Conol

Answers that die in a chat scroll are not deliverables. Conol takes a different shape: you describe a job once — a daily competitor brief, a long-form research report, a standing morning summary — and the agent runs it on Conol’s servers without holding your browser open. Results land in a structured notes editor you can reorganize and share via link. Telegram or WeChat delivers the ping when the job completes, and you can reply from your phone to redirect the agent mid-run.

The memory model is the most deliberate architectural choice on the page. Everything the agent retains about you lives in a plain file — the vendor surfaces ‘soul.md’ as the example — that you open, edit, or delete directly. No opaque preference store, no vendor-controlled context. Every note exports to markdown and imports from markdown, so the content you accumulate is yours to take.

Conol fits teams running recurring research or monitoring tasks who want results in a notes layer rather than a chat history. Competitor tracking filed every morning, long research reports organized into a reference library, scheduled jobs that check in over messaging — these are the workflows the tool is built around. The tool does not expose an API, which means it cannot be triggered programmatically or embedded as a step inside another system. Teams whose use case requires Conol to receive a webhook, post results to a database, or be orchestrated by a workflow engine will find the architecture does not support that and will move to a platform that does.

On the integration side, the vendor describes per-user OAuth connections to GitHub, Vercel, Cloudflare, Gmail, Calendar, and Drive, with the ability to revoke at any time. Code execution runs in an isolated cloud sandbox rather than on the user’s machine. Messaging is bound once per channel — Telegram or WeChat — and the agent uses that channel both to deliver results and to accept steering replies.