Memory Infrastructure for AI Agents
- Echo provides an external long-term memory layer for AI agents, overcoming the limitations of stateless sessions.
- Instead of relying on repeated prompts, memory tools retrieve stored context so agents can process requests without the developer re-typing project instructions.
- It supports two primary interfaces: an automated MCP server for AI tool calls and a terminal CLI for direct human curation of the memory state.
- The integrated DuckDB analytical engine tracks knowledge ROI and resource impact so memory usage can be reviewed, refined, and kept high-signal.
Core Capabilities
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Shared MCP & CLI Access
Agents and humans work with the same local memory state through MCP tools or terminal commands.
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Persistent Store & Recall
Save instructions, facts, and artifacts, then recall them by context with benchmarked sub-millisecond local retrieval.
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FTS5 Search & Curation
Find, update, and delete memories precisely with SQLite FTS5 search and ID-based governance.
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Usage Analytics
Use embedded DuckDB telemetry to review knowledge ROI, cost-efficiency, and environmental impact.
Driving Smarter, More Capable AI
- Enhanced Context: AI agents can retrieve relevant context across extended interactions.
- Continuous Learning: Knowledge gained is persistent, improving agent performance over time.
- Reliable Persistence: Leverages SQLite for persistent, local data storage.
- Developer Experience: Simple JSON-RPC interface for easy integration into agent architectures.
- Local Control: Memory stays inspectable, editable, and portable through shared MCP and CLI workflows.