Sim

Build the agent as a workflow on the canvas, or describe it conversationally, or write it as code, depending on which you prefer; all three produce the same underlying workflow. Attach a knowledge base so the agent can retrieve from your own documents with semantic search rather than relying on what the model already knows. Use Tables where the agent needs structured state it can read and write across runs, and the shared file store where it needs documents the team also touches. Wire in whichever of the thousand plus integrations the job requires, covering Slack, HubSpot, Salesforce, Notion and the rest, and pick from any major LLM provider rather than being tied to one. Deploy, then watch Logs, which trace each decision end to end with cost tracking attached so you can see both what the agent did and what it spent doing it. Run the whole thing on the hosted product, which carries SOC2, or self host it under Apache-2.0 on your own infrastructure.

• •Visual Workflow Canvas - Design agent logic as a graph rather than assembling it in code
• •Knowledge Base - Semantic memory over your own data so agents retrieve rather than guess
• •Tables - Structured data storage that agents read and write across runs
• •Shared Files - One file store used by both the team and the agents
• •End To End Logs - Trace every agent decision with cost tracking attached
• •1,000+ Integrations - Slack, HubSpot, Salesforce, Notion and hundreds more already wired
• •Every Major LLM - Provider agnostic across OpenAI, Anthropic, Gemini and DeepSeek
• •Three Authoring Modes - Visual builder, conversational, or code, producing the same workflow
• •Apache-2.0 and Self Hostable - Run it yourself, or use the hosted product which carries SOC2

Teams moving agent prototypes into production who do not want to adopt a closed platform to do it. The Apache-2.0 licence and self hosting path matter most to companies with data that cannot leave their infrastructure, and SOC2 on the hosted product covers the ones that would rather not operate it. The three authoring modes let a mixed team collaborate, with an operations person on the canvas and an engineer in code, on the same workflow. Logs with cost tracking are the feature you will care about in month three, when an agent is running unattended and the bill needs explaining. The honest caveat is that this is the most crowded category in AI right now, competing against n8n, Dify, Langflow, Flowise and every model vendor building its own agent builder, and per seat pricing sits awkwardly for agent workloads where value scales with runs rather than headcount.