Dataecho Memory

The problem

Agent context windows are volatile. When a session ends or a sandbox is destroyed, the agent loses all knowledge of user preferences, past architectural decisions, and project-specific gotchas.

What it does

• Persists facts, decisions, and preferences in a private cloud drive that follows the user across different machines and platforms.
• Retrieves relevant information using a lightweight indexing system that minimizes token usage by loading only what is needed.
• Maintains atomic consistency using compare-and-swap operations to prevent data corruption during concurrent writes from multiple agents.
• Supports versioning and history, allowing developers to audit memory changes or roll back to a known good state.
• Enables secure state handoffs between different agents using time-limited, read-only or writable share tokens.

Frameworks & tools

Requires bash and python3 standard library. Uses the DataEcho REST API for storage. Works with Claude Code, Cursor, and any agentic environment with shell access.

Why this beats prompting it yourself

Standard prompting relies on the user to manually re-supply context or maintain a growing README. This skill automates the capture of "delta" knowledge at the moment it is learned, ensuring that expensive decisions and specific corrections are never forgotten or re-litigated in future sessions.

Use cases

• Logging architectural decisions (the "why" and "how to apply") to ensure consistent implementation across sessions.
• Storing project-specific user preferences such as coding style, library choices, or documentation formats.
• Capturing mid-task state and "next steps" before a sandbox times out or a session is closed.
• Sharing context between a discovery agent and an implementation agent via secure handoff tokens.

Known limitations

Designed for durable facts and decisions rather than high-frequency session logs. Not intended for storing secrets or API keys.