?
Pricing is budget-based. The service keeps researching until the budget is reached, and the pricing page says new accounts include one $5 Report or Dataset plus $5 free on signup.
What integrations or access methods are supported?
The API docs show a JSON API with endpoints for research, sessions, extractions, chains, files, publications, and account usage. The home page also says Webhound runs inside MCP clients and tools such as Claude Code, Codex, and Manus.
Quick Facts
- Category
- Deep research / research automation
- Primary access
- Web app, API, and MCP clients
- Output types
- Reports, datasets, claims, chains, and Ask
- Source traceability
- Each claim links to source evidence and tool calls
- Pricing model
- Pay-as-you-go, no subscription
- Website domain
- webhound.ai
Альтернативы Webhound
Scite
Scite — AI-платформа для поиска статей, проверки контекста цитирования и ответов на основе научной литературы.
Model Council
Perplexity Model Council is a multi-model research feature that runs a query across several AI models and combines the results into one answer. It is available to Perplexity Max subscribers on the web, with mobile app support coming soon.
GoldenRetriever
GoldenRetriever is a macOS public beta for AI search across videos, audio, and documents in a local media library. It returns sourced answers with timestamps and uses the user's own Gemini API key for AI requests.
Hype
Hype is a web tool for finding trending YouTube topics by category, time range, and scoring mode. It helps creators spot emerging ideas, inspect source videos, and decide what to cover next.
ClayHog
ClayHog — платформа для AI-поиска, которая помогает отслеживать видимость бренда в ChatGPT, Gemini, Perplexity, Claude и Google AI Overviews из одной панели.
Grok AI Assistant
Grok — это бесплатный ИИ-помощник, разработанный xAI, который ставит во главу угла правдивость и объективность, предлагая расширенные возможности, такие как доступ к информации в реальном времени и генерация изображений.