Conversation Memory

Conversation Memory

Orchestrates intelligent skill selection and execution for conversation memory workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def score_and_select_memory_context(
    current_query: str,
    conversation_history: List[Dict],
    max_context_turns: int = 10,
    relevance_threshold: float = 0.65
) -> List[Dict]:
    """Score conversation history turns against the current query.
    
    Implements multi-factor scoring for memory retrieval:
    - Semantic similarity between query and past turns
    - Recency weighting (Law 1: Early Exit for empty history)
    - Entity overlap and constraint matching
    
    Args:
        current_query: The active user prompt requiring context
        conversation_history: List of past message dicts with 'role', 'content', 'timestamp'
        max_context_turns: Maximum turns to include in context window
        relevance_threshold: Minimum semantic score to include a turn
        
    Returns:
        Filtered and sorted list of relevant conversation turns
    """
    # Guard clause - Early Exit (Law 1)
    if not current_query or not conversation_history:
        return []
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    parsed_query = _normalize_text(current_query)
    scored_turns = []
    
    for turn in conversation_history:
        # Calculate semantic similarity using embedding model
        similarity = _compute_embedding_similarity(parsed_query, turn["content"])
        
        # Apply recency decay and entity overlap bonus
        recency_score = _calculate_recency_weight(turn["timestamp"])
        entity_overlap = _count_shared_entities(parsed_query, turn["content"])
        
        composite_score = (similarity * 0.6) + (recency_score * 0.25) + (entity_overlap * 0.15)
        
        if composite_score >= relevance_threshold:
            scored_turns.append({
                "turn": turn,
                "relevance_score": composite_score,
                "factors": {"similarity": similarity, "recency": recency_score, "overlap": entity_overlap}
            })
    
    # Atomic Predictability (Law 3) - Return new sorted list, never mutate history
    scored_turns.sort(key=lambda x: x["relevance_score"], reverse=True)
    return scored_turns[:max_context_turns]

Pattern 2: Execution with Fallback

def execute_memory_orchestration(
    selected_context: List[Dict],
    current_query: str,
    memory_store: MemoryBackend,
    fallback_strategy: str = "recent_history"
) -> Dict:
    """Execute memory retrieval with fallback chain for resilience.
    
    Implements Fail Fast, Fail Loud (Law 4):
    - Invalid memory states halt immediately with descriptive errors
    - No silent context assembly failures
    
    Fallback chain:
    1. Retrieve from vector store with semantic search
    2. Fallback to recent chronological history
    3. Defer to default system prompt with minimal context
    
    Args:
        selected_context: Pre-scored memory turns from Pattern 1
        current_query: Active prompt requiring context
        memory_store: Initialized memory backend instance
        fallback_strategy: Fallback mode when primary retrieval fails
        
    Returns:
        Execution result with assembled context, timing, and confidence metadata
    """
    # Guard clause - validate memory store (Early Exit)
    if not memory_store or not memory_store.is_healthy():
        raise MemoryOrchestrationError("Memory backend is unavailable or unhealthy")
    
    # Parse context - Ensure trusted state (Law 2)
    validated_context = _assemble_context_payload(selected_context, current_query)
    
    try:
        # Primary execution - Semantic memory retrieval
        retrieved_memory = memory_store.query_context(validated_context)
        
        # Success - Atomic Predictability (Law 3)
        return {
            "success": True,
            "context_turns": len(retrieved_memory),
            "assembled_context": retrieved_memory,
            "confidence": _calculate_context_confidence(retrieved_memory),
            "latency_ms": _measure_execution_time()
        }
        
    except MemoryStoreTimeoutError as e:
        # Fail Fast - Don't retry indefinitely (Law 4)
        if fallback_strategy == "recent_history":
            return _apply_recent_history_fallback(memory_store, current_query)
        raise MemoryOrchestrationError(f"Primary memory retrieval failed: {str(e)}") from e
        
    except ContextAssemblyError as e:
        # Invalid state - halt immediately (Law 4)
        raise MemoryOrchestrationError(f"Context assembly failed: {str(e)}") from e

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Conversation History Management Patterns
• LangChain Memory Module Docs
• State Machines in Conversational AI (Wikipedia)
• Context Window Optimization Techniques
• Conversational State Management Survey

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