Manage agent memories via Python async APIs to a StixDB server.
npx skills add https://github.com/Pr0fe5s0r/StixDB --skill engine-memory-layer
Please help me install this Agent Skill. Skill: engine-memory-layer Source: https://github.com/Pr0fe5s0r/StixDB/tree/main/skills/API Command: npx skills add https://github.com/Pr0fe5s0r/StixDB --skill engine-memory-layer
StixDBEngine provides a clean async Python API to manage memories by delegating storage, retrieval, and reasoning to a running StixDB server, enabling scalable agent memory workflows.
Install stixdb-engine, start the server with stixdb daemon start, then connect with StixDBEngine to store memories, retrieve results, and ask questions.
High-intent search queries and answers about installing and using this skill.
Agent memory management in Python async workflows is handled by delegating storage, retrieval, and reasoning to a running StixDB server. StixDBEngine provides clean async APIs to store memories, retrieve chunks, and ask questions with citations.
Async multi-hop reasoning for LLM agents is the process of streaming complex queries across stored memory collections. StixDBEngine supports streaming and multi-hop reasoning to enable scalable agent memory workflows.
To ingest files into an agent memory collection, use the StixDBEngine async API to ingest files and folders directly into memory tiers. This allows the StixDB server to process and store relevant chunks for later retrieval.
Yes, async memory management in Python requires a running StixDB server and the stixdb-engine package installed. You must start the server using the command line before connecting with the StixDBEngine client.
Retrieving relevant memory chunks with citations for LLM chat is supported by StixDBEngine. The API enables clients to ask questions and receive answers with citations, suitable for agent chat and multi-hop reasoning workflows.
To run maintenance cycles on stored agent memories, use the StixDBEngine API to trigger maintenance on your memory collections. This ensures stored data remains optimized for retrieval and multi-hop reasoning tasks.
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