Rivet started as Ironclad's internal tool for building AI agents. Ironclad is a contract lifecycle management company, not an AI dev tooling company, which means Rivet was built under real production pressure by engineers solving actual problems rather than as a developer experience product designed to look compelling in demos. It was open-sourced in 2023, has around 4,000 GitHub stars, and remains one of the more seriously useful tools in a crowded node-based AI workflow space.
The model is a drag-and-drop canvas where you connect nodes: prompt nodes, conditional nodes, tool call nodes, data transformation nodes, subgraph nodes that contain entire workflows you can reuse. The execution is visual and real-time, which sounds like a minor aesthetic choice but is the actual important thing about Rivet. When an AI agent misbehaves, the problem with code-based tools like LangChain is that you're reading logs trying to reconstruct what happened. In Rivet you watch data flow through the graph in real time and see exactly which node produced unexpected output. That debugging experience is qualitatively different.
The graphs are saved as YAML files, which means they live in your git repository alongside your application code. Prompt changes go through code review. This is not how most AI tooling is structured, and it matters for teams trying to maintain quality control over agent behaviour.
Rivet is model-agnostic: OpenAI, Anthropic (including Claude Sonnet 4 and Opus 4 as of recent releases), Google Gemini, and others. Parallel node execution, live token cost tracking, and MCP server integration for GitHub, Slack, Google Drive, and Notion are all built in. The TypeScript library means you can embed Rivet graphs directly in your application code, and rivet serve exposes any graph as an HTTP endpoint.
Who it's for: developers and AI engineers who need to build multi-step LLM agents and want visual tooling without giving up code-level control. If you've been fighting LangChain's abstraction layers, Rivet's transparency is the thing you've been missing. Who it's not for: non-technical users; this is a developer tool with a visual interface, not a no-code product.
Pricing: fully open-source, free, self-hosted. You pay for API calls to whatever model providers you use.
Rivet started as Ironclad's internal tool for building AI agents. Ironclad is a contract lifecycle management company, not an AI dev tooling company, which means Rivet was built under real production pressure by engineers solving actual problems rather than as a developer experience product designed to look compelling in demos. It was open-sourced in 2023, has around 4,000 GitHub stars, and remains one of the more seriously useful tools in a crowded node-based AI workflow space.
The model is a drag-and-drop canvas where you connect nodes: prompt nodes, conditional nodes, tool call nodes, data transformation nodes, subgraph nodes that contain entire workflows you can reuse. The execution is visual and real-time, which sounds like a minor aesthetic choice but is the actual important thing about Rivet. When an AI agent misbehaves, the problem with code-based tools like LangChain is that you're reading logs trying to reconstruct what happened. In Rivet you watch data flow through the graph in real time and see exactly which node produced unexpected output. That debugging experience is qualitatively different.
The graphs are saved as YAML files, which means they live in your git repository alongside your application code. Prompt changes go through code review. This is not how most AI tooling is structured, and it matters for teams trying to maintain quality control over agent behaviour.
Rivet is model-agnostic: OpenAI, Anthropic (including Claude Sonnet 4 and Opus 4 as of recent releases), Google Gemini, and others. Parallel node execution, live token cost tracking, and MCP server integration for GitHub, Slack, Google Drive, and Notion are all built in. The TypeScript library means you can embed Rivet graphs directly in your application code, and rivet serve exposes any graph as an HTTP endpoint.
Who it's for: developers and AI engineers who need to build multi-step LLM agents and want visual tooling without giving up code-level control. If you've been fighting LangChain's abstraction layers, Rivet's transparency is the thing you've been missing. Who it's not for: non-technical users; this is a developer tool with a visual interface, not a no-code product.
Pricing: fully open-source, free, self-hosted. You pay for API calls to whatever model providers you use.