TinyFish
TinyFish is the web operating layer for AI agents, providing the infrastructure agents need to reliably access and operate across the live web. As AI applications evolve beyond text generation into autonomous systems that gather information, navigate websites, and complete multi-step tasks, fragmented automation stacks and custom-built infrastructure become difficult to maintain.
TinyFish unifies web search, content extraction, browser infrastructure, and agentic web interaction within a single platform. Agents can search the live web, extract clean LLM-ready content from modern websites, render JavaScript-heavy pages, and execute authenticated workflows without stitching together multiple vendors or maintaining custom infrastructure.
Built for production AI systems, TinyFish emphasizes accuracy, efficiency, freshness, and scalability. By providing clean web context and dependable execution across dynamic web environments, the platform helps developers build AI applications that
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Scalebrowser
Scalebrowser is self-hosted browser infrastructure for AI agents that need to finish real work. Each isolated profile keeps the same identity, cookies, passkeys, memory, and tasks across restarts and machines. A patched Chromium engine, real local GPU values, human-like input, captcha handling, email and SMS codes, TOTP, and browser-level prompt control help agents get through the places where ordinary automation stops. Connect over MCP, REST, Node.js, Python, or direct CDP.
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Contextually
Contextually is an enterprise AI platform designed to help organizations build and deploy production-ready AI agents that can reason over complex, domain-specific data using advanced context engineering. It provides a unified context layer that connects AI models to large volumes of enterprise knowledge, including documents, databases, and multimodal data, enabling agents to deliver accurate, grounded, and relevant outputs. It allows users to define and configure agents quickly through prebuilt templates, natural language prompts, or a visual drag-and-drop interface, supporting both dynamic agents and structured workflows tailored to specific use cases. It includes tools for ingesting and processing massive datasets from multiple sources, transforming unstructured and structured information into retrievable knowledge with intelligent parsing, metadata generation, and continuous updates.
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StateFabric
StateFabric a small infrastructure layer for AI agents that need more context than chat history.
Once an agent starts using tools, running for longer sessions between restarts, ‘keep the messages in memory’ doesn’t really cut it. You need to know:
- what happened?
- what state changed?
- which tools were called?
- what context should go into the next model turn?
StateFabric stores an append-only event log for agent runs, then derives working context from it.
Today it supports:
- Durable sessions and event storage
- User/model/tool event timelines
- Reconstructed session state from stored events
- Compacted model-facing context
- A dashboard for inspecting sessions, raw payloads, compaction artefacts, and usage
- Google ADK integration via @statefabric/adk
- Direct Node/REST usage via @statefabric/client (for custom runtimes)
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