shihanwan/memonto
Augment AI agents with long-term memory through knowledge graph ðŸ§
This tool helps AI agents remember past interactions and relationships between different pieces of information, enabling them to provide more accurate and context-aware responses. It takes unstructured text as input, extracts relevant details based on a predefined structure, and stores them in a knowledge graph. The output is either intelligent summaries or raw data about what the AI agent has 'learned.' This is for anyone building or managing AI agents, especially those needing to maintain context over long conversations or complex tasks.
No commits in the last 6 months.
Use this if you need your AI agents to have a persistent, structured memory that allows them to understand context, recall details from previous interactions, and improve their performance over time.
Not ideal if your AI agent's task is simple and doesn't require remembering past conversations or understanding complex relationships between pieces of information.
Stars
93
Forks
12
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 16, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/agents/shihanwan/memonto"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Featured in
Higher-rated alternatives
kayba-ai/agentic-context-engine
🧠Make your agents learn from experience. Now available as a hosted solution at kayba.ai
MemMachine/MemMachine
Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory...
knowns-dev/knowns
The memory layer for AI-native development — giving AI persistent understanding of your software...
rexdivakar/HippocampAI
HippocampAI — Autonomous Memory Engine for LLM Agents
Dicklesworthstone/cass_memory_system
Procedural memory for AI coding agents: transforms scattered session history into persistent,...