Based on the search results and your question about external memory storage and agent systems, here’s a structured breakdown: --- ### **1. External Memory Storage Solutions** These tools/data platforms could help expand your system’s capabilities: - **Memori (GitHub/GibsonAI)**: - Open-source SQL-native memory engine for AI agents. - Reduces costs and complexity compared to vector databases. - Focuses on persistent memory for agents (ideal for your use case). - [Link](https://github.com/GibsonAI/memori) - **mem0 (GitHub/mem0ai)**: - Universal memory layer for AI agents. - Stores user preferences and learned data over time. - Lightweight and secure, suitable for proxy environments. - [Link](https://github.com/mem0ai/mem0) - **MemVerge’s MemMachine**: - Open-source memory layer for LLMs with long-context support. - Cross-platform compatibility. - [Link](

Blocks and Files

MemVerge unveils open source AI memory layer for LLMs

MemVerge has launched an open source MemMachine software project to provide a cross-platform and long-context memory layer for LLMs.

- **Redis/Valkey/Amazon MemoryDB**: - High-performance in-memory databases (Redis is widely used for caching and real-time data). - AWS MemoryDB is Redis/Valkey compatible and scalable for cloud-based proxy environments. - [AWS MemoryDB](https://aws.amazon.com/memorydb/) --- ### **2. Proxy Environment & Sub-Agent Feasibility** - **Technical Viability**: - Storing memory externally (e.g., Memori/mem0) would allow sub-agents to write/update data in a centralized database, avoiding token limits or local storage constraints. - A proxy environment could host these tools (e.g., self-hosted Memori or Redis on AWS/Aiven). - **Sub-Agent Workflow**: - Sub-agents could handle discrete tasks (e.g., image generation, prompt splitting) and report results to a central agent via API calls to the external memory system. - Example: A sub-agent generates an image using Venice.ai, stores metadata in Memori, and the main agent aggregates outputs. --- ### **3. Token/Quota Limitations** - **Current Constraints**: - My tools (e.g., Venice.ai, image generation) may have quotas, but **external databases/models** (Memori, Redis) aren’t limited by the same constraints. - Token limits would only apply if using LLM APIs (e.g., GPT), not for database storage or open-source tools. --- ### **Next Steps** 1. **Immediate Action**: - Explore **Memori** or **mem0** for lightweight, agent-specific memory storage. - Test **Amazon MemoryDB** or **Aiven** for a secure, scalable proxy environment. 2. **Custom Development**: - Build a backend system to coordinate sub-agents, using APIs to Memori/Redis for memory management. Would you like me to dive deeper into one of these tools or outline a technical plan for sub-agent integration?