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328 lines
6.8 KiB
328 lines
6.8 KiB
# CLI Multi-Agent Features: 3-Step Quickstart Guide
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Run LLM Council and Heavy Swarm directly from the command line for seamless DevOps integration. Execute sophisticated multi-agent workflows without writing Python code.
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## Overview
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| Feature | Description |
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|---------|-------------|
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| **LLM Council CLI** | Run collaborative decision-making from terminal |
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| **Heavy Swarm CLI** | Execute comprehensive research swarms |
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| **DevOps Ready** | Integrate into CI/CD pipelines and scripts |
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| **Configurable** | Full parameter control from command line |
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---
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## Step 1: Install and Verify
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Ensure Swarms is installed and verify CLI access:
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```bash
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# Install swarms
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pip install swarms
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# Verify CLI is available
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swarms --help
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```
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You should see the Swarms CLI banner and available commands.
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---
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## Step 2: Set Environment Variables
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Configure your API keys:
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```bash
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# Set your OpenAI API key (or other provider)
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export OPENAI_API_KEY="your-openai-api-key"
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# Optional: Set workspace directory
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export WORKSPACE_DIR="./agent_workspace"
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```
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Or add to your `.env` file:
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```
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OPENAI_API_KEY=your-openai-api-key
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WORKSPACE_DIR=./agent_workspace
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```
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---
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## Step 3: Run Multi-Agent Commands
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### LLM Council
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Run a collaborative council of AI agents:
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```bash
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# Basic usage
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swarms llm-council --task "What is the best approach to implement microservices architecture?"
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# With verbose output
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swarms llm-council --task "Evaluate investment opportunities in AI startups" --verbose
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```
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### Heavy Swarm
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Run comprehensive research and analysis:
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```bash
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# Basic usage
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swarms heavy-swarm --task "Analyze the current state of quantum computing"
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# With configuration options
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swarms heavy-swarm \
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--task "Research renewable energy market trends" \
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--loops-per-agent 2 \
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--question-agent-model-name gpt-4o-mini \
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--worker-model-name gpt-4o-mini \
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--verbose
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```
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---
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## Complete CLI Reference
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### LLM Council Command
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```bash
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swarms llm-council --task "<your query>" [options]
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```
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| Option | Description |
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|--------|-------------|
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| `--task` | **Required.** The query or question for the council |
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| `--verbose` | Enable detailed output logging |
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**Examples:**
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```bash
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# Strategic decision
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swarms llm-council --task "Should our startup pivot from B2B to B2C?"
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# Technical evaluation
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swarms llm-council --task "Compare React vs Vue for enterprise applications"
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# Business analysis
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swarms llm-council --task "What are the risks of expanding to European markets?"
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```
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---
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### Heavy Swarm Command
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```bash
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swarms heavy-swarm --task "<your task>" [options]
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```
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| Option | Default | Description |
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|--------|---------|-------------|
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| `--task` | - | **Required.** The research task |
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| `--loops-per-agent` | 1 | Number of loops per agent |
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| `--question-agent-model-name` | gpt-4o-mini | Model for question agent |
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| `--worker-model-name` | gpt-4o-mini | Model for worker agents |
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| `--random-loops-per-agent` | False | Randomize loops per agent |
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| `--verbose` | False | Enable detailed output |
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**Examples:**
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```bash
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# Comprehensive research
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swarms heavy-swarm --task "Research the impact of AI on healthcare diagnostics" --verbose
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# With custom models
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swarms heavy-swarm \
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--task "Analyze cryptocurrency regulation trends globally" \
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--question-agent-model-name gpt-4 \
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--worker-model-name gpt-4 \
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--loops-per-agent 3
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# Quick analysis
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swarms heavy-swarm --task "Summarize recent advances in battery technology"
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```
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---
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## Integration Examples
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### Bash Script Integration
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```bash
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#!/bin/bash
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# research_script.sh
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TOPICS=(
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"AI in manufacturing"
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"Autonomous vehicles market"
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"Edge computing trends"
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)
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for topic in "${TOPICS[@]}"; do
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echo "Researching: $topic"
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swarms heavy-swarm --task "Analyze $topic" --verbose >> research_output.txt
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echo "---" >> research_output.txt
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done
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```
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### CI/CD Pipeline (GitHub Actions)
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```yaml
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name: AI Research Pipeline
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on:
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schedule:
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- cron: '0 9 * * 1' # Every Monday at 9 AM
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jobs:
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research:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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- name: Install dependencies
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run: pip install swarms
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- name: Run LLM Council
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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run: |
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swarms llm-council \
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--task "Weekly market analysis for tech sector" \
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--verbose > weekly_analysis.txt
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- name: Upload results
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uses: actions/upload-artifact@v3
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with:
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name: analysis-results
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path: weekly_analysis.txt
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```
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### Docker Integration
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```dockerfile
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FROM python:3.10-slim
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RUN pip install swarms
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ENV OPENAI_API_KEY=""
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ENV WORKSPACE_DIR="/workspace"
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WORKDIR /workspace
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ENTRYPOINT ["swarms"]
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CMD ["--help"]
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```
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```bash
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# Build and run
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docker build -t swarms-cli .
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docker run -e OPENAI_API_KEY="your-key" swarms-cli \
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llm-council --task "Analyze market trends"
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```
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---
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## Other Useful CLI Commands
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### Setup Check
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Verify your environment is properly configured:
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```bash
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swarms setup-check --verbose
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```
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### Run Single Agent
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Execute a single agent task:
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```bash
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swarms agent \
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--name "Research-Agent" \
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--task "Summarize recent AI developments" \
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--model "gpt-4o-mini" \
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--max-loops 1
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```
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### Auto Swarm
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Automatically generate and run a swarm configuration:
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```bash
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swarms autoswarm --task "Build a content analysis pipeline" --model gpt-4
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```
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### Show All Commands
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Display all available CLI features:
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```bash
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swarms show-all
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```
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---
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## Output Handling
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### Capture Output to File
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```bash
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swarms llm-council --task "Evaluate cloud providers" > analysis.txt 2>&1
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```
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### JSON Output Processing
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```bash
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swarms llm-council --task "Compare databases" | python -c "
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import sys
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import json
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# Process output as needed
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for line in sys.stdin:
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print(line.strip())
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"
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```
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### Pipe to Other Tools
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```bash
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swarms heavy-swarm --task "Research topic" | tee research.log | grep "RESULT"
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```
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---
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## Troubleshooting
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### Common Issues
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| Issue | Solution |
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| "Command not found" | Ensure `pip install swarms` completed successfully |
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| "API key not set" | Export `OPENAI_API_KEY` environment variable |
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| "Task cannot be empty" | Always provide `--task` argument |
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| Timeout errors | Check network connectivity and API rate limits |
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### Debug Mode
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Run with verbose output for debugging:
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```bash
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swarms llm-council --task "Your query" --verbose 2>&1 | tee debug.log
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```
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---
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## Next Steps
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- Explore [CLI Reference Documentation](../swarms/cli/cli_reference.md) for all commands
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- See [CLI Examples](../swarms/cli/cli_examples.md) for more use cases
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- Learn about [LLM Council](./llm_council_quickstart.md) Python API
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- Try [Heavy Swarm Documentation](../swarms/structs/heavy_swarm.md) for advanced configuration
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