$ loading_
Provide persistent semantic memory storage and retrieval for AI agents via MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Selti" yet — see the docs or source repo.
Please save the current multi-turn conversation between the support agent and the user to Selti and create semantic indexing for later retrieval.
The conversation is persistently stored and can be retrieved later by semantic relevance.
Retrieve the historical document snippets most relevant to 'refund policy' from Selti and return them to the current AI agent as context.
Semantically matched document content is returned to help the agent answer based on existing materials.
Read the agent state saved in Selti from the last task execution so the agent can resume from where it stopped.
The saved agent state is returned for task resumption or context recovery.
Developers building MCP-based AI agents can use it to persist dialogues, documents, and states so agents retain context across sessions. This reduces repeated questioning and improves continuity in long-running tasks.
When an agent needs to find relevant information from historical materials, this tool can perform semantic storage and retrieval. It is suitable for using past conversations or documents as context for later reasoning and responses.
For agent tasks that pause or run in stages, teams can save agent states and restore them later. This helps continue workflows without reinitializing all context each time.
Selti is a persistent memory server for AI agents that works via the MCP protocol. It supports semantic storage and retrieval of dialogues, documents, and agent states.
The known prerequisite is that your system or agent needs to use the MCP protocol. For other runtime, dependency, or key requirements, see the source repository.
Based on the given description, it emphasizes being a persistent memory server for AI agents and covers dialogues, documents, and agent states. For more specific implementation differences and architecture details, see the source repository.
Give AI agents durable local memory, knowledge graph storage, and fast recall.
Give AI agents persistent long-term memory with hybrid semantic and keyword search.
Add selective persistent memory for AI agents with structured recall and decay.
Enable AI assistants to store, search, and manage persistent semantic memories.
Give AI agents local persistent memory with hybrid search and decay management.
Provide local-first persistent memory for AI agents across projects.