Fetch Hive

Teams trying to move AI ideas into production often struggle with the operational plumbing around large language models: versioning prompts, deploying workflows, tracing what happened during a run, and connecting agents to the tools and data they need. Without this infrastructure, engineers have to hand-build orchestration, monitoring, and integration logic themselves, which slows down shipping and makes it hard to debug or improve AI features once they are live. Customers quoted on the page describe needing to quickly build and implement AI into their tools with little effort, and wanting to track sources, outputs, and steps behind each run rather than treating AI as a black box.

Fetch Hive lets users describe an AI idea in natural language, connect it to tools, knowledge bases, and sub-agents, and deploy it as an API, following a Describe, Connect, Deploy flow. The platform includes a Visual Agent Designer for building and debugging automations by dragging and dropping steps, Real-Time Monitoring that traces every step, tool call, sub-agent, cost, usage, and duration, and a Custom Knowledge Base feature for training agents on a company's own documents and processes. It also supports multi-agent collaboration through shared knowledge and state, a Team Collaboration dashboard for building, testing, deploying, and managing agents in one place, and enterprise security with encryption and granular permissions. Fetch Hive integrates with over 50 apps such as Airtable, Linear, Slack, Dropbox, Google Analytics, and Shopify, and offers an AI Gateway providing access to every major model, so teams can focus on workflow logic rather than managing infrastructure.