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

bash {baseDir}/install.sh

The script explicitly lists every network operation before proceeding and requires confirmation.

Step 2 — Add to soul.md:

markdown

## Skills
- reflex-learn

Usage#

Run after every agent response (post-response trigger):

bash

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 \
  --offline

Run on heartbeat to scan for positive reinforcement candidates:

bash

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 \
  --offline

Optionally, use local Ollama for richer AI-generated reflections (no additional network access — Ollama runs locally):

bash

python3 {baseDir}/reflex_learn.py --query "<query>" --use-ollama --ollama-model llama3

Slash commands (pass as --query value):

bash

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

OptionDefaultDescriptionSIMILARITY_THRESHOLD``0.85Cosine similarity above which two queries are considered the sameLOOKBACK_INTERACTIONS``10How many past interactions to compare againstPOSITIVE_REINFORCEMENT_DELAY``3Interactions to wait before confirming positive reinforcementREPEAT_COUNT_THRESHOLD``2Repeats within the session window required to flag as failureSESSION_WINDOW_MINUTES``60Time window (minutes) within which repeats are countedMODE``cautious``cautious = stage updates in pending file; aggressive = write directly to SOUL.md

Signal Types#

SignalMeaningneutralNo similar query found in historywatchingSimilar query found, repeat count below threshold — monitoringpreferenceSimilar query with modifier words — preference extracted, not a failurenegativeRepeat threshold reached — reflection written to MEMORY.mdreinforcedQuery 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.sh has 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:11434 only — 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.