Assaf Elovic Portfolio
Founder of Tavily and creator of GPT Researcher, an open-source autonomous research agent (29,000+ GitHub stars) that plans a research task, spawns sub-queries, and synthesizes cited findings; also built Tovana, a memory-management library specifically for giving agents persistent, context-aware recall across sessions.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A named architecture for a specific agent capability
GPT Researcher decomposes "research a topic" into sub-query generation, parallel retrieval, and cited synthesis — a specific, reusable planning pattern for open-ended information tasks, not a generic chatbot wrapper.
A standalone project focused only on agent memory
Building Tovana as its own library, separate from the research-agent product, treats memory as a distinct architectural problem worth solving in isolation rather than bolting ad hoc context storage onto one agent.
Protocol-level integration work alongside the agent itself
GPTR-MCP exposes the research agent through the Model Context Protocol, showing attention to how an agent should be composed into other systems, not just how it works standalone.
What AI Agent Architects can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Give your agent's planning strategy a specific name and describe the steps precisely — a described pattern is more reusable than "it uses an LLM to research things."
If you solve agent memory or state management well, consider extracting it into its own library — it's a distinct hard problem worth solving independently of any one agent.
Expose your agent through a standard protocol (like MCP) so it composes into other systems, not just as a standalone product.
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