Based on the search results and your question about external memory storage and agent systems, here’s a structured breakdown:
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### **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](https://blocksandfiles.com/2025/09/24/memverges-ambitious-long-context-ai-memmachine-memory/)
- **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/)
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### **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.
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### **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.
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### **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?
