$ loading_
Provide persistent, explainable, MCP-native memory for AI agents.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.Astrix-Labs/genesys-memory" MCP server from askskill: Run: claude mcp add 'io-github-astrix-labs-genesys-memory' -- npx -y genesys-memory
Save the following user preferences as long-term memory and explain how future conversations should use them: prefers Chinese replies, focuses on B2B SaaS, needs a weekly project report every Monday.
Returns the stored memory and explains that it can be reused in future tasks.
Retrieve the agent's recorded memory related to the user's writing style preferences and summarize it as supporting context.
Outputs relevant memory snippets or a summary to help the agent answer in the user's preferred style.
Explain how this memory was formed, which interactions it came from, and why it affects the current recommendation.
Provides an explainable account of memory origin and impact on the agent's decision.
Developers can use it to give AI agents persistent memory across sessions, reducing repeated context input. It fits agent workflows that need long-term context.
Researchers or product managers can use its explainable memory to understand why an agent responded a certain way. This makes it easier to trace how memory affects current outputs.
If your agent system uses MCP, this tool can be integrated as a native memory component. It is designed for AI agent memory rather than general document management.
It is an open-source causal memory tool for AI agents, offering persistent, explainable, MCP-native memory. The description states that it includes 13 tools.
It is suitable for AI agent scenarios that need cross-session context retention, memory retrieval, and explainable memory provenance. For framework-specific integration details, see the source repository.
The provided material does not include installation steps, runtime requirements, or key prerequisites. Please see the source repository for setup details.
Free persistent memory for AI agents with storage and semantic search.
Give AI agents persistent memory with semantic search and automatic memory management.
Provide local-first persistent memory for AI agents across projects.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Store completed agent sessions as long-term memory and retrieve relevant past interactions through MCP.
Give AI agents persistent memory to log and recall conversation context via MCP.