parallel-agents

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Parallel Agent Orchestration

AI & Automation 3,795 stars 297 forks Updated 4 months ago MIT

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# Parallel Agent Orchestration When launching multiple agents in parallel, follow this pattern to avoid context bloat. ## Core Principles 1. **No TaskOutput calls** - TaskOutput returns full agent output, bloating context 2. **Run in background** - Always use `run_in_background: true` 3. **File-based confirmation** - Agents write status to files, not return values 4. **Append, don't overwrite** - Multiple agents can write to same status file ## Output Patterns ### Simple Confirmation (parallel batch work) For tasks where agents just need to confirm completion: ```bash # Agent writes to shared status file echo "COMPLETE: <task-name> - $(date)" >> .claude/cache/<batch-name>-status.txt ``` - Use `>>` to append (not `>` which overwrites) - Include timestamp for ordering - One line per agent completion - Check with: `cat .claude/cache/<batch-name>-status.txt` ### Detailed Output (research/exploration) For tasks requiring detailed findings: ``` .claude/cache/agents/<task-type>/<agent-id>/ ├── output.md # Main findings ├── artifacts/ # Any generated files └── status.txt # Completion confirmation ``` - Each agent gets own directory - Full output preserved for later reading - Status file still used for quick completion check ## Task Prompt Template ```markdown # Task: <TASK_NAME> ## Your Mission <clear objective> ## Output When done, write confirmation: \`\`\`bash echo "COMPLETE: <identifier> - $(date)" >> .claude/cache/<batch>-status.txt \`\`\` Do NOT retu...

Details

Author
parcadei
Repository
parcadei/Continuous-Claude-v3
Created
5 months ago
Last Updated
4 months ago
Language
Python
License
MIT

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