- name
- reflex-learn
- description
- Detects repeated queries as implicit negative feedback and non-repetition as positive feedback, enabling continuous learning by writing reflections and patterns to MEMORY.md and SOUL.md. v1.1.1 adds path validation, model-download guard, --offline flag, and a formal install.sh.
- version
- 1.1.1
- triggers
- metadata
- openclaw
- requires
- bins
- ["python3", "bash"]
ReflexLearn
ReflexLearn enables true continuous learning via implicit feedback. It turns repetition of the same question into an automatic "I screwed up" signal and non-repetition into a "user is satisfied" signal — with no explicit rating or feedback required from the user.
v1.1.1 fixes: path validation enforced in code (all writes restricted to ~/.openclaw/), model-download guard with explicit warning and --offline flag, install.sh for declared one-step PyPI + model-weight setup, scikit-learn removed from dependencies (was unused).
Installation
Step 1 — Run the install script. This is the only step that touches the network. It installs Python packages from PyPI and pre-caches the model weights from Hugging Face (~80 MB, one-time only). After this step the skill can run fully offline.
bash {baseDir}/install.shThe script explicitly lists every network operation before proceeding and requires confirmation.
Step 2 — Add to soul.md:
## Skills
- reflex-learnUsage
Run after every agent response (post-response trigger):
python3 {baseDir}/reflex_learn.py \
--query "<current_user_query>" \
--memory-file ~/.openclaw/MEMORY.md \
--soul-file ~/.openclaw/SOUL.md \
--history-file ~/.openclaw/reflex_history.json \
--pending-file ~/.openclaw/reflexlearn-pending.md \
--skill-md {baseDir}/SKILL.md \
--offlineRun on heartbeat to scan for positive reinforcement candidates:
python3 {baseDir}/reflex_learn.py \
--heartbeat \
--memory-file ~/.openclaw/MEMORY.md \
--soul-file ~/.openclaw/SOUL.md \
--history-file ~/.openclaw/reflex_history.json \
--skill-md {baseDir}/SKILL.md \
--offlineOptionally, use local Ollama for richer AI-generated reflections (no additional network access — Ollama runs locally):
python3 {baseDir}/reflex_learn.py --query "<query>" --use-ollama --ollama-model llama3Slash commands (pass as --query value):
python3 {baseDir}/reflex_learn.py --query "/reflex status"
python3 {baseDir}/reflex_learn.py --query "/reflex ignore-last"Configuration
Edit these values directly in this file to tune behaviour. They are parsed at runtime.
- SIMILARITY_THRESHOLD: 0.85
- LOOKBACK_INTERACTIONS: 10
- POSITIVE_REINFORCEMENT_DELAY: 3
- REPEAT_COUNT_THRESHOLD: 2
- SESSION_WINDOW_MINUTES: 60
- MODE: cautious
| Option | Default | Description |
|---|---|---|
SIMILARITY_THRESHOLD | 0.85 | Cosine similarity above which two queries are considered the same |
LOOKBACK_INTERACTIONS | 10 | How many past interactions to compare against |
POSITIVE_REINFORCEMENT_DELAY | 3 | Interactions to wait before confirming positive reinforcement |
REPEAT_COUNT_THRESHOLD | 2 | Repeats within the session window required to flag as failure |
SESSION_WINDOW_MINUTES | 60 | Time window (minutes) within which repeats are counted |
MODE | cautious | cautious = stage updates in pending file; aggressive = write directly to SOUL.md |
Signal Types
| Signal | Meaning |
|---|---|
neutral | No similar query found in history |
watching | Similar query found, repeat count below threshold — monitoring |
preference | Similar query with modifier words — preference extracted, not a failure |
negative | Repeat threshold reached — reflection written to MEMORY.md |
reinforced | Query not repeated in next N interactions — positive reinforcement written |
Core Behavior
On every user message, ReflexLearn embeds the query with sentence-transformers (all-MiniLM-L6-v2) and compares it to the last LOOKBACK_INTERACTIONS interactions stored in ~/.openclaw/reflex_history.json.
If cosine similarity > SIMILARITY_THRESHOLD and the query contains modifier words (e.g., "be more concise", "add examples", "in table format"), it extracts a preference and writes it to MEMORY.md — it does not flag this as a failure.
If cosine similarity > SIMILARITY_THRESHOLD without modifier words and the repeat count within SESSION_WINDOW_MINUTES reaches REPEAT_COUNT_THRESHOLD, it triggers a reflection and writes it to MEMORY.md.
In cautious mode (default), proposed SOUL.md updates are staged in reflexlearn-pending.md for human review. In aggressive mode, they are written directly to SOUL.md.
On heartbeat, if the same query is NOT repeated in the next POSITIVE_REINFORCEMENT_DELAY interactions, it triggers positive reinforcement.
All memory writes are valid Markdown that OpenClaw already understands.
Security and Network Rules
- Path enforcement: The code resolves all file paths and aborts with an error if any path falls outside
~/.openclaw/. This is enforced in code, not just documentation. - No runtime network access: After
install.shhas been run, the skill operates fully offline when invoked with--offline. Without--offline, a warning is printed if the model is not cached. - Declared network operations: All network access (PyPI, Hugging Face) is performed exclusively by
install.sh, which lists operations and requires user confirmation before proceeding. - Local Ollama only: The optional Ollama integration calls
localhost:11434only — no external API. - No writes outside
~/.openclaw/: Enforced at runtime; any misconfigured path triggers an immediate exit. - In cautious mode, NEVER write directly to SOUL.md without staging in pending file first.