docs(openclaw): document lasagna logging recovery and compaction in issue 27

2026-10-12 21:05:22 +02:00
parent 972d629df8
commit 6acceec701
1 changed files with 2 additions and 0 deletions
@@ -900,6 +900,8 @@ This document captures fixed issues, operational quirks, and incident root-cause
     - Updated `knowledge-wiki/agents/README.md` with cross-agent tool-calling standards (`tool_search` top-level only, `sessions_history` non-negative offset + `sessionKey`, `ask_user` questions array, `uv run` for Python scripts).
  3. **Pre-installed Python Libraries**:
     Installed `pillow`, `google-api-python-client`, and `pypdf` into the persistent Python environment using `uv pip install --break-system-packages`, and added the command to `openclaw-instance.secret.yaml` for permanent self-healing.
+  4. **Retroactive Meal Intake Recovery & Compaction**:
+     The user prompt (`540g selbst gemachte Lasagne mit bechqmele sauce`) that was dropped during the unfinalized GLM loop was logged retroactively into DuckDB (`01M4RQT5PFNN7J4307TCRM3E80`, ~864 kcal, ~43.2 g Protein). Daily totals (both portions 540g + 430g and the 30g Clear Whey shake) updated to 3,266 kcal / 177.9 g protein, web dashboards re-rendered, and Topic 21 compacted from 49.6k tokens down to 4.6k tokens.
 - **How to Avoid Recurrence**:
  - For agents that routinely process user photos or document scans, assign dedicated vision models (`google/gemini-3.8-flash`) as the agent's primary model rather than relying on `agents.defaults.imageModel`.
  - Maintain the response guarantee in agent prompts to prevent empty turns when reasoning models encounter uncertainty.