---
name: "🧠 Smart Learning"
about: Feature requests for feedback loops, RLHF for tools, and adaptive routing
title: "[Learning] "
labels: ["enhancement", "learning", "rlhf", "adaptive"]
assignees: []
---

## Feature Area
<!-- Check the area(s) this feature request relates to -->
- [ ] Feedback Loop (RLHF for Tools)
- [ ] Adaptive Embeddings
- [ ] Tool Performance Learning
- [ ] User Preference Learning
- [ ] Other Learning Feature

## Problem Statement
<!-- Describe the current limitation in learning/adaptation this feature would address -->

**Current Behavior:**
<!-- How does routing/selection work today? -->

**Desired Behavior:**
<!-- How should the system learn and adapt? -->

## Proposed Solution

### Overview
<!-- High-level description of the learning mechanism -->

### Learning Mechanism
<!-- Describe how feedback is collected and applied -->

### Technical Details
<!-- Technical implementation details, if applicable -->

### Example Usage
<!-- Provide a code example showing how this would work -->

```python
# Example usage
from agent_gantry import AgentGantry

gantry = AgentGantry()

# Example of feedback collection and learning
```

## Use Case
<!-- Describe a real-world scenario where learning would improve outcomes -->

**Example Scenario:**


**Expected Improvement:**


## Context: Current Routing Behavior

Agent-Gantry currently uses:
- ✅ Semantic similarity (vector search)
- ✅ Intent classification
- ✅ MMR diversity
- ✅ Static health metrics (success rate, circuit breaker state)

This feature request aims to add **adaptive learning** capabilities that improve over time.

### Feedback Loop (RLHF for Tools)
- **Goal**: Learn from tool selection feedback to improve future routing
- **Mechanism**:
  - If an LLM tries to use a tool and fails (or user explicitly says "wrong tool")
  - Record this negative feedback in a feedback store
  - Adjust embeddings or routing weights to penalize Tool A for Query X
  - Even if semantically similar, learn that Tool A is bad for this query type
- **Example**:
  ```python
  # User corrects tool selection
  await gantry.record_feedback(
      query="cancel my subscription",
      selected_tool="pause_subscription",  # Wrong
      correct_tool="cancel_subscription",  # Correct
      feedback_type="tool_selection_error"
  )
  
  # Future queries automatically improve
  tools = await gantry.retrieve_tools("cancel my subscription")
  # Now correctly ranks cancel_subscription higher
  ```

### Adaptive Embeddings
- **Goal**: Fine-tune embeddings based on actual usage patterns
- **Approach**: 
  - Collect positive/negative pairs from tool execution outcomes
  - Periodically retrain or adjust embeddings
  - Learn domain-specific semantics

### Tool Performance Learning
- **Goal**: Learn which tools work well together, which fail frequently, etc.
- **Metrics**: Track correlation between tool pairs, failure patterns, timing

### User Preference Learning
- **Goal**: Learn per-user or per-team tool preferences
- **Example**: Team A prefers `slack_notify` over `email_notify`

## Feedback Collection Strategy
<!-- How would feedback be collected? Manual, automatic, both? -->

## Privacy & Data Considerations
<!-- Any privacy concerns with collecting feedback data? -->

## Evaluation Metrics
<!-- How would you measure if the learning is working? -->

## Additional Context
<!-- Add any other context, research papers, or references -->

## Related Issues
<!-- Link to related issues or PRs -->

---
**Note**: This feature aligns with the strategic goal of creating a self-improving tool orchestration system that gets better with use.
