Mininglamp-OSS

Mininglamp-OSS / octo-smart-summary

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LLM-powered conversation summarisation service for OCTO — turns group chats and threads into structured briefs with key decisions, open questions, and follow-up candidates. Supports any OpenAI-compatible LLM backend.

github.comMininglamp-OSS apache-2 chat-summary conversation-intelligence golang llm
14
2
85% credibility
Found May 17, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

OCTO Smart Summary is an AI-powered conversation summarization service that transforms long team discussions -- like group chats, threads, and meeting transcripts -- into scannable, structured briefs. It uses a configurable AI model to read through messages, identify key decisions, extract open questions, and suggest follow-up actions. The service supports both personal summaries (for one person catching up) and team summaries (merging multiple participants' perspectives). It outputs structured JSON so other tools can use the data directly. Part of the larger OCTO open workplace platform, it emphasizes local-first data handling and human-AI collaboration.

How It Works

1
💬 You discover you missed a long team discussion

You return from vacation to find hundreds of messages in your team channels and realize there's no way to catch up quickly.

2
🔗 You connect OCTO Smart Summary to your workplace

Your team sets up the summary service by connecting it to your chat platform, so it can read conversations on your behalf.

3
✨ You ask for a summary of a specific conversation

You pick a group chat, meeting thread, or time period and ask the AI to create a summary -- like handing a stack of notes to a smart assistant.

4
🤖 The AI reads through every message carefully

Behind the scenes, the service fetches all messages from your selected channels, groups them by topic, and uses AI to identify what's important.

5
Your team has different involvement levels
👤
Personal summary for one person

If you're the only one reviewing, you get your own personalized summary showing your key contributions and decisions.

👥
Team summary with multiple people

Each team member creates their own mini-summary, then the AI combines them into one team document with everyone's input.

✅ You receive a scannable brief with clear action items

Instead of scrolling through hundreds of messages, you get a clean summary showing key decisions made, open questions still unanswered, and suggested follow-up actions assigned to specific people.

7
✏️ You can edit or regenerate if needed

If something feels incomplete, you can tweak the summary or regenerate it -- and share the final result with others who also missed the conversation.

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AI-Generated Review

What is octo-smart-summary?

This is a Go service that automatically generates structured summaries from group chats and meeting threads. Give it a conversation ID and it returns a JSON brief with key decisions, unanswered questions, and suggested follow-up actions. The output feeds directly into task management tools as draft todos. It works with any OpenAI-compatible LLM endpoint, so you can use self-hosted models or commercial APIs without code changes.

Why is it gaining traction?

The structured output is the selling point. Instead of dumping prose, you get strict JSON that downstream apps can render natively. The by-person mode is interesting: participants receive summaries, can accept or decline, edit their section, and submit it before a meta-summary gets generated. That workflow handles multi-party conversations better than simple summarization tools. The citation tracking back to original messages is also practical for accountability. Being able to point it at vLLM, Ollama, or Claude without touching the code removes vendor lock-in concerns.

Who should use this?

Backend teams building workplace chat platforms who want to add AI summarization without rolling their own LLM integration. Product managers running recurring team syncs who need automated briefs. Anyone tired of manually extracting action items from long threads. Not ideal for simple use cases where a single API call to an LLM would suffice.

Verdict

The architecture is clean and the feature set covers real workflows, but with 14 stars and limited community signals, this is early-stage software. The 0.85% credibility score reflects that maturity gap. Worth evaluating if you're already in the OCTO ecosystem or need the multi-participant workflow, but treat it as a reference implementation rather than production-ready without thorough testing.

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