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SESSION_SUMMARY_2026-06-24

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PAI Session Summary - June 24, 2026

Objectives Completed

1. ✅ Documented PAI Daemon Architecture

File: ENGINE_DAEMON_ARCHITECTURE.md (27KB)

Comprehensive documentation of PAI’s 47 systemd services organized into 4 daemon patterns:

  • Pure Daemons (18): Background services (Qdrant, Ollama, SSH, Docker, etc.)
  • Timed Daemons (12): Scheduled tasks (backups, monitoring, cleanup)
  • Interactive Services (12): User-initiated (Claude CLI, Codex, Antigravity)
  • Hybrid Services (5): Both modes (ICM, Dashboard, Herdr, Conductor, Legion)

Key insight: PAI uses a hybrid interactive/daemon model that balances automation with on-demand responsiveness.

2. ✅ Created Daemon Management Guide

File: DAEMON_MANAGEMENT_QUICKREF.md (13KB)

Quick reference covering:

  • Essential systemd commands
  • Service templates
  • Troubleshooting workflows
  • Logging best practices
  • Timer management
  • Resource limits

3. ✅ Investigated Ollama Timeouts

File: OLLAMA_TIMEOUT_INVESTIGATION.md (19KB)

Root cause identified: Disk 99% full prevented model decompression
Solution: Freed 36GB (Docker images, apt cache, logs)
Result: Disk usage 99% → 92%, Ollama operational
Status: Verified working (math ✅, classification ✅, routing ✅)

Created automated cleanup script: ollama-disk-cleanup.sh

4. ✅ Compared OpenRouter vs OpenCode

Files:

  • OPENROUTER_VS_OPENCODE.md (24KB) - Detailed comparison
  • OPENROUTER_INTEGRATION_GUIDE.md (16KB) - 3-phase integration plan

Key findings:

  • OpenRouter: API gateway for 400+ models, good for failover
  • Minimal cost savings (~3% via OpenRouter Pro credits)
  • Adds latency overhead (extra API hop)
  • Best use: Fallback routing, not primary dispatch

Recommendation: Keep current architecture, optionally add OpenRouter as tertiary fallback

5. ✅ Assessed Ollama Capabilities

File: OLLAMA_CAPABILITY_ASSESSMENT.md (16KB)

Detailed quality scores across 11 task types:

  • Excellent (9-10/10): Classification, JSON extraction, scoring
  • Good (7-8/10): Summarization, simple reasoning
  • Acceptable (5-6/10): Code generation (simple)
  • Poor (3-4/10): Complex reasoning, multi-turn

Cost savings: $225/month vs all-cloud
Current routing: 60% local free, 40% cloud paid

6. ⚠️ Attempted vLLM Installation

File: VLLM_INSTALLATION_RESULT.md (7KB)

Status: Installation successful, deployment blocked

What worked:

  • ✅ Python venv created
  • ✅ vLLM 0.23.0 installed
  • ✅ Qwen2.5-7B model downloaded (15GB)
  • ✅ Systemd service configured

What failed:

  • ❌ GPU out of memory (7.2GB model > 8GB available)
  • ❌ Cannot run alongside Ollama (GPU conflict)
  • ❌ Benchmark not possible (both need to run)

Root cause: Hardware constraints - RTX 3060 (6GB) + RTX 4060 (8GB) insufficient for 7B model with overhead

Key Deliverables

Documentation Created (10 files, 153KB total)

  1. ENGINE_DAEMON_ARCHITECTURE.md - 47 systemd services, daemon patterns
  2. DAEMON_MANAGEMENT_QUICKREF.md - Operations guide
  3. OLLAMA_TIMEOUT_INVESTIGATION.md - Disk exhaustion forensics
  4. OLLAMA_STATUS_VERIFIED.md - Working after cleanup
  5. OLLAMA_CAPABILITY_ASSESSMENT.md - Task quality matrix
  6. OPENROUTER_VS_OPENCODE.md - Platform comparison
  7. OPENROUTER_INTEGRATION_GUIDE.md - 3-phase integration
  8. VLLM_VS_OLLAMA_BENCHMARK.md - Installation guide
  9. VLLM_INSTALLATION_RESULT.md - OOM findings
  10. SESSION_SUMMARY_2026-06-24.md - This file

Scripts Created (3 files)

  1. ollama-disk-cleanup.sh - Automated disk space recovery
  2. vllm-install.sh - vLLM installation automation
  3. vllm-benchmark.sh - Performance testing suite (not run)

Services Configured

  1. pai-vllm.service - vLLM OpenAI-compatible API server (disabled, OOM)

Recommendations

Immediate (Keep Current Setup)

  1. Keep Ollama as primary local inference - proven working, saves $225/month
  2. Keep current routing - 60% local free, 40% cloud paid
  3. Monitor disk space - run cleanup script monthly
  4. Document daemon patterns - reference for future services

Optional (Low Priority)

  1. Add OpenRouter as tertiary fallback - for exotic models only
  2. Remove vLLM installation - free 17GB if not planning to use
  3. Consider smaller vLLM model - Qwen2.5-3B if want to benchmark later

Future (Hardware Upgrade)

  1. GPU upgrade to 16GB+ - would enable vLLM alongside Ollama
  2. NVMe expansion - prevent disk space issues
  3. Revisit vLLM - if batch processing needs emerge

Metrics & Impact

Cost Savings

  • Ollama savings: $225/month (vs all-cloud)
  • OpenRouter potential: ~$7/month (3% via Pro credits)
  • Total monthly savings: $225+ achieved

Disk Space

  • Before cleanup: 7.5GB free (99% used)
  • After cleanup: 43GB free (92% used)
  • vLLM impact: -17GB if kept

Performance (Ollama)

  • Math reasoning: 3s (acceptable)
  • Classification: 2s (excellent)
  • Routing: Works reliably
  • Uptime: Restored after cleanup

System Load

  • Swap usage: 21GB/23GB (heavy)
  • Load average: 2.48-4.80 (high but stable)
  • GPUs: RTX 3060 (644MB used), RTX 4060 (4MB used)

Files Modified

  • None (all new documentation)

Files Created

  • 10 documentation files (153KB)
  • 3 shell scripts
  • 1 systemd service (disabled)
  • ~/.vllm-env/ (1.8GB, optional to keep)
  • ~/.cache/huggingface/models—Qwen—Qwen2.5-7B-Instruct/ (15GB, optional)

Outstanding Questions for Duane

  1. vLLM cleanup: Keep installed (17GB) for future testing or remove now?
  2. OpenRouter: Add as tertiary fallback or skip entirely?
  3. Smaller vLLM model: Try Qwen2.5-3B for benchmark comparison?
  4. Daemon documentation: Sufficient or need more operational runbooks?

Next Session Recommendations

If continuing this work:

  1. Test smaller vLLM model (Qwen2.5-3B-Instruct, ~4GB)
  2. Run side-by-side benchmark if smaller model works
  3. Implement OpenRouter fallback if desired
  4. Create systemd timer for monthly disk cleanup
  5. Document cross-engine handoff patterns (RecordRouter integration)

Conclusion

Primary objective achieved: Ollama operational and saving $225/month vs all-cloud.

Secondary findings:

  • vLLM blocked by GPU memory constraints (hardware limitation)
  • OpenRouter offers minimal cost benefit (~3%)
  • Current architecture (Ollama + Claude fallback) is optimal for hardware
  • Comprehensive daemon documentation will prevent future issues

Bottom line: Keep Ollama, keep current routing, monitor disk space, revisit vLLM only if GPU upgraded.

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