mcal-ai-crewai 0.2.0

CrewAI integration for MCAL - Goal-aware memory for AI agent crews

mcal-crewai

Goal-aware memory integration for CrewAI agent crews.

Installation

pip install mcal-crewai

Quick Start

Using MCALStorage (Mem0-style)

MCAL provides a storage backend that integrates directly with CrewAI's memory system:

from crewai import Crew, Agent, Task, Process
from crewai.memory.short_term.short_term_memory import ShortTermMemory
from crewai.memory.long_term.long_term_memory import LongTermMemory
from crewai.memory.entity.entity_memory import EntityMemory
from mcal_crewai import MCALStorage

# Create MCAL-backed memories
short_term = ShortTermMemory(
    storage=MCALStorage(type="short_term", user_id="john")
)
long_term = LongTermMemory(
    storage=MCALStorage(type="long_term", user_id="john")
)
entity_memory = EntityMemory(
    storage=MCALStorage(type="entities", user_id="john")
)

# Use with CrewAI
crew = Crew(
    agents=[agent],
    tasks=[task],
    memory=True,
    short_term_memory=short_term,
    long_term_memory=long_term,
    entity_memory=entity_memory,
)

Using External Memory

For cross-session persistence with goal awareness:

from crewai.memory.external.external_memory import ExternalMemory
from mcal_crewai import MCALStorage

external = ExternalMemory(
    embedder_config={
        "provider": "mcal",
        "config": {
            "user_id": "john",
            "llm_provider": "anthropic",
            "enable_goal_tracking": True,
        }
    }
)

crew = Crew(
    agents=[...],
    tasks=[...],
    external_memory=external,
    process=Process.sequential,
)

Features

Goal-Aware Memory

Unlike basic memory systems, MCAL tracks user goals and priorities:

storage = MCALStorage(
    type="long_term",
    user_id="project_manager",
    config={
        "enable_goal_tracking": True,
        "extract_priorities": True,
    }
)

Context Preservation

MCAL maintains reasoning context across agent handoffs:

# Agent 1 saves with context
await storage.save(
    "Research findings on market trends",
    metadata={
        "agent": "researcher",
        "goal": "market_analysis",
        "confidence": 0.95
    }
)

# Agent 2 retrieves with goal awareness
results = await storage.search(
    "What do we know about market trends?",
    limit=5,
    score_threshold=0.7
)

TTL Support

Automatic expiration for short-term memories:

storage = MCALStorage(
    type="short_term",
    user_id="session_user",
    default_ttl=3600,  # 1 hour
)

Configuration

Parameter	Type	Default	Description
type	str	required	Memory type: "short_term", "long_term", "entities", "external"
user_id	str	"default"	User identifier for memory isolation
llm_provider	str	"anthropic"	LLM for goal extraction
embedding_provider	str	"openai"	Embedding model provider
default_ttl	int	None	Default TTL in seconds
enable_goal_tracking	bool	True	Enable goal extraction

API Reference

MCALStorage

class MCALStorage(Storage):
    """MCAL storage backend for CrewAI memory."""
    
    def save(self, value: Any, metadata: dict) -> None:
        """Save value with goal-aware processing."""
    
    def search(
        self, 
        query: str, 
        limit: int = 5, 
        score_threshold: float = 0.6
    ) -> list:
        """Search with goal-aware relevance."""
    
    def reset(self) -> None:
        """Clear all stored memories."""

Comparison with Mem0

Feature	Mem0	MCAL
Basic Memory	✓	✓
Goal Tracking	✗	✓
Priority Extraction	✗	✓
Context Preservation	✗	✓
TTL Support	✗	✓
Local Storage	✓	✓
Cloud API	✓	Coming

License

MIT License