Jerry Liu Portfolio
Co-founder and CEO of LlamaIndex, the data framework used to connect LLM agents to external knowledge sources; his GitHub profile shows the underlying "llamaagents" project series — scoped agent architectures for tasks like arXiv summarization, patent search, and financial statement extraction, built on LlamaIndex's retrieval and orchestration primitives.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A general framework demonstrated through many small, scoped agents
Rather than one flagship demo, the llamaagents-* repositories each apply the same orchestration primitives to a distinct real task (patents, court orders, resumes), showing the underlying architecture is genuinely reusable, not built for a single showcase.
Framework maintenance visible alongside application work
The profile shows both the core llama-hub data-loader library and applied agent projects side by side, demonstrating the same person doing infrastructure and application work rather than only one layer of the stack.
Forks of adjacent frameworks kept visible
Maintaining visible forks of DSPy and CrewAI alongside his own framework signals active engagement with how competing approaches to agent orchestration solve the same problems, not a closed-off, single-framework view.
What AI Agent Architects can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Demonstrate a general framework through several small, scoped example agents rather than one flagship demo — it proves the architecture is reusable.
Show both the infrastructure you maintain and the applications built on it in the same place — it demonstrates full-stack ownership of the pattern.
Keep visible forks or experiments with competing frameworks in your space — it signals you track the field, not just your own corner of it.
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