feat: showcase text generation and thinking steps from suggested prompts #37
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jonigl
reviewed
Dec 18, 2025
Comment on lines
72
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| # The second example shows detailed thinking steps similar to tool calls | ||
| else: | ||
| streamer.append( | ||
| chunks=[ | ||
| MarkdownTextChunk( | ||
| text="Hello.\nI have received the task. ", | ||
| ), | ||
| MarkdownTextChunk( | ||
| text="This task appears manageable.\nThat is good.", | ||
| ), | ||
| TaskUpdateChunk( | ||
| id="001", | ||
| title="Understanding the task...", | ||
| status="in_progress", | ||
| details="- Identifying the goal\n- Identifying constraints", | ||
| ), | ||
| TaskUpdateChunk( | ||
| id="002", | ||
| title="Performing acrobatics...", | ||
| status="pending", | ||
| ), | ||
| ], | ||
| ) | ||
| time.sleep(4) | ||
|
|
||
| streamer.append( | ||
| chunks=[ | ||
| TaskUpdateChunk( | ||
| id="001", | ||
| title="Understanding the task...", | ||
| status="complete", | ||
| details="\n- Pretending this was obvious", | ||
| output="We'll continue to ramble now", | ||
| ), | ||
| TaskUpdateChunk( | ||
| id="002", | ||
| title="Performing acrobatics...", | ||
| status="in_progress", | ||
| ), | ||
| ], | ||
| ) | ||
| time.sleep(4) |
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Nice! I've been experimenting with something similar on my own, I like this idea. Featuring what is the agent doing is great like (tools calls or thinking steps)
srtaalej
approved these changes
Dec 18, 2025
listeners/events/app_mentioned.py
Outdated
| ) | ||
|
|
||
| returned_message = call_llm([{"role": "user", "content": text}]) | ||
| returned_message = call_llm(text) |
Member
Author
|
📸 Demo with current changes! Timeline with tool calls and LLM call dice.movPlan with mocked data plan.mov |
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Type of change
Summary
This PR showcases "chunks" in a chat stream. The example is meant to be quick to understand but perhaps not so meaningful. Related: slackapi/python-slack-sdk#1809
Requirements