https://github.com/eugen-goebel/tech-trend-agent
Multi-agent system that generates comprehensive technology trend reports
https://github.com/eugen-goebel/tech-trend-agent
anthropic automation docker docx-report market-analysis multi-agent pydantic python trend-analysis
Last synced: 4 months ago
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Multi-agent system that generates comprehensive technology trend reports
- Host: GitHub
- URL: https://github.com/eugen-goebel/tech-trend-agent
- Owner: eugen-goebel
- License: mit
- Created: 2026-03-26T21:45:36.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-03-29T04:36:55.000Z (4 months ago)
- Last Synced: 2026-03-29T07:28:17.187Z (4 months ago)
- Topics: anthropic, automation, docker, docx-report, market-analysis, multi-agent, pydantic, python, trend-analysis
- Language: Python
- Size: 50.8 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tech Trend Report Agent
An AI-powered multi-agent system that generates technology trend reports. Enter any technology — Artificial Intelligence, Blockchain, Quantum Computing — and get a professional Word document with market analysis, key players, use cases, and strategic outlook.
---
## Architecture
```
CLI Input ("Quantum Computing")
│
▼
┌─────────────────────┐
│ Orchestrator │ Coordinates the 3-phase pipeline
└────────┬────────────┘
│
┌────┴─────┐
▼ ▼
┌────────┐ ┌──────────┐
│Research│ │ Analysis │
│ Agent │ │ Agent │
└────┬───┘ └────┬─────┘
│ │
▼ ▼
┌─────────────────────┐
│ Report Generator │ → DOCX Output
└─────────────────────┘
```
| Agent | Role | Technology |
|-------|------|-----------|
| **ResearchAgent** | Gathers live web intelligence on the technology | Server-side web search, adaptive thinking |
| **AnalysisAgent** | Extracts structured insights from raw research | Structured outputs (Pydantic), adaptive thinking |
| **ReportGenerator** | Produces a professional multi-page DOCX report | python-docx with color-coded tables |
---
## Quickstart
```bash
# 1. Clone the repo
git clone https://github.com/eugen-goebel/tech-trend-agent.git
cd tech-trend-agent
# 2. Install dependencies
pip install -r requirements.txt
# 3a. Test without an API key (uses sample AI data)
python main.py --dry-run
# 3b. Full run with Anthropic API key
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env
python main.py "Quantum Computing"
python main.py "Blockchain"
python main.py "Edge Computing"
```
The report is saved to `./output/tech_trend__.docx`.
---
## Testing
```bash
# Run the full test suite (52 tests, no API key needed)
python -m pytest tests/ -v
```
The test suite covers:
- **Model validation** — Pydantic schemas, Literal constraints, serialization
- **Mock data integrity** — ensures dry-run data is complete and valid
- **Report generation** — DOCX output, section presence, table structure
- **Agent logic** — web search tool usage, pause_turn handling, structured outputs
- **CLI integration** — argument parsing, dry-run mode, error handling
---
## Example Output
Running `python main.py "Artificial Intelligence"` produces a ~10-page Word document:
---
## Project Structure
```
tech-trend-agent/
├── main.py # CLI entry point (supports --dry-run)
├── agents/
│ ├── researcher.py # Web search intelligence gathering
│ ├── analyst.py # Structured analysis (Pydantic models)
│ ├── orchestrator.py # Pipeline coordinator
│ └── mock_data.py # AI sample data for --dry-run mode
├── utils/
│ └── report_generator.py # Professional DOCX generation
├── tests/
│ ├── test_models.py # Pydantic model validation tests
│ ├── test_mock_data.py # Mock data integrity tests
│ ├── test_report_generator.py # DOCX generation tests
│ ├── test_agents.py # Agent logic tests (mocked API)
│ └── test_cli.py # CLI integration tests
├── output/ # Generated reports (git-ignored)
├── requirements.txt
└── .env.example
```
---
## Tech Stack
| Component | Technology |
|-----------|-----------|
| AI Backend | Anthropic API (claude-opus-4-6) |
| Structured Outputs | Pydantic v2 + `messages.parse()` |
| Server-side Search | `web_search_20260209` tool |
| Report Generation | python-docx |
| Testing | pytest (52 tests) |
| Thinking Mode | Adaptive thinking |
---
## Report Sections
1. **Cover Page** — Technology name, date, system branding
2. **Executive Summary** — High-level overview
3. **Technology Overview** — How the technology works
4. **Maturity Assessment** — Adoption lifecycle stage
5. **Market Landscape** — Market size, growth, regional split
6. **Key Players** — Major companies with focus areas (table)
7. **Use Cases** — Real-world applications with impact levels (color-coded table)
8. **Strengths & Limitations** — Two-column comparison table
9. **Adoption Drivers & Barriers** — Two-column comparison table
10. **Key Trends** — Current industry developments
11. **Future Outlook** — 3-5 year predictions
12. **Risk Factors** — Threats and challenges
---
## License
MIT License — see [LICENSE](LICENSE)