StackMemory Specification v1.0

Executive Summary

StackMemory is a lossless, project-scoped memory runtime for AI coding and writing tools that preserves full project context across sessions using a call stack metaphor instead of linear chat logs. It organizes memory as nested frames with smart retrieval, enabling AI tools to maintain context across thread resets, model switches, and long-running projects.

Core Architecture

1. Memory Model

1.1 Frame Stack Structure

memory_model:
  structure: "call_stack"  # Not linear chat log
  max_depth: 10000         # Maximum frames in stack
  retention: 30_days       # Local retention window
  storage:
    local: "SQLite"        # Fast local storage
    remote: "TimeSeries DB + S3"  # Infinite remote storage

1.2 Frame Composition

frame:
  metadata:
    id: "uuid"
    title: "descriptive_name"
    type: "task|debug|feature|architecture"
    owner: "user_id"
    created: "timestamp"
    
  contents:
    events: []            # Tool calls, messages, observations
    anchors: []          # Decisions, constraints, interfaces
    digest: {}           # 60% deterministic, 40% AI-generated summary
    score: 0.0-1.0       # Importance score for retention

2. Storage Architecture

2.1 Two-Tier Storage System

local_storage:
  young: 
    age: "< 1 day"
    retention: "complete"          # Full events, all tool calls
    memory_strategy: "hot"         # RAM for instant access
    compression: "none"
    
  mature:
    age: "1-7 days"
    retention: "selective"         # Digests + anchors + high-score events
    memory_strategy: "warm"        # SQLite with memory cache
    compression: "lz4"
    score_threshold: 0.4
    
  old:
    age: "7-30 days"
    retention: "critical"          # Anchors + decisions only
    memory_strategy: "cold"        # SQLite, no cache
    compression: "zstd"
    score_threshold: 0.7
    
  max_size: 2GB
  overflow_strategy: "promote_to_remote"

remote_storage:
  retention: "infinite"
  indexing:
    primary: "timeseries"          # ClickHouse/TimescaleDB
    secondary: "inverted"          # Elasticsearch
    graph: "relationships"         # Neo4j for frame deps
    
  retrieval:
    cache_layer: "redis"
    p50_latency: 50ms
    p99_latency: 500ms
    prefetch: true
    
  cost_model:
    storage: "$0.02/GB/month"
    retrieval: "$0.0004/1000_reads"
    
  tiers:
    hot: "< 7 days"                # S3 Standard
    warm: "7-90 days"              # S3 Standard-IA
    cold: "> 90 days"              # S3 Glacier
    archive: "> 1 year"            # Glacier Deep Archive

2.2 Migration Strategy

local_to_remote_migration:
  triggers:
    age_based:
      schedule: "0 */6 * * *"      # Every 6 hours
      migrate_after: 24h
      
    size_pressure:
      soft_limit: 75%              # Start migration
      hard_limit: 90%              # Force migration
      strategy: "lowest_score_first"
      
    importance_based:
      score_thresholds:
        "< 0.3": 2h                # Low importance
        "< 0.5": 12h               # Medium
        "< 0.7": 24h               # High
        ">= 0.7": 7d               # Critical
        
  upload_strategy:
    mode: "hybrid"
    continuous_streaming:
      for_events: ["decision", "constraint", "api_change"]
      latency: "< 1 minute"
      
    batch_upload:
      for_events: ["tool_call", "observation", "message"]
      batch_size: 100
      interval: 300s
      compression: true
      
    smart_batching:
      group_by: "frame"
      wait_for_frame_close: true
      max_wait: 1h

3. Importance Scoring System

3.1 Tool Call Scoring (Deterministic)

tool_scores:
  # Discovery & Intelligence (0.8-1.0)
  search: 0.95                     # Finding context/code
  task_creation: 0.90              # Planning work
  decision_recording: 0.90         # Architectural choices
  context_retrieval: 0.85          # Loading memory
  
  # Structural Changes (0.6-0.8)
  write_new_file: 0.75
  major_refactor: 0.70
  api_change: 0.70
  
  # Modifications (0.3-0.6)
  edit: 0.50
  test: 0.45
  bash_execution: 0.40
  
  # Simple Reads (0.1-0.3)
  read: 0.25
  ls: 0.20
  grep: 0.15                      # Simple pattern matching

3.2 Scoring Formula

scoring:
  formula: |
    score = (base_score * weights.base) +
            (impact_multiplier * weights.impact) +
            (persistence_bonus * weights.persistence) +
            (reference_count * weights.reference)
            
  weights:
    configurable: true             # Per-project tuning
    defaults:
      base: 0.4
      impact: 0.3
      persistence: 0.2
      reference: 0.1
      
  profiles:
    security_focused:
      impact: 0.5                  # Changes matter more
    exploration_heavy:
      reference: 0.5               # Discovery paths matter
    production_system:
      persistence: 0.3             # Permanent changes critical

4. Smart Context Retrieval

4.1 LLM-Driven Retrieval

context_retrieval:
  compressed_summary:
    # Provided to LLM for analysis
    recent_session:
      frames: 15
      dominant_operations: []
      files_touched: []
      errors_encountered: []
      
    historical_patterns:
      topic_frame_counts: {}
      key_decisions: []
      recurring_issues: []
      
    queryable_indices:
      by_error_type: {}
      by_timeframe: {}
      by_contributor: {}
      
  llm_analysis:
    inputs:
      - current_query
      - compressed_summary
      - token_budget
      
    output:
      reasoning: "visible/auditable"
      frames_to_retrieve: []
      confidence_score: 0.0-1.0
      
  generation:
    when: "on_demand"              # Not pre-computed
    visibility: "settings/on_request"  # Auditable

4.2 Query Language

4.2.1 Natural Language Queries

nlp_queries:
  time_based:
    - "provide context from the last day"
    - "show me what happened yesterday"
    - "get all work from December 15-20"
    - "what did Alice work on last week"
    
  topic_based:
    - "find all authentication work"
    - "show database migration frames"
    - "get frames about the login bug"
    - "what decisions were made about caching"
    
  combined:
    - "show Alice's auth work from last week"
    - "get high-priority bug fixes from yesterday"
    - "find security decisions in the last month"

4.2.2 Structured Query Format

interface StackMemoryQuery {
  // Time filters
  time?: {
    last?: string;         // "1d", "3h", "1w", "2m"
    since?: Date;          // ISO timestamp
    until?: Date;         
    between?: [Date, Date];
    specific?: Date;       // Exact date
  };
  
  // Content filters
  content?: {
    topic?: string[];      // ["auth", "database"]
    files?: string[];      // ["src/*.ts", "tests/*"]
    errors?: string[];     // ["timeout", "null pointer"]
    tools?: string[];      // ["search", "edit", "test"]
  };
  
  // Frame filters
  frame?: {
    type?: FrameType[];    // ["bug", "feature", "refactor"]
    status?: Status[];     // ["open", "closed", "stalled"]
    score?: {
      min?: number;        // 0.0-1.0
      max?: number;
    };
    depth?: {
      min?: number;        // Stack depth
      max?: number;
    };
  };
  
  // People filters
  people?: {
    owner?: string[];      // ["alice", "bob"]
    contributors?: string[];
    team?: string;         // "backend-team"
  };
  
  // Output control
  output?: {
    limit?: number;        // Max frames to return
    sort?: SortBy;         // "time" | "score" | "relevance"
    include?: string[];    // ["digests", "events", "anchors"]
    format?: Format;       // "full" | "summary" | "ids"
  };
}

4.2.3 Query Examples

// Last day's context
{
  time: { last: "1d" },
  output: { format: "summary" }
}

// High-importance auth work
{
  content: { topic: ["auth", "oauth"] },
  frame: { score: { min: 0.7 } },
  output: { sort: "score", limit: 20 }
}

// Team's recent critical work
{
  time: { last: "3d" },
  people: { team: "backend-team" },
  frame: { score: { min: 0.8 } },
  output: { sort: "time" }
}

4.2.4 Hybrid Query Syntax

# Command-line style
stackmemory query "auth work" --since="2024-12-20" --owner=alice

# Inline modifiers
"show auth work @alice #high-priority since:yesterday depth:10"

# Template style
"context from {time.last=1d} about {topic=authentication}"

4.2.5 Query Shortcuts

shortcuts:
  # Time shortcuts
  "today": { time: { last: "24h" } }
  "yesterday": { time: { between: ["yesterday 00:00", "yesterday 23:59"] } }
  "this week": { time: { last: "7d" } }
  
  # Topic shortcuts
  "bugs": { frame: { type: ["bug", "error", "fix"] } }
  "features": { frame: { type: ["feature", "enhancement"] } }
  "critical": { frame: { score: { min: 0.8 } } }
  
  # Workflow shortcuts
  "my work": { people: { owner: ["$current_user"] } }
  "team work": { people: { team: "$current_team" } }
  "recent": { time: { last: "4h" } }

4.2.6 Query Response Format

interface QueryResponse {
  query: {
    original: string;        // User's input
    interpreted: Query;      // Parsed query
    expanded: Query;         // After expansion
  };
  
  results: {
    frames: Frame[];         // Matching frames
    count: number;           // Total matches
    score: number;           // Query confidence
  };
  
  metadata: {
    execution_time: number;  // ms
    tokens_used: number;
    cache_hit: boolean;
  };
  
  suggestions: {
    refine: string[];        // "Try adding time filter"
    related: string[];       // "See also: auth decisions"
  };
}

4.3 Trace Bundling

trace_detection:
  definition: "Chain of related tool calls"
  
  boundaries:
    time_proximity: 30s            # Tools within 30 seconds
    same_target: true              # Same file/directory
    causal_relationship: true      # Error → fix → test
    
  compression:
    strategy: "single_trace"       # Bundle as one unit
    scoring: "max(all_tools)"      # Use highest score
    
  example:
    raw: "Search → Read(10) → Edit(3) → Test → Fix → Test"
    compressed: "Fixed auth bug via search-driven refactor [0.95]"

5. Garbage Collection

5.1 Incremental GC Strategy

garbage_collection:
  type: "incremental"              # Avoid stop-the-world
  
  process:
    frames_per_cycle: 100          # Process in chunks
    cycle_interval: 60s            # Every minute
    
  generational:
    young: "< 1 day"
    mature: "1-7 days"
    old: "7-30 days"
    
  priorities:
    protect:
      - current_session
      - pinned_frames
      - unsynced_changes
      - high_score_frames
      
    evict_first:
      - low_score_frames
      - orphaned_frames
      - duplicate_traces

6. Digest Generation

6.1 Hybrid Approach (60/40)

digest_generation:
  deterministic: 60%               # Reliable extraction
  ai_generated: 40%                # AI-generated summary
  
  deterministic_fields:
    - files_modified
    - tests_run
    - errors_encountered
    - tool_call_count
    - duration
    - exit_status
    
  ai_generated_fields:
    - summary                      # 1-2 sentences
    - key_decisions
    - learned_insights
    - next_steps
    
  processing:
    when: "batch_during_idle"      # Not immediate
    max_tokens: 200
    fallback: "deterministic_only"

7. Team Collaboration

7.1 Dual Stack Architecture

stack_types:
  individual:
    owner: "single_user"
    visibility: "private"
    can_promote: true
    
  shared:
    team: "team_id"
    visibility: "team"
    participants: []
    handoff_enabled: true
    
  interaction:
    promote: "individual → shared"
    fork: "shared → individual"
    merge: "individual → shared"
    handoff: "alice → bob"

7.2 Frame Ownership

frame_ownership:
  creator: "original_author"
  contributors: []
  last_active: "current_user"
  
  permissions:
    read: "team"
    continue: "team"
    close: "owner_or_admin"
    delete: "owner_only"
    
  handoff:
    explicit: "transfer_command"
    implicit: "continue_working"
    timeout: "idle_24h"

8. Configuration System

8.1 Configuration File

# .stackmemory/config.yaml
version: 1.0

scoring:
  weights:
    base: 0.4
    impact: 0.3
    persistence: 0.2
    reference: 0.1
    
  tool_scores:
    # Custom overrides
    custom_tool: 0.75
    
retention:
  local:
    young: 1d
    mature: 7d
    old: 30d
    max_size: 2GB
    
  remote:
    enabled: true
    retention: infinite
    
performance:
  max_stack_depth: 10000
  retrieval_timeout_ms: 500
  
profiles:
  environment: "production"

8.2 Configuration Validation

$ stackmemory config validate

validation_checks:
  - syntax_validation
  - semantic_validation
  - performance_analysis
  - compatibility_check
  - environment_verification
  
output:
  errors: []
  warnings: []
  suggestions: []
  auto_fix_available: true

9. MCP Integration

9.1 Available Tools

mcp_tools:
  # Context Management
  - get_context              # Smart retrieval with LLM
  - add_decision            # Record decisions
  - start_frame             # Begin new frame
  - close_frame             # Close with digest
  
  # Task Management  
  - create_task
  - update_task_status
  - get_active_tasks
  - get_task_metrics
  
  # Linear Integration
  - linear_sync
  - linear_update_task
  - linear_get_tasks
  
  # Analytics
  - get_metrics
  - get_frame_history
  - search_frames

9.2 Context Bundle Format

{
  "compressed_summary": {
    "recent_activity": {},
    "historical_patterns": {},
    "statistics": {}
  },
  "hot_frames": [],
  "relevant_anchors": [],
  "query_endpoints": {
    "deep_search": "endpoint",
    "replay_session": "endpoint",
    "get_specific_frames": "endpoint"
  }
}

10. Security & Privacy

10.1 Secret Detection

secret_detection:
  patterns:
    - api_keys: "regex_patterns"
    - passwords: "regex_patterns"
    - tokens: "regex_patterns"
    - custom: "user_defined"
    
  action:
    detection: "real_time"
    handling: "redact"            # Not block
    notification: "warn_user"
    
  storage:
    hashed: true
    reversible: false

10.2 Privacy Controls

privacy:
  data_residency: "configurable"
  encryption:
    at_rest: "AES-256"
    in_transit: "TLS 1.3"
    
  retention:
    deletion_on_request: true
    audit_trail: "maintained"
    
  sharing:
    default: "private"
    team_opt_in: true
    org_visibility: "admin_only"

11. Performance Targets

11.1 SLAs

performance_slas:
  retrieval:
    p50: 50ms
    p95: 200ms
    p99: 500ms
    
  storage:
    write_throughput: "10K events/sec"
    batch_upload: "100MB/min"
    
  availability:
    uptime: "99.9%"
    data_durability: "99.999999999%"  # 11 nines
    
  scale:
    max_frames: 10000
    max_events_per_frame: 5000
    max_storage_per_project: "unlimited"

12. Advanced Memory Patterns

12.1 Episodic Memory System

episodic_memory:
  definition: "Capture and reuse past agent experiences"
  
  episode_structure:
    trigger: "significant_event"        # Decision, error, breakthrough
    context_snapshot:
      - pre_state                       # State before episode
      - action_sequence                 # Tools and decisions
      - outcome                         # Result and impact
      - learned_pattern                 # Extracted insight
      
  retrieval_strategy:
    similarity_matching:
      current_context: true
      embedding_distance: "cosine"
      threshold: 0.85
      
    temporal_relevance:
      recent_weight: 0.7
      historical_weight: 0.3
      
  injection_mechanism:
    when: "similar_context_detected"
    format: "Past episode: {summary} led to {outcome}"
    max_episodes: 3

12.2 Memory Synthesis from Execution Logs

log_synthesis:
  pattern_extraction:
    frequency_analysis:
      - common_error_sequences
      - repeated_tool_patterns
      - decision_reversals
      
    causality_detection:
      error_to_fix_chains: true
      search_to_discovery: true
      test_to_refactor: true
      
  synthesis_output:
    workflow_patterns:
      - "Search → Read → Edit → Test → Fix"
      - "Error → Analyze → Search → Solution"
      
    anti_patterns:
      - "Repeated failed attempts"
      - "Circular dependencies"
      
    optimization_opportunities:
      - "Batch similar operations"
      - "Cache frequent queries"

13. Feedback Loop Architecture

13.1 Reflection Loop Pattern

reflection_loop:
  trigger_conditions:
    - frame_completion
    - significant_error
    - milestone_reached
    - context_switch
    
  reflection_process:
    analyze:
      - what_worked: "successful patterns"
      - what_failed: "error patterns"
      - alternative_approaches: "unexplored paths"
      
    synthesize:
      key_insights: []
      patterns_identified: []
      improvements_suggested: []
      
    persist:
      to_anchors: true              # Save as decisions
      to_digest: true               # Include in summary
      score_boost: 0.2              # Important for learning

13.2 Self-Critique Evaluation System

self_critique:
  evaluation_dimensions:
    code_quality:
      - correctness: "Does it work?"
      - efficiency: "Is it optimal?"
      - maintainability: "Is it clean?"
      
    decision_quality:
      - rationale: "Was reasoning sound?"
      - alternatives: "Were options considered?"
      - evidence: "Was it data-driven?"
      
    process_quality:
      - methodology: "Was approach systematic?"
      - tool_usage: "Were tools used effectively?"
      - time_management: "Was effort proportional?"
      
  critique_storage:
    attach_to_frame: true
    influence_scoring: true
    guide_future_retrieval: true
    
  continuous_improvement:
    track_critique_patterns: true
    adjust_weights_based_on_outcomes: true
    share_learnings_across_team: true

13.3 Rich Feedback Integration

feedback_sources:
  automated:
    - test_results
    - linting_output
    - performance_metrics
    - security_scans
    
  human:
    - code_review_comments
    - user_satisfaction
    - explicit_feedback
    
  environmental:
    - build_success_rate
    - deployment_outcomes
    - production_incidents
    
  integration:
    collection: "multi_channel"
    correlation: "cross_reference"
    weight_by_reliability: true
    
  feedback_to_memory:
    positive: "boost_frame_score"
    negative: "annotate_with_lessons"
    neutral: "record_for_pattern"

14. Context Optimization Strategies

14.1 Context Minimization Pattern

context_minimization:
  strategies:
    intelligent_filtering:
      remove_redundant: true
      compress_similar: true
      prioritize_relevant: true
      
    hierarchical_summarization:
      detail_levels:
        - full: "complete events"
        - medium: "key operations"
        - summary: "outcomes only"
        
    dynamic_windowing:
      expand_on: "high_relevance"
      contract_on: "low_relevance"
      adaptive_sizing: true
      
  benefits:
    reduced_token_usage: "40-60%"
    faster_processing: true
    clearer_focus: true

14.2 Dynamic Context Injection

dynamic_injection:
  triggers:
    - context_switch_detected
    - new_error_type
    - unfamiliar_codebase_area
    - performance_degradation
    
  injection_sources:
    - relevant_documentation
    - similar_past_solutions
    - team_knowledge_base
    - external_references
    
  injection_timing:
    just_in_time: true              # Right before needed
    predictive: true                # Anticipate needs
    on_demand: true                 # User requested
    
  injection_format:
    inline_hints: "minimal disruption"
    sidebar_context: "additional detail"
    full_frame: "comprehensive context"

14.3 Context Window Anxiety Management

anxiety_management:
  monitoring:
    track_usage: "continuous"
    alert_threshold: 70%
    critical_threshold: 90%
    
  mitigation_strategies:
    progressive_compression:
      - summarize_old_frames
      - drop_low_score_events
      - archive_to_retrieval
      
    selective_loading:
      - load_only_relevant
      - defer_deep_history
      - use_pointers_not_content
      
    smart_truncation:
      preserve: "decisions_and_outcomes"
      truncate: "intermediate_steps"
      compress: "repetitive_patterns"

15. Tool Orchestration Patterns

15.1 Progressive Tool Discovery

tool_discovery:
  learning_progression:
    basic: ["read", "write", "search"]
    intermediate: ["edit", "test", "analyze"]
    advanced: ["refactor", "optimize", "architect"]
    
  discovery_mechanism:
    observation: "watch_usage_patterns"
    suggestion: "recommend_when_relevant"
    education: "explain_tool_benefits"
    
  tool_introduction:
    gradual: true
    context_appropriate: true
    with_examples: true

15.2 Conditional Parallel Execution

parallel_execution:
  conditions:
    can_parallelize:
      - independent_files
      - different_subsystems
      - non_conflicting_operations
      
    must_serialize:
      - dependent_changes
      - shared_resources
      - ordered_operations
      
  orchestration:
    plan: "identify_parallelizable"
    execute: "batch_similar_operations"
    synchronize: "merge_results"
    handle_conflicts: "retry_or_serialize"
    
  benefits:
    speed: "3-5x improvement"
    efficiency: "reduced_overhead"
    atomicity: "group_related_changes"

16. Multi-Agent Coordination

16.1 Sub-Agent Spawning Pattern

sub_agent_spawning:
  spawn_triggers:
    - complex_subtask
    - specialized_domain
    - parallel_workstream
    - exploratory_analysis
    
  agent_types:
    analyzer: "deep_investigation"
    builder: "implementation"
    reviewer: "quality_check"
    documenter: "knowledge_capture"
    
  coordination:
    handoff: "clear_context_transfer"
    results: "structured_return"
    state: "shared_memory_access"
    
  lifecycle:
    spawn: "with_specific_context"
    execute: "autonomous_operation"
    report: "structured_findings"
    terminate: "clean_resource_release"

16.2 Multi-Agent Debate Pattern

debate_pattern:
  participants:
    proposer: "suggests_solution"
    critic: "identifies_issues"
    synthesizer: "merges_perspectives"
    
  debate_process:
    rounds: 3
    convergence_required: true
    consensus_threshold: 0.8
    
  decision_recording:
    all_perspectives: true
    final_consensus: true
    dissenting_opinions: true
    
  benefits:
    better_decisions: "multiple viewpoints"
    error_reduction: "critical analysis"
    learning: "exposed reasoning"

17. Evaluation and Scoring Evolution

17.1 Anti-Reward-Hacking Design

anti_reward_hacking:
  diverse_metrics:
    - outcome_based: "actual_results"
    - process_based: "methodology_quality"
    - efficiency_based: "resource_usage"
    - learning_based: "knowledge_gained"
    
  dynamic_weights:
    adjust_based_on:
      - gaming_detection
      - metric_reliability
      - context_importance
      
  validation:
    cross_check_metrics: true
    human_spot_checks: true
    anomaly_detection: true

17.2 Continuous Calibration

calibration:
  feedback_loop:
    collect: "outcome_data"
    analyze: "prediction_vs_actual"
    adjust: "scoring_weights"
    
  calibration_frequency:
    minor: "daily"
    major: "weekly"
    reset: "monthly"
    
  drift_detection:
    monitor: "score_distributions"
    alert: "significant_changes"
    auto_adjust: "within_bounds"

18. Future Extensibility

18.1 Roadmap Features (Enhanced)

planned_features:
  # Original features
  - cross_repository_memory
  - team_memory_spaces
  - background_project_compilers
  - fine_grained_retention_policies
  - ml_based_importance_scoring
  - predictive_context_loading
  - ide_frame_boundary_visualization
  
  # New pattern-based features
  - episodic_memory_retrieval
  - reflection_loop_automation
  - multi_agent_orchestration
  - context_anxiety_management
  - progressive_tool_discovery
  - debate_based_decision_making
  - continuous_self_improvement

18.2 Integration Points

integrations:
  current:
    - claude_code
    - linear
    - github
    
  planned:
    - vscode
    - cursor
    - jetbrains
    - gitlab
    - jira
    - slack
    
  pattern_integrations:
    - langchain: "memory_patterns"
    - autogen: "multi_agent"
    - guidance: "structured_generation"
    - dspy: "optimization_loops"

Implementation Priorities

Phase 1: Core Runtime (Current)

Phase 2: Intelligence Layer

LLM-driven retrieval
Hybrid digest generation
Smart trace detection
Configurable scoring

Phase 3: Collaboration

Shared team stacks
Frame handoff
Merge conflict resolution
Team analytics

Phase 4: Scale

Remote infinite storage
Incremental GC
Performance optimization
Enterprise features

Success Metrics

adoption:
  - daily_active_projects: 10000
  - frames_created_per_day: 1M
  - context_retrievals_per_day: 10M
  
quality:
  - retrieval_relevance: "> 90%"
  - digest_accuracy: "> 85%"
  - user_satisfaction: "> 4.5/5"
  
performance:
  - retrieval_latency: "< 100ms p50"
  - zero_context_loss: true
  - uptime: "> 99.9%"

Configuration Examples

Example 1: Security-Focused Project

scoring:
  weights:
    impact: 0.5
    persistence: 0.3
  tool_scores:
    security_scan: 0.95
    
retention:
  local:
    old: 90d  # Keep security decisions longer

Example 2: Exploration-Heavy Project

scoring:
  weights:
    reference: 0.5
    base: 0.2
  tool_scores:
    search: 0.99
    
performance:
  retrieval_timeout_ms: 1000  # Allow deeper searches

Implementation Guidance

Pattern Implementation Priority Matrix

high_impact_easy:
  # Implement first - quick wins
  - context_minimization      # 40-60% token savings
  - reflection_loop           # Improves decision quality
  - parallel_tool_execution   # 3-5x speed improvement
  - episodic_memory          # Reuse past solutions
  
high_impact_complex:
  # Phase 2 - significant value
  - self_critique_system     # Continuous improvement
  - multi_agent_debate       # Better decisions
  - dynamic_context_injection # Just-in-time context
  - log_synthesis           # Learn from patterns
  
moderate_impact:
  # Phase 3 - refinements
  - progressive_tool_discovery # Gradual capability
  - anti_reward_hacking       # Robust metrics
  - sub_agent_spawning       # Task delegation
  - context_anxiety_mgmt     # Proactive optimization

Key Design Principles from Patterns

principles:
  1_externalize_state:
    rationale: "Enable persistence across sessions"
    implementation: "Filesystem + database hybrid"
    
  2_minimize_context:
    rationale: "Maximize efficiency and clarity"
    implementation: "Hierarchical summarization"
    
  3_learn_continuously:
    rationale: "Improve over time"
    implementation: "Reflection loops + pattern extraction"
    
  4_orchestrate_intelligently:
    rationale: "Use right tool for task"
    implementation: "Progressive discovery + conditional execution"
    
  5_critique_systematically:
    rationale: "Ensure quality"
    implementation: "Multi-dimensional evaluation"

Practical Implementation Steps

step_1_baseline:
  - implement_frame_stack
  - add_basic_scoring
  - create_sqlite_storage
  - build_mcp_interface
  
step_2_memory_patterns:
  - add_episodic_retrieval
  - implement_log_synthesis
  - create_reflection_loops
  - build_pattern_detection
  
step_3_optimization:
  - add_context_minimization
  - implement_dynamic_injection
  - create_parallel_execution
  - optimize_retrieval_speed
  
step_4_intelligence:
  - add_self_critique
  - implement_debate_patterns
  - create_continuous_calibration
  - build_learning_system
  
step_5_scale:
  - add_multi_agent_coordination
  - implement_distributed_memory
  - create_team_collaboration
  - optimize_for_production

Conclusion

StackMemory provides a revolutionary approach to AI tool memory management through:

Lossless storage with smart retrieval
Frame-based organization replacing linear chat logs
Two-tier storage balancing performance and capacity
LLM-driven context selection for optimal relevance
Team collaboration through shared and individual stacks
Configurable scoring adapting to project needs
Advanced patterns from agentic AI research
Continuous learning through reflection and synthesis
Intelligent orchestration of tools and agents
Context optimization for efficiency at scale

The system ensures AI tools never lose context while maintaining performance at scale, incorporating state-of-the-art patterns from the agentic AI community.

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