Appendix: file inventory
The public workshop repo has two Python projects. The ingestion project:
flow/
├── activities.py
├── create_index.py
├── main.py
├── notebook.ipynb
├── pyproject.toml
├── utills.py
├── uv.lock
├── worker.py
└── workflow.py
The agent project:
agent/
├── agent.ipynb
├── agent.py
└── tools.py
The repo also includes:
data/
├── D2rw52SOFfM.txt
├── ...
README.md
pipeline.md
temporal-install.md
workflow.py
data/ contains cached transcripts. pipeline.md contains an early plain
Python version of the ingestion workflow. temporal-install.md contains
Temporal CLI install notes for multiple platforms.
Learning in public
After you build the project, share what you learned. You do not need a perfect announcement. Write down what you learned while the details are fresh.
A LinkedIn-style post can be short:
I completed the Deep Research Agent workshop with Temporal.
I built an end-to-end AI research agent that:
- fetches transcripts from YouTube videos,
- indexes them in Elasticsearch for search,
- uses Pydantic AI to conduct multi-stage research,
- runs ingestion and agent workflows durably with Temporal.
The main things I learned:
- how to build reliable workflow activities,
- how to make transcript ingestion retryable,
- how to give an agent search and summarization tools,
- how Temporal helps long-running AI workflows survive failures.
Code: <your repo link>
Workshop materials: https://github.com/alexeygrigorev/workshops/tree/main/temporal.io
A shorter post can focus on the system:
Built a durable AI research agent over podcast transcripts.
The system:
- downloads YouTube transcripts,
- indexes them in Elasticsearch,
- searches with a Pydantic AI agent,
- summarizes long transcripts with a sub-agent,
- runs through Temporal for retries and durable execution.
Code: <your repo link>
Additional resources
Useful references:
- Temporal 101: Getting Started with Temporal
- Building Durable AI Applications with Temporal
- Temporal Deep Research documentation
- Pydantic AI Temporal integration
- Temporal Python SDK
- YouTube Transcript API
- Elasticsearch Python client
- DataTalks.Club podcasts
Workshop code
Use the workshop repo if you want to compare your local files:
Use the repo when you want to compare your files with the final code.
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