𝗚𝗶𝘃𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔 𝗣𝗲𝗿𝗺𝗮𝗻𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆
Every new AI session starts from zero.
You lose your research from yesterday. You lose your analysis from last week. You lose your mistakes from last month. Most memory solutions lock your data to one specific agent. If you switch agents, you lose everything.
Knowledge-and-Memory-Management v0.0.2 (KMM) solves this. It acts as an external memory layer. It does not touch your agent core code.
KMM works through three steps:
- Knowledge Collection
- Structured Storage
- Cross-session Retrieval
The system collects data from four sources:
- Web: Uses six engines to bypass protection and scrape websites.
- Video: Uses eight tools to pull data from YouTube or TikTok. It uses Whisper for speech and OCR for text on screen.
- Articles: Pulls from social media and RSS feeds.
- Documents: Scans PDFs and images via OCR.
The system turns raw data into assets. It takes raw material, uses an LLM to extract key points, writes it into Markdown notes, and saves it to a knowledge graph.
You can search across different domains at once. The system searches local notes, databases, semantic memory, and knowledge graphs. It uses an algorithm to merge these results. If one source fails, the others take over.
This setup fits you if:
- You use multiple agents like Hermes, Codex, or Claude Code.
- You need to reuse knowledge across different sessions.
- You want a flexible system that works via a single environment variable.
The trade-off is complexity. You need PostgreSQL 16, Hindsight, and gbrain to run it. This is harder to deploy than simple files. However, you get much higher retrieval quality through three layers of memory.
For production, run a maintenance script to automate archiving and indexing. Use a sidecar check script to verify your setup after installation.
Optional learning community: https://t.me/GyaanSetuAi