AgentDrive – persistent, versioned file storage for AI agents

Hacker News

We’re opening the private beta for AgentDrive, a persistent file/workspace layer for AI agents. AgentDrive gives agents: - Versioned artifacts and folders - Hosted MCP access for coding agents - Direct upload sessions for larger files - Share links and controlled public publishing - Workspace-scoped authorization and auditability - Many supported file formats such as MD, HTML, JSON, images, videos, and even spreadsheets The MCP surface is intentionally narrow and reviewed rather than exposing the entire storage API. It’s a private beta, so some features are deliberately out of scope for now. See more details: https://tokencanopy.com/products/agentdrive Try the beta: https://app.tokencanopy.com/drive/ For MCP clients: claude mcp add --transport http --scope user agentdrive https://drive.mcp.tokencanopy.com/mcp claude mcp login agentdrive codex mcp add agentdrive --url https://drive.mcp.tokencanopy.com/mcp codex mcp login agentdrive A public API/SDK integration experience is on the roadmap so that you can use AgentDrive in your product, but it is not available as a beta user interface today. We’d especially appreciate feedback on: 1. Was connecting and using AgentDrive through MCP clear? 2. Which tools or storage primitives are missing? 3. What would be the first workflow you ask your agents do with AgentDrive? Open source repo is also on the roadmap so that you can host it on your own infrastructure! Sign up at tokencanopy.com for more future updates. Any feedback or feature requests can be sent to support@team.tokencanopy.com. You can reach out to me directly at josh@tokencanopy.com I'd love to personally connect with more founders and developers who are building in the coding agent infra space: https://github.com/jiashuoz

Visit product View on Hacker NewsSep 14, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video · Missing: mobile apps, ios, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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