Welcome to momo-research. This is a public research log for Context Engineering and Persistent Memory for AI agents.
This repository serves as a archive of insights from papers focused on:
- Context engineering
- Long-term and persistent memory for agents
- Memory architectures
- Unified organizational memory
- Agent workflows and memory-aware system design
Our goal is to translate complex technical research into actionable insights that help developers, builders, and researchers understand how AI agents can leverage memory more effectively.
We will continuously update this repository with:
Concise breakdowns of key papers, including:
- Google’s whitepaper on Context Engineering
- Manus’s blog on Context Engineering for AI Agents
- Chroma's blog on Context Rot
- The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
- Google's Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
- Multi-Agent Collaboration via Evolving Orchestration
- CodeAct: Executable Code Actions Elicit Better LLM Agents
- Recursive Language Models
Let me know if there are other memory related papers.
We believe the next generation of AI agents won’t be defined by model size,
but by their ability to remember, integrate, and act on information across many apps over time.
This repository documents our process as we:
- Study foundational research
- Extract the core principles of context engineering
- Apply these insights to build real memory-as-a-tool modules for modern applications
Our goal is to push forward the understanding and implementation of persistent memory systems and openly share what we learn along the way.
As we develop memory-as-a-tool modules for apps such as Slack, Gmail, and Linear, we will share:
- Architecture sketches
- Design rationales
- Lessons learned
We are also building a lightweight Memory Playground where developers can test these modules, inspect how memory is stored and retrieved, and experiment with different context-engineering strategies in a real environment.
Follow progress here:
- Product website: https://usemomo.com
- X: https://x.com/cailynyongyong
If you're researching similar topics or want to collaborate, feel free to:
- Open an issue
- Submit a pull request
- Reach out directly