It's the management layer your team needs to build, run and benchmark agents in production.
Design your agent visually. Lay out its prompts, tools, decision branches and guardrails on a single canvas, then run it on the same platform you built it in.
Add a sub-agent, attach its tools, pick its model. See the whole architecture at a glance instead of tracing it through code.

Product and ops teams design agents on the canvas. Engineers open the same agent as YAML to review it in a pull request, version it in Git, and ship it with the rest of the code.
name: Support triage
config:
entries:
- type: handler
handler: github
start: agent.classifier
agents:
classifier:
providerID: 1
systemPrompt: Label the issue.
next: agent.researcher
researcher:
providerID: 2
tools: [web_search, http, github_mcp]
next: agent.responder
responder:
providerID: 1
next: end
Agents run shell commands, read files and call tools. Servflow runs all of it inside an operating-system sandbox, on an engine written in Go so it stays small and quick.
$ git clone repo && make test{{ secret "github-token" }}Watch an agent go from an empty canvas to a working run.
Attach any of the 40+ built-in actions, point a sub-agent at an MCP server, or hand a task to another sub-agent on the canvas. Tools are wired visually and versioned with the agent.
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