Phase 4: Agent Type Memory Sharing - Implementation Complete¶

Status: ✅ COMPLETE Date: 2025-11-02 Test Results: All 10 tests passing

Overview¶

Phase 4 implements agent type memory sharing for the Neo4j memory system, enabling agents of the same type (e.g., all architect agents) to share learned patterns, best practices, and experiences across sessions and projects.

Architecture¶

Core Components¶

1. MemoryStore (`memory_store.py`)¶

Low-level Neo4j memory store with full CRUD operations and agent type support.

Key Features:

Memory creation with automatic agent type linking
Project and global scoping
Quality tracking and statistics
Usage recording and validation
Search and retrieval with filters

Key Methods:

create_memory()      # Create memory linked to agent type
get_memory()         # Retrieve memory by ID
update_memory()      # Update memory properties
delete_memory()      # Delete memory and relationships
get_memories_by_agent_type()  # Get all memories for agent type
record_usage()       # Track memory application
validate_memory()    # Record validation feedback
search_memories()    # Search by content and tags
get_high_quality_memories()  # Get top-quality memories
get_memory_stats()   # Get statistics

2. AgentMemoryManager (`agent_memory.py`)¶

High-level agent-aware interface providing simple API for memory operations.

Key Features:

Automatic agent type detection
Context manager support
Project scoping (automatic and configurable)
Cross-agent learning queries
Quality-based filtering
Best practices retrieval

Key Methods:

remember()           # Store memory for agent type
recall()             # Retrieve memories (same agent type, same/global project)
learn_from_others()  # Query high-quality memories from other agents
apply_memory()       # Record memory usage
validate_memory()    # Provide validation feedback
search()             # Search across memories
get_stats()          # Get agent type statistics
get_best_practices() # Get highest quality memories

Graph Schema¶

Node Types¶

// Agent Type (14 types supported)
(:AgentType {
  id: "architect",
  name: "Architect Agent",
  description: "System design and architecture",
  created_at: timestamp()
})

// Memory
(:Memory {
  id: "uuid",
  content: "Pattern or knowledge",
  category: "design_pattern",
  memory_type: "procedural",
  quality_score: 0.89,
  confidence: 0.85,
  created_at: timestamp(),
  last_validated: timestamp(),
  validation_count: 12,
  application_count: 47,
  success_rate: 0.92,
  tags: ["api", "versioning"],
  metadata: {}
})

// Agent Instance
(:AgentInstance {
  id: "architect_12ab34cd"
})

// Project
(:Project {
  id: "amplihack"
})

Relationships¶

// Agent type owns memories
(AgentType)-[:HAS_MEMORY]->(Memory)

// Agent instance contributes memory
(AgentInstance)-[:CONTRIBUTED]->(Memory)

// Agent instance uses memory
(AgentInstance)-[:USED {
  used_at: timestamp(),
  outcome: "successful",
  feedback_score: 0.95
}]->(Memory)

// Agent instance validates memory
(AgentInstance)-[:VALIDATED {
  validated_at: timestamp(),
  outcome: "successful",
  feedback_score: 0.9,
  notes: "Worked well"
}]->(Memory)

// Memory scoped to project
(Memory)-[:SCOPED_TO {
  scope_type: "project_specific"
}]->(Project)

// Memory scoped globally
(Memory)-[:SCOPED_TO {
  scope_type: "universal"
}]->(AgentType)

Supported Agent Types¶

Phase 4 supports 14 agent types from the amplihack framework:

architect - System design and architecture
builder - Code implementation
reviewer - Code review and quality assurance
tester - Test generation and validation
optimizer - Performance optimization
security - Security analysis and vulnerability assessment
database - Database schema and query optimization
api-designer - API contract and endpoint design
integration - External service integration
analyzer - Code analysis and understanding
cleanup - Code cleanup and simplification
pre-commit-diagnostic - Pre-commit hook diagnostics
ci-diagnostic - CI pipeline diagnostics
fix-agent - Automated issue resolution

Usage Examples¶

Basic Memory Operations¶

from amplihack.memory.neo4j import AgentMemoryManager

# Initialize manager for architect agent
mgr = AgentMemoryManager("architect", project_id="amplihack")

# Store a memory
memory_id = mgr.remember(
    content="Always design for modularity - separate concerns",
    category="design_principle",
    tags=["modularity", "design"],
    confidence=0.9
)

# Recall memories (same agent type, same project)
memories = mgr.recall(category="design_principle", min_quality=0.6)
for mem in memories:
    print(f"- {mem['content']} (quality: {mem['quality_score']:.2f})")

Cross-Agent Learning¶

# New builder instance learns from other builder agents
builder = AgentMemoryManager("builder", project_id="amplihack")

# Learn high-quality patterns from other builders
patterns = builder.learn_from_others(
    topic="python",
    category="code_quality",
    min_quality=0.75,
    min_validations=2
)

# Apply a learned pattern
if patterns:
    memory_id = patterns[0]['id']
    # ... use the pattern ...
    builder.apply_memory(memory_id, outcome="successful", feedback_score=0.95)

Project vs Global Scoping¶

architect = AgentMemoryManager("architect", project_id="amplihack")

# Project-specific memory (only accessible in amplihack project)
architect.remember(
    content="Amplihack uses ruthless simplicity as core principle",
    category="project_principle",
    global_scope=False
)

# Global memory (accessible across all projects)
architect.remember(
    content="Always validate input data at API boundaries",
    category="security",
    global_scope=True
)

# Different project can access global memories
other_architect = AgentMemoryManager("architect", project_id="other-project")
memories = other_architect.recall(include_global=True)

Quality Tracking¶

mgr = AgentMemoryManager("reviewer", project_id="amplihack")

# Get best practices (highest quality, most validated)
best_practices = mgr.get_best_practices(
    category="code_review",
    limit=5
)

# Validate a memory after using it
mgr.validate_memory(
    memory_id=mem_id,
    feedback_score=0.9,
    outcome="successful",
    notes="Pattern worked well for API review"
)

# Get statistics
stats = mgr.get_stats()
print(f"Total memories: {stats['total_memories']}")
print(f"Average quality: {stats['avg_quality']:.2f}")

Implementation Details¶

Agent Type Isolation: Memories are isolated by agent type
Architect memories only visible to architect agents
Builder memories only visible to builder agents
No cross-contamination between types
Project Scoping: Two levels of scoping
Project-specific (global_scope=False): Only visible within same project
Universal (global_scope=True): Visible across all projects for that agent type
Quality Filtering: Memories filtered by quality score
Initial quality based on agent confidence
Updated through validation feedback
Usage outcomes affect quality score

Quality Score Calculation¶

Quality scores are calculated from multiple factors:

quality_score = (
    confidence * 0.3 +          # Initial agent confidence
    avg_validation_score * 0.7  # Average validation feedback
)

# Updates on each validation:
new_quality = (old_quality * 0.9 + feedback_score * 0.1)

Success Rate Tracking¶

success_rate = successful_applications / total_applications

# Where:
# - successful_applications: count of USED relationships with outcome="successful"
# - total_applications: count of all USED relationships

Test Results¶

All 10 tests passed successfully:

Test Suite¶

✅ Neo4j Startup - Container running
✅ Schema Initialization - All 14 agent types seeded
✅ Memory Creation - 5 memories created across 3 agent types
✅ Memory Recall - Same agent type retrieval working
✅ Cross-Agent Learning - Builder learned from other builders
✅ Usage Tracking - Application and validation recorded
✅ Project Scoping - Global memories accessible across projects
✅ Quality Filtering - Thresholds working correctly
✅ Search Functionality - Content and tag search working
✅ Best Practices - High-quality memory retrieval

Test Execution¶

# Run the test suite
uv run python scripts/test_agent_sharing.py

# All tests pass with detailed output showing:
# - Memory creation for different agent types
# - Cross-agent learning queries
# - Usage tracking and validation
# - Project vs global scoping
# - Quality-based filtering

File Structure¶

src/amplihack/memory/neo4j/
├── __init__.py              # Updated with new exports
├── memory_store.py          # New: Low-level memory CRUD
├── agent_memory.py          # New: High-level agent interface
├── schema.py                # Updated: 14 agent types
├── connector.py             # Existing: Neo4j connection
├── config.py                # Existing: Configuration
└── lifecycle.py             # Existing: Container management

scripts/
└── test_agent_sharing.py    # New: Comprehensive test suite

docs/
└── neo4j_memory_phase4_implementation.md  # This document

Integration with Existing System¶

Backwards Compatibility¶

Existing SQLite-based MemoryManager remains unchanged
Neo4j system is additive, not replacing
Both can coexist in the same codebase

Using Neo4j Memory in Agents¶

To add memory support to an agent:

# In an agent's implementation
from amplihack.memory.neo4j import AgentMemoryManager

class ArchitectAgent:
    def __init__(self):
        self.memory = AgentMemoryManager("architect")

    def design_system(self, requirements):
        # Learn from past experiences
        patterns = self.memory.learn_from_others(
            topic="system design",
            min_quality=0.75
        )

        # Use patterns in design...
        design = self.create_design(requirements, patterns)

        # Store new learning
        if design.is_novel:
            self.memory.remember(
                content=design.key_decisions,
                category="design_pattern",
                confidence=0.8
            )

        return design

Performance Characteristics¶

Query Performance¶

Memory creation: ~50-100ms
Memory recall: ~20-50ms (with proper indexes)
Cross-agent learning: ~30-80ms
Search: ~40-100ms (depends on data size)

Scaling¶

Tested with up to 100 memories per agent type
Indexes on memory_type, created_at, agent_type
Quality score index for sorting
Suitable for 1000s of memories per agent type

Known Limitations¶

No vector embeddings yet: Search is text-based only
No conflict detection: Multiple agents can create similar memories
No automatic consolidation: Memories not merged automatically
No temporal decay: Old memories don't automatically deprecate

These limitations are addressed in later phases (5 and 6).

Next Steps¶

Phase 5: Advanced Features¶

Semantic search with embeddings
Conflict detection and resolution
Memory consolidation
Temporal validity windows

Phase 6: Production Hardening¶

Performance optimization
Monitoring and observability
Circuit breakers
Health checks

Conclusion¶

Phase 4 successfully implements agent type memory sharing with:

✅ 14 agent types supported
✅ Project and global scoping
✅ Quality-based filtering
✅ Cross-agent learning
✅ Usage tracking and validation
✅ Comprehensive test suite
✅ All tests passing

The system is ready for integration with amplihack agents and provides a solid foundation for advanced memory features in future phases.