Live-Evo: Online Self-Evolving Memory

You are using the Live-Evo memory system that learns from past mistakes through experience accumulation and adaptive evaluation.

IMPORTANT — Script location: All scripts are in the scripts/ subdirectory next to this SKILL.md file. When running scripts, use the absolute path to the scripts/ directory relative to where this file is located. For example, if this SKILL.md is at /path/to/live-evo/SKILL.md, the scripts are at /path/to/live-evo/scripts/.

Experience data is stored persistently at ~/.live-evo/experience_db.jsonl (independent of skill installation location).

Core Workflow

1. Retrieve & Compile (Before Acting)

Run the experience retrieval script to find relevant past experiences:

python <scripts-dir>/retrieve.py --query "YOUR_TASK_DESCRIPTION"

If experiences are found, they will be compiled into a task-specific guideline. Use this guideline to inform your approach.

2. Decide: Verify or Direct Apply

You must judge whether contrastive verification (two attempts) is worthwhile based on:

If contrastive eval IS worthwhile → Go to Step 2A

If contrastive eval is NOT worthwhile → Go to Step 2B

Step 2A: Contrastive Evaluation (Two Attempts)

Make two independent attempts:

Attempt A (Without Memory):

• Solve the task using only your base knowledge
• Record your answer/approach

Attempt B (With Guideline):

• Apply the retrieved guideline
• Solve the task with this informed approach
• Record your answer/approach

Then verify and update weights:

python <scripts-dir>/update.py \
  --task "TASK_DESCRIPTION" \
  --result-a "RESULT_WITHOUT_MEMORY" \
  --result-b "RESULT_WITH_GUIDELINE" \
  --correct "CORRECT_ANSWER" \
  --experience-ids "id1,id2,..."

Step 2B: Direct Apply with Feedback-Based Learning

When contrastive evaluation is not feasible:

1. Apply the guideline directly (if one was retrieved) and complete the task
2. Observe feedback from any of these sources:
• User feedback (corrections, complaints, approval)
• Environment signals (test results, error messages, build output)
• Outcome observation (did the result work as expected?)
3. Store experience directly if feedback reveals a lesson:

python <scripts-dir>/add_experience.py \
  --question "THE_TASK_QUESTION" \
  --failure-reason "What went wrong (from feedback)" \
  --improvement "Key lesson learned" \
  --category "coding|analysis|prediction|debugging|other"

No contrastive comparison needed — just learn from what happened.

3. Add New Experience (On Any Failure)

Whenever a task fails or feedback reveals a learnable lesson — regardless of which path you took — store the experience:

python <scripts-dir>/add_experience.py \
  --question "THE_TASK_QUESTION" \
  --failure-reason "What went wrong" \
  --improvement "Key lesson learned" \
  --category "coding|analysis|prediction|debugging|other"

4. Update Weights (When Possible)

If you used a retrieved guideline and can determine whether it helped:

python <scripts-dir>/update.py \
  --task "TASK_DESCRIPTION" \
  --result-a "WHAT_WOULD_HAVE_HAPPENED" \
  --result-b "WHAT_ACTUALLY_HAPPENED" \
  --correct "CORRECT_OUTCOME" \
  --experience-ids "id1,id2,..."

If you cannot determine whether the guideline helped, skip weight updates — no update is better than a wrong update.

When to Use Live-Evo

Use this system for:

• Coding tasks: Bug fixes, implementations where tests can verify
• Analysis tasks: Where ground truth can be checked
• Predictions: Forecasting with eventual verification
• Problem solving: Tasks with objectively correct answers
• Any task with user feedback: Even without formal verification, user corrections are valuable signals

Experience Format

Each experience contains:

• question: The original task/question
• failure_reason: What went wrong in the original attempt
• improvement: Key lesson or approach that would have helped
• missed_information: Information sources or considerations that were missed
• weight: Quality score (0.1-2.0) updated based on usefulness
• category: Domain category for filtering

Key Principles

1. Cost-Aware Verification: Only do contrastive evaluation when the cost is justified — don't waste tokens/time on expensive double-runs
2. Feedback is Gold: User corrections, test failures, and error messages are direct learning signals — always store these
3. Selective Acquisition: Only store experiences that contain a genuine, actionable lesson
4. Weight-based Retrieval: Good experiences rise, bad ones fade
5. Task-Specific Guidelines: Don't apply raw experiences — synthesize them into actionable guidance
6. When in Doubt, Store: It's better to store a potentially useful experience than to miss a lesson; low-quality experiences will naturally decay via weight updates

Manual Commands

View all experiences:

python <scripts-dir>/list_experiences.py

Search experiences:

python <scripts-dir>/retrieve.py --query "your search query" --top-k 5

Get statistics:

python <scripts-dir>/stats.py