[Hands-on] Build Your Own AI Avatar With Human-like Memory
Strands Agents is an open-source SDK designed for building agent harnesses rather than just the agents themselves. It provides developers with tools for end-to-end control over agent behavior, including what actions are taken and when they occur. The framework emphasizes managing the scope and limits of agent operations to ensure controlled execution. This section introduces an open-source solution for building real-time AI avatars that utilize knowledge graphs for memory. It addresses the limitations of NaiveRAG and traditional GraphRAG by combining vector search speed with knowledge graph intelligence. The system is powered by Zep's Graphiti framework and leverages optimized retrieval algorithms to maintain real-time performance. This section outlines nine distinct projects utilizing the Model Context Protocol (MCP) to enhance AI applications. The projects range from building local MCP clients and agentic RAG systems to creating specialized agents for stock market analysis and voice-activated database queries. It also highlights integrations with tools like Cursor, Claude Desktop, MindsDB, and SDV for tasks such as unified data access and synthetic data generation.
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Strands Agents: The open source agent harness SDK
Strands Agents is an open-source SDK designed for building agent harnesses rather than just the agents themselves. It provides developers with tools for end-to-end control over agent behavior, including what actions are taken and when they occur. The framework emphasizes managing the scope and limits of agent operations to ensure controlled execution.
- Strands Agents is an open-source SDK for building agent harnesses.
- The tool focuses on providing end-to-end control over agent actions and timing.
- It allows developers to define the operational boundaries and limits of an agent.
- AWS is a partner for this specific announcement or section.
Build your own AI Avatar with human-like memory
This section introduces an open-source solution for building real-time AI avatars that utilize knowledge graphs for memory. It addresses the limitations of NaiveRAG and traditional GraphRAG by combining vector search speed with knowledge graph intelligence. The system is powered by Zep's Graphiti framework and leverages optimized retrieval algorithms to maintain real-time performance.
- NaiveRAG lacks document connections, while traditional GraphRAG is often too slow for real-time applications.
- The featured AI Avatar uses a knowledge graph as its memory to provide context and understanding in real-time conversations.
- Zep's Graphiti framework is open-source under the Apache 2.0 license and supports self-hosting.
- The system achieves low latency using fine-tuned Qwen3 and Gemma models with sub-10ms embedding and sub-50ms reranking.
- Retrieval is optimized using S3 and hot caching for dense vector and BM25 search instead of traditional vector databases.
9 MCP projects for AI engineers
This section outlines nine distinct projects utilizing the Model Context Protocol (MCP) to enhance AI applications. The projects range from building local MCP clients and agentic RAG systems to creating specialized agents for stock market analysis and voice-activated database queries. It also highlights integrations with tools like Cursor, Claude Desktop, MindsDB, and SDV for tasks such as unified data access and synthetic data generation.
- MCP clients act as components in AI apps to establish connections with external tools.
- Agentic RAG systems can be built with MCP to fallback to web search when vector database results are insufficient.
- MindsDB can be integrated via MCP to provide a natural language interface for over 200 data sources.
- A common memory layer can be implemented to share context between independent tools like Claude Desktop and Cursor.
- The SDV library can be used within an MCP server to generate realistic tabular synthetic datasets.
- Local alternatives to ChatGPT's deep research feature can be developed using MCP frameworks.