Aslan
@aslandizaji.bsky.social
330 followers 1.3K following 65 posts
Artificial Intelligence, Machine Learning, Neuroscience, Complex Systems, Economics. PhD Student at the University of Tehran. Cofounder: @AutocurriculaLab, @NeuroAILab, @LangTechAI. https://sites.google.com/a/umich.edu/aslansdizaji/
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aslandizaji.bsky.social
Finally, this app was inspired by two courses from @DeepLearningAI and @LangChainAI Academy. I would like to thank them!
aslandizaji.bsky.social
The results?
📊 Final answers with explanations
📈 Automatically generated charts
📝 Full trace of every step the system took
It’s like having a research assistant that works systematically and shows its work!
aslandizaji.bsky.social
My app combines all of this into a multi-agent framework:
A Planner breaks down your research question
Specialized agents (web researcher, chart generator, summarizer) handle subtasks
Everything is orchestrated into a transparent workflow you can trace
aslandizaji.bsky.social
Traditional LLM agents can be “shallow” — they just loop through tools and struggle with long, complex tasks.
Deep Agents (a new feature in LangGraph) bring:
✅ Task planning (TODOs)
✅ Sub-agent delegation
✅ Context offloading to files
✅ Robust reasoning prompts
aslandizaji.bsky.social
It’s a Streamlit app powered by LangGraph Deep Agents that can take your research question, plan a workflow, fetch data, generate charts, and explain results step by step.
aslandizaji.bsky.social
Big thanks to @DeepLearningAI & @LangChainAI Academy for the resources that made this possible.
aslandizaji.bsky.social
Multi-Modal RAG App (Streamlit + Ollama)
Built a lightweight Retrieval-Augmented Generation (RAG) system that processes both text + image docs. Users can load files, build a vector store, and run retrieval-grounded QA.
github.com/aslansd/mult...
GitHub - aslansd/multi-modal-rag-web: A Multi-Modal RAG Application Built with Streamlit Using a Lightweight Ollama Model
A Multi-Modal RAG Application Built with Streamlit Using a Lightweight Ollama Model - aslansd/multi-modal-rag-web
github.com
aslandizaji.bsky.social
Streamlit App for Open Deep Research
Adapted LangChain’s Open Deep Research (LangGraph) into a Streamlit app to support advanced research workflows: ingestion, retrieval, multi-modal analysis, and context-aware Q&A.
github.com/aslansd/open...
rneaovknvzddyykhkeu2et.streamlit.app
GitHub - aslansd/open_deep_research_web: A Streamlit App for open_deep_research Framework of LangChain/LangGraph
A Streamlit App for open_deep_research Framework of LangChain/LangGraph - aslansd/open_deep_research_web
github.com
aslandizaji.bsky.social
Over the past two weeks at LangTechAI, I built 3 generative AI apps on top of LangChain / LangGraph frameworks — inspired by courses from @DeepLearningAI & @LangChainAI Academy.
aslandizaji.bsky.social
Feel free to use any one of the above apps and I would be happy to receive any feedback.
aslandizaji.bsky.social
These three projects were not possible without taking the online courses offered by DeepLearningAI and LangChain Academy. Here, I would like to thank them. I would like to thank CrewAI, Gradio, Ollama, and TogetherAI too.
aslandizaji.bsky.social
cycles. It will provide the user a final markdown summary with all sources used.
aslandizaji.bsky.social
(via seven different search APIs: DuckDuckGo, Tavily, Perplexity, Linkup, Exa, ArXiv, and PubMed), summarise the results of web search, reflect on the summary to examine knowledge gaps, generate a new search query to address the gaps, search, and improve the summary for a user-defined number of
aslandizaji.bsky.social
In the third project, I extended one of the LangChain apps called Ollama Deep Researcher which is a local web research assistant built upon the multi-agent framework of LangGraph that uses any LLM hosted by Ollama. It accepts a topic and it will generate a web search query, gather web search results
aslandizaji.bsky.social
In the second project, I built a Dungeon game simulating a fantasy world composed of kingdoms, towns, characters, and inventories powered by one of the LLMs provided by TogetherAI and used Gradio as a user interface. Again all the codes and results are brought in one notebook.
aslandizaji.bsky.social
given by the user for their startup considering their expertise.For this purpose, I used the multi-agent framework of CrewAI combining it with LangChain, Gradio, and one of the open source LLMs of Ollama. All the codes and results are provided in a notebook.
aslandizaji.bsky.social
In the first project, I simulated an environment similar to a startup having three cofounders: the first cofounder is more technical, the second cofounder is more product oriented, and the third cofounder is more business one. These three cofounders do brainstorming about various topics