$ loading_
Give AI agents durable memory, observable retrieval, and governed context assembly.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "ai.multi-turn/enacta" MCP server from askskill: Run: claude mcp add --transport http 'ai-multi-turn-enacta' 'https://api.multi-turn.ai/mcp'
Design a long-term memory plan for this AI agent: store user preferences, past task conclusions, and key decision records, and explain what should be written into durable records.
A long-term memory design recommendation for an AI agent, including what information types should be persisted and recording principles.
Evaluate this agent’s memory retrieval flow, focusing on how to make retrieval observable, traceable, and easier to audit for context sources.
An observability review for retrieval, describing what process and source information should be logged.
Design context assembly rules for a multi-turn AI agent so that only relevant, governed long-term memory is injected into the current task.
A set of context assembly rules explaining how to filter, constrain, and inject long-term memory.
Developers building multi-turn AI agents can use it to keep durable records so agents can reuse past information in later tasks. It fits agent systems that need context continuity across sessions.
When a team needs to understand why an agent received certain historical context, it can rely on observable retrieval to inspect the process. This makes it easier to trace memory sources and usage.
Product or research teams designing agent behavior can use it for governed context assembly to reduce irrelevant or inappropriate information in the current task. This helps keep agent context more bounded.
It provides long-term memory for AI agents, including durable records, observable retrieval, and governed context assembly. It suits multi-turn agent scenarios that need continuity across sessions.
The provided material does not specify installation steps, runtime requirements, or API keys. See the source repository for exact prerequisites.
From the description, it emphasizes long-term memory, observable retrieval, and governed context assembly rather than simply appending text to a prompt. See the source repository for the exact mechanism.
Provide persistent memory for AI agents across sessions and tasks.
Give AI coding agents persistent memory across sessions for people, decisions, and context.
Give AI agents persistent, self-managing memory with recall and forgetting.
Give AI agents long-term memory with user-scoped storage, recall, and deletion.
Give AI agents persistent memory with semantic search and automatic memory management.
Give AI agents local-first memory to learn, consult, and retrieve related context.