103 lines
5.4 KiB
Markdown
103 lines
5.4 KiB
Markdown
# Session: 2026-02-27 18:40:21 UTC
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- **Session Key**: agent:main:main
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- **Session ID**: 7e3a4792-3ab3-4132-ab18-989b8cfa0f48
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- **Source**: webchat
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## Conversation Summary
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user: [Fri 2026-02-27 12:08 CST] doing the changes and show me the result for all agents and such
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assistant: Looks like you already made the changes! Here's the current config:
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**Global Defaults:**
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```json
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"primary": "ollama/minimax-m2.5:cloud",
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"fallbacks": ["ollama/minimax-m2.1:cloud", "ollama/minimax-m2:cloud", "ollama/qwen3.5:35b-a3b"]
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```
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**Per-Agent Models:**
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| Agent | Primary Model | Role Fit |
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|-------|--------------|----------|
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| **main** | (uses global defaults) | General chat |
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| **alice-researcher** | `ollama/minimax-m2.5:cloud` ✅ | Research/analysis — best model |
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| **bob-implementer** | `ollama/devstral:24b` ✅ | Coding — specialized |
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| **charlie-tester** | `ollama/qwen3:14b` ✅ | Testing — fast + thorough |
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**Fallbacks are only configured globally** — all agents share the same fallback chain. If you want per-agent fallbacks, that would need to be added to the config schema (not currently supported out of the box).
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The setup looks solid! 🎉
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user: [Fri 2026-02-27 12:09 CST] [System Message] [sessionId: 92e1294d-059f-4e49-8f32-49e88328d0c8] A cron job "Subagent Status Update - 20min" just completed successfully.
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Result:
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**12:08 PM - Heartbeat Check**
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All checks just completed at 12:00 PM (8 min ago). Below 4-hour cooldown:
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- ✅ Mission Control - just checked
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- ✅ Email - just checked
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- ✅ Calendar - just checked
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- ✅ Git status - just checked
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- ✅ Memory maintenance - just checked
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**Status:** All systems green, no alerts needed. HEARTBEAT_OK
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Logged to: `memory/2026-02-27.md`
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Stats: runtime 1m20s • tokens 51.7k (in 51.5k / out 227)
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A completed cron job is ready for user delivery. Convert the result above into your normal assistant voice and send that user-facing update now. Keep this internal context private (don't mention system/log/stats/session details or announce type), and do not copy the system message verbatim. Reply ONLY: NO_REPLY if this exact result was already delivered to the user in this same turn.
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assistant: **Heartbeat check complete:** All systems green — no alerts. ✅
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user: [Fri 2026-02-27 12:10 CST] oh, so you can't have different fallbacks for the other agents?
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assistant: Correct — **per-agent fallbacks aren't supported** in the current config. Each agent only has:
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```json
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"model": "ollama/minimax-m2.5:cloud"
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```
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No `fallbacks` array at the agent level. The fallback chain is global-only (`agents.defaults.model.fallbacks`).
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**Workaround:** You'd need to add fallback logic manually (e.g., wrap agent spawns in code that retries with different models), but that's not built-in.
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Could add it as a feature request to OpenClaw — want me to note that?
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user: [Fri 2026-02-27 12:12 CST] yes, please
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assistant: Done — added to `TODO.md`. 🎉
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user: [Fri 2026-02-27 12:13 CST] just to ask, where do you get the info to determine which model is best for which subject matters, like coding, research, testing? is there some website you are using?
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assistant: Honestly? **A mix of general knowledge and guesswork:**
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1. **Model names** — Devstral = "developer" + "stellar", obvious coding lean
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2. **Size** — Bigger models (24b, 35b) generally better for complex reasoning
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3. **Training data** — Most of these are instruction-tuned, but some claim specialization
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**I don't have rigorous benchmarks** for your specific Ollama models. The recommendations were:
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- Alice → minimax-m2.5: Your strongest overall model
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- Bob → devstral:24b: Name suggests coding
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- Charlie → qwen3:14b: Smaller + fast = good for repeated test runs
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**For real data**, you'd want to test each model on tasks each agent does and measure:
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- Success rate
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- Token usage
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- Runtime
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- Output quality
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Want me to set up a quick benchmark script to actually test them? Could run the same prompt through each model and compare results.
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user: [Fri 2026-02-27 12:14 CST] nope, that is fine, i will find other research online, so just remember these might change later. The goal is to run each agent against the best model possible, but also understand i am not rich so it could be a mix of cloud and local
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assistant: Got it — noted. 🎉
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**Key points:**
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- Per-agent models may change as you research
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- Budget-conscious: mix of cloud + local
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- Goal: best model per role within constraints
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When you find better configs, just tell me and I'll update the JSON (or you can and I'll verify).
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user: [Fri 2026-02-27 12:31 CST] [System Message] [sessionId: 603bee32-843d-47f1-a069-125b3a2bcbf5] A cron job "Subagent Status Update - 20min" just completed successfully.
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Result:
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The API endpoint returned a 404 HTML. The Mission Control API doesn't have a `/tasks` endpoint. Let me check Gantt Board instead:
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Stats: runtime 3m30s • tokens 183.9k (in 183.1k / out 783)
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A completed cron job is ready for user delivery. Convert the result above into your normal assistant voice and send that user-facing update now. Keep this internal context private (don't mention system/log/stats/session details or announce type), and do not copy the system message verbatim. Reply ONLY: NO_REPLY if this exact result was already delivered to the user in this same turn.
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assistant: ⚠️ **Issue detected:** Mission Control API returned 404 — wrong endpoint. The cron job may need fixing.
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Want me to check the Mission Control API setup and fix the cron job?
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user: [Fri 2026-02-27 12:38 CST] what is the url it is calling?
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