@joshuaswarren/openclaw-engram
Implement local-first, persistent memory for your OpenClaw AI agents using Engram. It leverages LLMs for intelligent information extraction, stores data in markdown, and uses hybrid search (QMD) for efficient retrieval, enhancing agent context and learning.
5 Steps
- 1
Install OpenClaw Engram: Add the OpenClaw Engram library to your project dependencies. This makes the memory plugin available for your OpenClaw agents.
- 2
Initialize Engram Memory: Instantiate the Engram memory plugin within your OpenClaw agent's setup. Specify a directory for local memory storage.
- 3
Enable LLM-Powered Extraction: Configure your agent to feed relevant interactions into Engram. The plugin uses an integrated LLM to intelligently extract and summarize key information for storage.
- 4
Store Memories in Markdown: Engram automatically stores extracted memories in human-readable markdown files within your specified `memory_path`. Periodically inspect these files to understand your agent's knowledge base.
- 5
Retrieve Context with Hybrid Search: Query Engram for relevant past memories. It employs QMD for hybrid (keyword + semantic) search, ensuring efficient and contextually accurate retrieval for your agent's current tasks.
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