$ loading_
Give AI agents portable memory with temporal and semantic retrieval scoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Kontext MCP Server" yet — see the docs or source repo.
Explain how to integrate Kontext MCP Server into my AI agent to store past conversations, tool results, and user preferences, and provide a recommended write and retrieval flow.
An integration plan covering when to write memory, how to retrieve it, how to assemble context, and implementation guidance.
I want my agent to prioritize memories that are both recent and semantically relevant. Help me design a retrieval strategy using time decay and semantic scoring, and explain how to tune the parameters.
An actionable retrieval scoring design with time and semantic weights plus parameter tuning recommendations.
Help me design a provider-agnostic memory layer so different LLMs or agent frameworks can share the same contextual memory, and describe the data schema and interface contract.
A reusable memory-layer design including storage schema, interface definitions, sharing mechanisms, and compatibility guidance.
Give LLMs persistent semantic memory and vector search for better context continuity.
Manage LLM context, compact conversations, and retain durable semantic memory.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Give AI agents persistent memory and semantic retrieval across conversations.
Centralize memory, tools, and logic for leaner prompts and scalable AI agents.
Add session-scoped memory so assistants can store and retrieve conversation context.