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ArcLogs

ArcLogs is an AI-native execution agent that eliminates daily standup meetings by turning natural language updates into real workflow automation.

Code Video Cerebral Valley

Core trick

Transcribes voice standups with Whisper, then has an LLM with sprint context call Jira APIs to comment on tickets, transition status, and flag blockers.

Tags

productivityvoiceagents

Description

ArcLogs is an AI-native execution agent that eliminates daily standup meetings by turning natural language updates into real workflow automation. Built with Opus 4.6, it processes text and voice standups in Slack, reasons over sprint and team context, and safely orchestrates actions across Jira. It auto-comments on tickets, transitions task statuses, detects blockers, routes help requests, and generates structured digests — all from unstructured input. ArcLogs is especially valuable for distributed, cross-functional teams working across time zones. Instead of forcing engineers into low-value sync meetings, teams contribute asynchronously while the system maintains alignment automatically. By combining context engineering, structured outputs, and tool orchestration, ArcLogs removes repetitive reporting and transforms standups into continuous, intelligent coordination.
samouh-waleed/ArcLogs★ 00 forksTypeScriptNOASSERTIONCreated Dec 30, 2025 · last push Mar 2, 2026

README excerpt

# ArcLogs

Slack-based async standup platform with AI-powered insights and Jira automation.

## What is ArcLogs?

ArcLogs replaces synchronous daily standups with async text and voice responses via Slack. The system transcribes audio (OpenAI Whisper), analyzes responses (GPT-4 with team/sprint context), auto-comments on Jira tickets, transitions ticket statuses, sends help request DMs, and posts team digests.

## Monorepo Structure

```
ArcLogs/
├── application/    # Next.js 16 frontend + API routes (TypeScript)
├── worker/         # Python backend worker (SQS consumer + AI processing)
├── landing/        # Landing page
└── CLAUDE.md       # Full project context for AI assistants
```

## Quick Start

### Prerequisites
- Node.js 18+
- Python 3.12+
- PostgreSQL (Neon recommended)
- AWS account (for SQS)
- OpenAI API key
- Slack workspace (for bot installation)

### Setup

1. **Clone the repository**
```bash
git clone https://github.com/samouh-waleed/ArcLogs.git
cd ArcLogs
```

2. **Configure Application**
```bash
cd application
npm install
cp .env.example .env
# Edit .env with your credentials (see .env.example for all required vars)
npm run db:push      # Push schema to Neon PostgreSQL
npm run dev          # Dev server on port 3000
```

3. **Configure Worker**
```bash
cd ../worker
uv sync              # Install dependencies
cp .env.example .env
# Edit .env with your credentials
uv run python migrations/run_migrations.py  # Setup database
uv run python main.py    # Start SQS wo

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