Kubbi provides a specialized infrastructure solution for developers building agentic workflows and autonomous systems. It addresses the common challenge of passing large or sensitive data payloads between AI agents through standard communication channels like webhooks, message queues, or Model Context Protocol tools. By implementing the claim-check pattern, the platform allows developers to offload data storage while passing a secure, temporary URL through the primary message channel. This approach helps bypass size limitations often found in standard messaging protocols and prevents sensitive information from being permanently stored in communication logs. The service is designed with security and automation in mind, offering server-enforced features such as burn-after-read functionality and customizable expiration timers. Every payload is encrypted at rest using AES-256-GCM, ensuring that data remains protected throughout the handoff process. Additionally, a manifest preview feature allows receiving agents or systems to inspect metadata like file names and types before fully retrieving the content, which preserves retrieval limits. By centralizing the management of temporary data payloads, Kubbi simplifies the plumbing required for complex agent-to-agent interactions, allowing engineers to focus on the logic of their autonomous applications.
Problem: Passing large data files between agents from different vendors often triggers size limits or security risks in communication channels.
Solution: Kubbi holds the large file and provides a claim URL that the recipient agent can use to retrieve the data securely.
Example: A data-analysis agent sends a 50MB CSV to a reporting agent via a claim URL passed through a standard webhook.
Problem: Sensitive data often lingers in webhook logs or message queues long after it has been processed.
Solution: The platform uses a burn-after-read policy where the data is automatically deleted as soon as it is retrieved.
Example: A customer support bot passes user credentials to a backend service via a claim URL that expires immediately after one use.
Problem: Agents often need to send multiple files at once, which is difficult to manage via simple messaging protocols.
Solution: Kubbi allows producers to bundle multiple files into a single 'kubbi' package with a manifest for the receiver to preview.
Example: A coding agent sends a bundle containing source code, a config file, and a README to a deployment agent using one URL.
Target audience: Best for: AI developers, backend engineers, autonomous system architects
Pricing: Freemium · Categories: Chatbot Development, Developer Tools, Startup tools
Tags: ai agent, API, chatbot, developer tools, startup tools
Kubbi is a specialized infrastructure service designed for AI developers and backend engineers. It uses the claim-check pattern to enable secure, temporary payload handoffs between autonomous agents over message queues, webhooks, and Model Context Protocol tools without overloading communication channels with large or sensitive data payloads.
Kubbi offloads large data payloads and returns secure claim URLs for downstream agents to access. It protects data using AES-256-GCM encryption at rest and features configurable expiration timers, multi-file bundling, manifest previews, and server-enforced burn-after-read deletion to remove data immediately upon retrieval.
Kubbi provides integration through native Model Context Protocol server tools as well as dedicated SDKs for TypeScript and Python. Because specific installation commands vary depending on your chosen environment and language runtime, developers should follow the setup instructions documented in the official project repository or documentation at https://kubbi.ai/.
Kubbi encrypts all stored payloads at rest using AES-256-GCM encryption. In addition, it prevents sensitive information from persisting in communication logs by applying server-enforced burn-after-read rules that delete data as soon as it is fetched, alongside configurable time-to-live expiration policies.
Kubbi follows a freemium pricing model. This structure allows developers and architects to test and integrate temporary payload handoffs and claim-check features for their AI workflows, with options to scale as their data volume and autonomous agent interactions increase.