Conversation Memory and Context Guide

Feature: PM-034 10-Turn Conversation Memory Implementation: Production-Ready Context Window System Updated: 2025-08-09

Understand how Piper Morgan remembers your conversations and maintains context across multiple interactions. This guide explains the “magic” behind conversational AI that feels natural and human-like.

How Piper’s Memory Works

The 10-Turn Window

Piper remembers your last 10 interactions in a conversation. Think of it like having a conversation with someone who takes excellent notes - Piper keeps track of everything you’ve discussed and can reference it naturally.

Key Benefits:

What Piper Remembers

Piper automatically tracks and remembers:

Memory in Action

Piper’s memory works seamlessly across your entire conversation. Here are real examples showing how context is maintained:

Example 1: Issue Management Memory

You: "Create a high priority bug for the payment processing failure"
Piper: "Created GitHub issue #85 for payment processing failure. Priority: High, Labels: [bug, payments, international]."

You: "What's the current status?"
Piper: "GitHub issue #85 (payment processing failure) is currently in 'To Do' status."

You: "Update it with the error logs from production"
Piper: "Updated GitHub issue #85 with production error logs."

You: "Create a follow-up task for the security review"
Piper: "Created security review task #86 linked to payment processing bug #85."

You: "What's the timeline for fixing that issue?"
Piper: "Based on the payment processing bug (#85) complexity, estimated timeline is 3-5 business days."

Memory Analysis:

Example 2: Document Management Memory

You: "Upload the new API documentation and user guide"
Piper: "Uploaded API_documentation_v2.1.pdf and User_Guide_v3.0.pdf to the knowledge base."

You: "Search the API documentation for authentication endpoints"
Piper: "Found 5 authentication endpoints in API_documentation_v2.1.pdf..."

You: "Summarize the user guide"
Piper: "Summary of User_Guide_v3.0.pdf: 45 pages covering user onboarding..."

You: "Compare it with the previous version"
Piper: "Comparing User_Guide_v3.0.pdf with User_Guide_v2.5.pdf: 12 new pages added..."

Memory Analysis:

Example 3: Sprint Planning Memory

You: "Show me the current sprint backlog"
Piper: "Current sprint backlog contains 12 items: 3 high priority bugs, 5 feature tasks..."

You: "What's the highest priority item?"
Piper: "The highest priority item is bug #89 (user authentication timeout) with 8 story points."

You: "Assign it to the frontend team"
Piper: "Assigned bug #89 (user authentication timeout) to the frontend team."

You: "What about the second priority item?"
Piper: "The second priority item is feature task #92 (dashboard analytics enhancement) with 13 story points."

You: "Update its status to in progress"
Piper: "Updated feature task #92 (dashboard analytics enhancement) status to 'In Progress'."

Memory Analysis:

Understanding Context Types

Conversation Context

Piper maintains complete conversation history including:

Entity Context

Piper tracks specific entities and their states:

User Context

Piper remembers your preferences and patterns:

Memory Management

Automatic Context Updates

Piper automatically manages your conversation context:

Context Expiration

Piper intelligently manages memory to maintain performance:

Memory Efficiency

Piper optimizes memory usage for the best performance:

Best Practices for Effective Memory

Conversation Design

Keep Related Topics Together:

Maintain Context Boundaries:

Reference Patterns

Use Consistent References:

Optimize for Memory:

Performance Optimization

Session Management:

Context Monitoring:

Troubleshooting Memory Issues

Common Memory Problems

Issue: “I can’t find that issue”

Issue: “Wrong item resolved”

Issue: “Context lost”

Issue: “Slow response”

Performance Tips

Optimal Usage:

When Memory Works Best:

When to Start Fresh:

Advanced Memory Features

Context Retrieval

You can inspect your current conversation context:

# Get current conversation context
curl -X GET "http://your-piper-instance/api/v1/conversation/my_session_123/context"

Sample Response:

{
  "turns": [
    {
      "turn_number": 1,
      "user_message": "Create issue for login bug",
      "assistant_response": "Created issue #85",
      "entities": ["#85", "login bug"],
      "timestamp": "2025-08-09T08:00:00Z"
    }
  ],
  "context_window_size": 10,
  "total_turns": 1
}

Context Settings

Customize your conversation memory:

{
  "context_window": 15, // Increase from default 10
  "cache_ttl": 600, // Cache duration in seconds
  "entity_tracking": true, // Enable entity tracking
  "performance_mode": "balanced" // speed/balanced/high_accuracy
}

Memory Analytics

Track your conversation patterns:

# Get memory performance metrics
curl -X GET "http://your-piper-instance/api/v1/conversation/my_session_123/analytics"

Metrics Include:

Integration with Other Features

Anaphoric Reference Resolution

Memory works seamlessly with reference resolution:

Getting Started Guide

This memory system is the foundation for conversational AI:

Performance Validation

Real performance data from PM-034:

Success Metrics

Personal Success Indicators

You’re effectively using conversation memory when:

Team Success Indicators

Your team is successfully leveraging memory when:

Performance Benchmarks

Target Performance:

Current Performance (PM-034):

Next Steps

Ready to experience the magic? Start a conversation and discover how Piper’s memory makes your interactions feel natural, efficient, and human-like!