The Pandas ecosystem today centers on four fronts: DataFrame ergonomics, I/O performance, groupby and aggregation, and integration with the PyData stack. These Pandas experts collectively cover all of them.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Adam Rose | @adamerose | PandasGUI contributor |
 |
Damien Farrell | @dmnfarrell | pandastable contributor |
 |
Doris Lee | @dorisjlee | lux contributor |
 |
Jason Carpenter | @jmcarpenter2 | Maintains swifter |
The Keras ecosystem today centers on four fronts: the model API, distributed training, KerasNLP and KerasCV, and deployment via TFLite and TF Serving. These Keras experts collectively cover all of them.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Yang | @fighting41love | Maintains funNLP |
 |
Donne Martin | @donnemartin | Maintains data-science-ipython-notebooks |
 |
François Chollet | @fchollet | keras contributor |
 |
Aurélien Geron | @ageron | Maintains handson-ml3 |
NLP development today spans four fronts: tokenization and embeddings, language model fine-tuning, named-entity recognition and tagging, and classification and sentiment analysis. The list below highlights NLP experts working on each of those areas.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
|
Yang | @fighting41love | Maintains funNLP |
 |
spencer kelly | @spencermountain | compromise contributor |
 |
Ines Montani | @ines | spaCy contributor |
 |
Sebastian Ruder | @sebastianruder | Maintains NLP-progress |
Active Deep Learning work spans four areas: neural network architectures, distributed training infrastructure, optimization techniques, and model deployment. Below is a curated list of Deep Learning experts driving those areas.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
scutan90 | @scutan90 | Maintains DeepLearning-500-questions |
 |
Lutz Roeder | @lutzroeder | netron contributor |
 |
Flood Sung | @floodsung | Maintains Deep-Learning-Papers-Reading-Roadmap |
| Ashish Patel | @ashishpatel26 | Maintains 500-AI-Machine |
The MCP ecosystem today centers on four fronts: server and client SDKs, tool and resource definitions, transport (stdio/SSE), and integrations with hosts. These MCP experts collectively cover all of them.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Lee Stott | @leestott | mcp-for-beginners contributor |
 |
Ironben | @nanbingxyz | 5ire contributor |
 |
Cameron Cooke | @cameroncooke | XcodeBuildMCP contributor |
 |
Stephen Akinyemi | @appcypher | Maintains awesome-mcp-servers |
The LangGraph ecosystem today centers on four fronts: graph-based agent design, state management, persistence and checkpointing, and human-in-the-loop. These LangGraph experts collectively cover all of them.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
bsorrentino | @bsorrentino | langgraph4j contributor |
 |
Mayo Oshin | @mayooear | Maintains ai-pdf-chatbot-langchain |
 |
Henry Krupp | @doobidoo | mcp-memory-service contributor |
 |
Nuno Campos | @nfcampos | langgraph contributor |
Active LlamaIndex work spans four areas: data connectors and indexes, retrieval engines, agents and tools, and observability and tracing. Below is a curated list of LlamaIndex experts driving those areas.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Logan | @logan-markewich | llama_index contributor |
 |
Jerry Liu | @jerryjliu | llama_index contributor |
 |
Denis Rothman | @Denis2054 | RAG-Driven-Generative-AI contributor |
 |
Marcus Schiesser | @marcusschiesser | LlamaIndexTS contributor |
Claude development today spans four fronts: prompting with Claude, tool use and Computer Use, prompt caching, and safety patterns. The list below highlights Claude experts working on each of those areas.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Yagil Burowski | @yagil | ChatIDE contributor |
 |
Vaibhav kumar | @vaibhavk97 | Poe contributor |
 |
Aaron Russell | @aaronrussell | anthropix contributor |
 |
Vishal Mysore | @vishalmysore | choturobo contributor |
LLM today is shaped by four threads: prompt engineering, model selection, inference optimization, and evaluation patterns. The LLM experts below lead work across each.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Jun Siang Cheah | @cheahjs | Maintains free-llm-api-resources |
 |
Jun Kim | @jundot | Maintains omlx |
 |
Eric Buehler | @EricLBuehler | mistral.rs contributor |
 |
Georgi Gerganov | @ggerganov | llama.cpp contributor |
| Phillip LeBla |
Active Agentic AI work spans four areas: agent design patterns, tool use and function calling, multi-agent orchestration, and observability and safety. Below is a curated list of Agentic AI experts driving those areas.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
Shubham Saboo | @Shubhamsaboo | Maintains awesome-llm-apps |
 |
Andrej | @karpathy | Maintains autoresearch |
 |
Mario Zechner | @badlogic | pi contributor |
 |
Magnus Müller | @MagMueller | browser-use contributor |