TinyFish

TinyFish enterprise infrastructure for AI web agents with search, clean page extraction, browser automation, and multi-step execution for dynamic workflows at scale.

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Enterprise infrastructure for AI web agents

TinyFish is an enterprise infrastructure platform for AI web agents. It combines search, page fetching, browser automation, and multi-step agent execution so teams can work with the live web rather than static datasets.

The product is positioned for workflows that depend on dynamic pages, authenticated sessions, structured outputs, and repeated web interactions at scale. The site presents Search and Fetch as free APIs, while Agent and Browser are credit-based services for heavier automation tasks.

Core capabilities

Live web search

Search returns fresh URLs from the live web and is described as real browser-rendered search that can surface structured JSON from dynamic sources.

Clean page extraction

Fetch renders a page in a real browser and returns cleaned content as markdown, JSON, or HTML for downstream processing.

Multi-step web automation

Agent handles multi-step browser tasks such as navigation, authentication, form filling, and structured result extraction.

Stealth browser sessions

Browser provides stealth Chromium sessions for authenticated or anti-bot-protected sites, with persistent state and sub-250ms cold starts.

Multiple integration paths

The platform is available through MCP, a Python SDK, CLI, and direct API, so teams can connect it to assistants and custom applications.

Plan structure by workload

Pricing includes separate support for Search, Fetch, Agent, and Browser usage, with credits applied to the heavier automation APIs.

Practical use cases

Travel inventory monitoring
Monitor booking sites and other dynamic listings to keep inventory and pricing current across many pages.
Social listening and research
Extract sentiment and trend signals from social platforms, forums, and review sites for research and reporting.
Competitive price monitoring
Track competitor prices, availability, and promotions across e-commerce catalogs and retail sites.
Insurance quoting automation
Automate insurance quote flows that require authentication, form filling, and structured quote extraction.
Autonomous QA testing
Use browser automation to validate web application behavior as part of autonomous QA pipelines.

Pros and Cons

Pros

Supports search, clean extraction, browser automation, and agent execution in one platform.
Search and Fetch are presented as free APIs, which lowers the barrier to trying the product.
Connects through MCP, SDKs, CLI, and direct API, so it can fit both assistants and custom engineering workflows.
Public use cases show coverage for monitoring, quoting, market intelligence, and QA workflows.
Pricing page includes a clear path from pay-as-you-go to enterprise procurement needs, including custom credits and on-premise availability.

Cons

The site gives limited detail on setup steps, data retention, and implementation requirements.
Several capability descriptions are broad, so readers may need the docs or sales team for exact limits and operational fit.
Enterprise security and compliance options are mentioned on the pricing page, but the public pages do not fully spell out the scope for every plan.

FAQ

What does TinyFish do?

TinyFish provides APIs and infrastructure for web agents, including Search, Fetch, Agent, and Browser. The site presents it as an enterprise product for teams that need to navigate websites, authenticate, extract data, and automate workflows.

How is TinyFish priced?

The pricing page shows free Search and Fetch APIs, while Agent and Browser consume credits. Plans include Pay as you go, Starter, Pro, and Enterprise, with Enterprise offering custom credits, SLA, and on-premise options.

What tools can TinyFish connect to?

The integrations page shows support for MCP-compatible clients, the MCP Registry, OpenClaw, Vercel, Dify, n8n, Cursor MCP, AG2, and a Python SDK. The site also says TinyFish offers a direct REST API for custom integrations.

What kinds of workflows is TinyFish used for?

The use-cases page highlights price monitoring, social listening intelligence, competitive price monitoring, insurance quoting automation, Japan market intelligence, and autonomous QA testing.

How do teams use TinyFish in practice?

The site does not describe a single setup path in detail, but it does show web and API access through Search, Fetch, Agent, Browser, MCP, and SDK options. That suggests it can fit both low-code workflows and custom developer integrations.

Quick Facts

Category: AI web agent infrastructure
Primary users: Developers, AI teams, and enterprise operations teams
Source domain: tinyfish.ai
Core APIs: Search, Fetch, Agent, Browser
Pricing model: Free Search and Fetch; credits for Agent and Browser; paid plans available
Integration options: MCP, Python SDK, CLI, REST API, and listed ecosystem integrations