Memory Configuration

Complete reference for memory system configuration including graph stores, quality scores, and embedders

Memory Configuration

This page provides comprehensive documentation for configuring the PraisonAI memory system, including graph database integration, quality scoring mechanisms, and embedder options.

Memory System Overview

The memory system in PraisonAI supports multiple storage backends and sophisticated retrieval mechanisms:

• Short-term Memory: Recent interactions and context
• Long-term Memory: Persistent knowledge storage
• Entity Memory: Relationship and entity tracking
• Graph Memory: Complex relationship networks
• User Memory: User-specific preferences and history

Core Configuration

Basic Memory Setup

from praisonaiagents import Agent

agent = Agent(
    name="MemoryAgent",
    memory=True,  # Enable memory
    memory={
        "provider": "rag",
        "use_embedding": True,
        "embedding_model": "text-embedding-3-small",
        "quality_threshold": 0.7,
        "max_results": 10
    }
)

Installing Optional Providers

Most memory providers require optional dependencies:

pip install "praisonaiagents[memory]"

If you try to use a provider without the required packages, you’ll see:

ImportError: mem0ai is not installed. Run: pip install 'praisonaiagents[memory]'

When a provider is explicitly requested (e.g. "provider": "mem0"), PraisonAI will raise this error instead of silently falling back. The default provider ("provider": "rag") falls back gracefully to built-in storage.

Provider Configuration

# RAG Provider (Default)
rag_config = {
    "provider": "rag",
    "rag_db_path": "./memory/rag_db",
    "chunk_size": 1000,
    "chunk_overlap": 200,
    "use_semantic_chunking": True
}

# Mem0 Provider
# Requires: pip install "praisonaiagents[memory]"
mem0_config = {
    "provider": "mem0",
    "api_key": "your-mem0-key",
    "user_id": "user-123",
    "organization_id": "org-456"
}

# Dakera Provider
# Requires: pip install "praisonaiagents[dakera]"
dakera_config = {
    "provider": "dakera",
    "config": {
        "url": "http://localhost:3000",
        "api_key": "dk-...",
        "agent_id": "my-agent",
        "short_term_type": "working",
        "long_term_type": "episodic",
        "default_importance": 0.5,
    }
}

# Custom Provider
custom_config = {
    "provider": "custom",
    "class": "myapp.CustomMemoryProvider",
    "connection_string": "custom://localhost:8080"
}

Graph Store Configuration

Graph Database Setup

# Neo4j Configuration
neo4j_config = {
    "graph_enabled": True,
    "graph_provider": "neo4j",
    "graph_uri": "bolt://localhost:7687",
    "graph_user": "neo4j",
    "graph_password": "password",
    "graph_database": "memories",
    "graph_config": {
        "max_connection_lifetime": 3600,
        "max_connection_pool_size": 50,
        "connection_acquisition_timeout": 60,
        "encrypted": True
    }
}

# Memgraph Configuration
memgraph_config = {
    "graph_enabled": True,
    "graph_provider": "memgraph",
    "graph_uri": "bolt://localhost:7687",
    "graph_user": "memgraph",
    "graph_password": "password",
    "graph_config": {
        "lazy": True,
        "ssl": False
    }
}

Graph Schema Definition

graph_schema = {
    "nodes": {
        "Entity": {
            "properties": ["name", "type", "description", "created_at"],
            "indexes": ["name", "type"]
        },
        "Concept": {
            "properties": ["name", "definition", "category"],
            "indexes": ["name", "category"]
        },
        "Event": {
            "properties": ["name", "timestamp", "description", "participants"],
            "indexes": ["timestamp"]
        }
    },
    "relationships": {
        "RELATES_TO": {
            "properties": ["strength", "context", "timestamp"]
        },
        "CAUSES": {
            "properties": ["probability", "mechanism"]
        },
        "PARTICIPATES_IN": {
            "properties": ["role", "duration"]
        }
    }
}

Advanced Graph Queries

# Complex graph query configuration
graph_query_config = {
    "retrieval_queries": {
        "find_related_entities": """
            MATCH (e1:Entity {name: $entity_name})-[r:RELATES_TO*1..3]-(e2:Entity)
            WHERE r.strength > $min_strength
            RETURN e2, r
            ORDER BY r.strength DESC
            LIMIT $limit
        """,
        "trace_causality": """
            MATCH path = (cause:Event)-[:CAUSES*1..5]->(effect:Event)
            WHERE cause.name = $event_name
            RETURN path
        """
    },
    "update_patterns": {
        "strengthen_relationship": """
            MATCH (e1:Entity {name: $entity1})-[r:RELATES_TO]-(e2:Entity {name: $entity2})
            SET r.strength = r.strength * $multiplier
            SET r.last_accessed = timestamp()
        """
    }
}

Quality Score Configuration

Quality Scoring System

quality_config = {
    "quality_threshold": 0.7,  # Minimum score for retrieval
    "scoring_weights": {
        "relevance": 0.4,
        "recency": 0.2,
        "frequency": 0.2,
        "confidence": 0.2
    },
    "scoring_functions": {
        "relevance": "cosine_similarity",
        "recency": "exponential_decay",
        "frequency": "log_normalization",
        "confidence": "source_reliability"
    }
}

Quality Score Components

Relevance Scoring

relevance_config = {
    "method": "hybrid",
    "components": {
        "semantic": {
            "weight": 0.6,
            "model": "text-embedding-3-small",
            "distance_metric": "cosine"
        },
        "keyword": {
            "weight": 0.3,
            "algorithm": "bm25",
            "parameters": {"k1": 1.2, "b": 0.75}
        },
        "structural": {
            "weight": 0.1,
            "factors": ["title_match", "section_match"]
        }
    }
}

Recency Scoring

recency_config = {
    "decay_function": "exponential",
    "half_life_days": 30,
    "boost_recent": {
        "last_hour": 2.0,
        "last_day": 1.5,
        "last_week": 1.2
    }
}

Confidence Scoring

confidence_config = {
    "source_weights": {
        "verified": 1.0,
        "agent_generated": 0.8,
        "external": 0.6,
        "unverified": 0.4
    },
    "consensus_threshold": 3,  # Number of sources for high confidence
    "contradiction_penalty": 0.5
}

Embedder Configuration

Embedder Options

# OpenAI Embedder
openai_embedder = {
    "provider": "openai",
    "model": "text-embedding-3-large",
    "dimensions": 3072,
    "api_key": "your-api-key",
    "batch_size": 100,
    "retry_config": {
        "max_retries": 3,
        "retry_delay": 1.0
    }
}

# Local Embedder
local_embedder = {
    "provider": "sentence-transformers",
    "model": "all-MiniLM-L6-v2",
    "device": "cuda",  # or "cpu"
    "normalize_embeddings": True,
    "batch_size": 32
}

Memory sizes its vector store from get_dimensions(embedding_model). Before PraisonAI PR #3027 (releases after 2026-07-14), two lookup bugs produced dimension mismatches on memory / RAG upsert:

• Case-sensitive keys — all-MiniLM-L6-v2 and sentence-transformers/all-MiniLM-L6-v2 fell through to the 1536 default even though the model emits 384.
• Substring collision — voyage/voyage-3-lite (512) matched the shorter voyage-3 (1024) entry first, so memory collections were sized 1024 while the model emits 512. Any voyage-3-lite, bge-small-en-v1.5, or other short-suffix model created on an older release needs to be re-created after upgrading.

Upgrade praisonaiagents and re-create the collection to fix either failure mode.

voyage-3-lite was affected by a related bug on the same release: the substring lookup returned 1024 (voyage-3’s dimension) instead of 512. Fixed in the follow-up commits of PR #3027 (praisonaiagents after 1.6.151). Same recovery — upgrade praisonaiagents and re-create any collection that was created 1024-dim with a voyage-3-lite embedding.

# Custom Embedder
custom_embedder = {
    "provider": "custom",
    "class": "myapp.embedders.DomainSpecificEmbedder",
    "model_path": "/models/domain-embedder",
    "preprocessing": {
        "lowercase": True,
        "remove_punctuation": True,
        "custom_tokenizer": "medical"
    }
}

Multi-Modal Embedding

multimodal_config = {
    "embedders": {
        "text": {
            "provider": "openai",
            "model": "text-embedding-3-small",
            "weight": 0.6
        },
        "image": {
            "provider": "clip",
            "model": "openai/clip-vit-base-patch32",
            "weight": 0.4
        }
    },
    "fusion_method": "weighted_average",
    "normalize_before_fusion": True
}

Storage Configuration

Database Paths and Structure

storage_config = {
    # SQLite databases
    "short_db": "./memory/short_term.db",
    "long_db": "./memory/long_term.db",
    "entity_db": "./memory/entities.db",
    "user_db": "./memory/users.db",
    
    # Vector storage
    "vector_db": {
        "provider": "chromadb",
        "path": "./memory/vectors",
        "collection_name": "memories"
    },
    
    # Backup configuration
    "backup": {
        "enabled": True,
        "interval": "daily",
        "retention_days": 30,
        "path": "./memory/backups"
    }
}

Memory Persistence

persistence_config = {
    "auto_save": True,
    "save_interval": 300,  # seconds
    "compression": "gzip",
    "encryption": {
        "enabled": True,
        "algorithm": "AES-256",
        "key_derivation": "PBKDF2"
    },
    "migration": {
        "auto_migrate": True,
        "backup_before_migration": True
    }
}

Performance Optimization

Caching Configuration

cache_config = {
    "embedding_cache": {
        "enabled": True,
        "size": 10000,
        "ttl": 3600,  # seconds
        "eviction": "lru"
    },
    "query_cache": {
        "enabled": True,
        "size": 1000,
        "ttl": 1800,
        "cache_similarity_threshold": 0.95
    },
    "graph_cache": {
        "enabled": True,
        "cached_patterns": ["neighbors", "paths", "subgraphs"],
        "ttl": 900
    }
}

Indexing Strategy

indexing_config = {
    "vector_index": {
        "type": "hnsw",
        "parameters": {
            "m": 16,
            "ef_construction": 200,
            "ef_search": 100
        }
    },
    "text_index": {
        "type": "inverted",
        "analyzer": "standard",
        "features": ["positions", "frequencies"]
    },
    "rebuild_schedule": "weekly"
}

Memory Cleanup and Maintenance

Cleanup Configuration

cleanup_config = {
    "ttl": {
        "short_term": 86400,  # 1 day in seconds
        "long_term": 2592000,  # 30 days
        "entity": None,  # No expiry
        "user": 31536000  # 1 year
    },
    "cleanup_strategy": {
        "method": "quality_based",
        "min_quality_score": 0.3,
        "max_memory_size": "10GB",
        "cleanup_interval": "daily"
    },
    "compression": {
        "enabled": True,
        "threshold_days": 7,
        "algorithm": "zstd"
    }
}

Complete Configuration Example

from praisonaiagents import Agent

# Comprehensive memory configuration
memory_agent = Agent(
    name="AdvancedMemoryAgent",
    memory=True,
    memory={
        # Provider settings
        "provider": "rag",
        "use_embedding": True,
        
        # Storage paths
        "rag_db_path": "./memory/rag_db",
        "short_db": "./memory/short_term.db",
        "long_db": "./memory/long_term.db",
        
        # Graph configuration
        "graph_enabled": True,
        "graph_provider": "neo4j",
        "graph_uri": "bolt://localhost:7687",
        "graph_user": "neo4j",
        "graph_password": "password",
        
        # Quality scoring
        "quality_threshold": 0.7,
        "scoring_weights": {
            "relevance": 0.4,
            "recency": 0.2,
            "frequency": 0.2,
            "confidence": 0.2
        },
        
        # Embedder configuration
        "embedder_config": {
            "provider": "openai",
            "model": "text-embedding-3-small",
            "batch_size": 100
        },
        
        # Performance settings
        "cache_enabled": True,
        "max_results": 20,
        "parallel_retrieval": True,
        
        # Maintenance
        "cleanup_interval": "daily",
        "ttl": {
            "short_term": 86400,
            "long_term": 2592000
        }
    }
)

Environment Variables

# Memory provider
export PRAISONAI_MEMORY_PROVIDER="rag"
export PRAISONAI_MEMORY_PATH="./memory"

# Dakera memory provider (DAKERA_URL and DAKERA_API_URL are both accepted)
export DAKERA_URL="http://localhost:3000"
# export DAKERA_API_URL="http://localhost:3000"  # alias for DAKERA_URL
export DAKERA_API_KEY="dk-..."
export DAKERA_AGENT_ID="my-agent"

# Graph database
export PRAISONAI_GRAPH_ENABLED="true"
export PRAISONAI_GRAPH_URI="bolt://localhost:7687"
export PRAISONAI_GRAPH_USER="neo4j"
export PRAISONAI_GRAPH_PASSWORD="password"

# Quality settings
export PRAISONAI_MEMORY_QUALITY_THRESHOLD="0.7"
export PRAISONAI_MEMORY_MAX_RESULTS="10"

# Embedder
export PRAISONAI_EMBEDDER_PROVIDER="openai"
export PRAISONAI_EMBEDDER_MODEL="text-embedding-3-small"

# Performance
export PRAISONAI_MEMORY_CACHE_ENABLED="true"
export PRAISONAI_MEMORY_CACHE_SIZE="10000"

Troubleshooting

Common Issues

1. Graph connection failures

   # Add connection retry logic
   "graph_config": {
       "retry_attempts": 5,
       "retry_delay": 2,
       "health_check_interval": 60
   }
   

2. Memory retrieval too slow

   # Enable parallel retrieval and caching
   "parallel_retrieval": True,
   "cache_enabled": True,
   "index_type": "hnsw"
   

3. Quality scores too low

   # Adjust scoring weights and thresholds
   "quality_threshold": 0.5,  # Lower threshold
   "scoring_weights": {
       "relevance": 0.6,  # Increase relevance weight
       "recency": 0.2,
       "frequency": 0.1,
       "confidence": 0.1
   }
   

See Also

• Memory Concepts - Understanding memory types
• Knowledge Base - Knowledge management
• Best Practices - Configuration guidelines