𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗠𝗲𝗺𝗼𝗿𝘆 𝘃𝘀. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲
Most AI agents only have memory. Memory means they remember your name or your preferences. This is not enough.
To add real value, an agent needs knowledge.
Memory is "I saw this." Knowledge is "I can use this."
Most frameworks use context caching. They store chat logs or simple key-value pairs. This is not a knowledge base. An agent with only memory cannot learn from a website, a video, or a technical book. It lacks a collection layer.
Build your agent with these three layers:
- Collection Layer: Pulls data from web pages, videos, documents, and books.
- Analysis Layer: Uses AI to summarize, extract keywords, and check facts.
- Storage Layer: Organizes data into three tiers.
The three storage tiers work like this:
- Hot Tier (Hermes Memory Tool): Uses nanosecond speed for exact key-value retrieval.
- Warm Tier (Hindsight Vector DB): Uses millisecond speed for semantic similarity.
- Cold Tier (gbrain Knowledge Graph): Uses second speed for complex reasoning and links.
When you search, the system follows a fallback path. It starts with FTS5. If that fails, it moves to Hindsight. If that fails, it moves to gbrain. This saves you LLM tokens by stopping as soon as it finds a match.
Example workflow for a video:
- Download video via yt-dlp.
- Convert speech to text via Whisper.
- Extract text from frames using OCR.
- Use LLM to create a structured summary.
- Save to the knowledge graph.
One command can trigger this whole chain.
Stop building agents that only remember what you said. Build agents that absorb information from the world. When an agent connects new web data to your existing knowledge, it starts to truly understand.
Source: https://dev.to/manoir_yantai_f22f01340f0/ai-dai-li-de-ji-yi-kun-jing-cong-ji-zhu-dao-zhi-dao--2g9n
Optional learning community: https://t.me/GyaanSetuAi