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CrewAI Integration

Z3rno provides Z3rnoCrewAIStorage, a storage adapter that implements CrewAI’s memory interface. This lets multiple CrewAI agents share persistent memory backed by Z3rno.

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Installation

pip install z3rno
pip install crewai

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Setup

from z3rno import Z3rnoClient
from z3rno.integrations.crewai import Z3rnoCrewAIStorage

client = Z3rnoClient(
    base_url="http://localhost:8000",
    api_key="z3rno_sk_...",
)

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Memory Type Mapping

CrewAI’s memory categories map to Z3rno memory types:
CrewAI CategoryZ3rno Memory TypeUse Case
short_termworkingActive task context
long_termepisodicInteraction history
entitysemanticFacts and knowledge

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Basic Usage

# Short-term memory (maps to Z3rno "working" type)
short_term = Z3rnoCrewAIStorage(
    client,
    agent_id="researcher",
    memory_type="short_term",
)

# Long-term memory (maps to Z3rno "episodic" type)
long_term = Z3rnoCrewAIStorage(
    client,
    agent_id="researcher",
    memory_type="long_term",
)

# Entity memory (maps to Z3rno "semantic" type)
entity = Z3rnoCrewAIStorage(
    client,
    agent_id="researcher",
    memory_type="entity",
)

# Save a memory
result = short_term.save(
    key="current_task",
    value="Researching competitor landscape in AI memory space",
    metadata={"priority": "high"},
)
print(f"Stored: {result['id']}")

# Search memories by semantic similarity
results = short_term.search(
    query="What am I currently working on?",
    limit=5,
    score_threshold=0.3,
)
for r in results:
    print(f"  {r['content']} (score: {r['score']:.2f})")

# Reset all memories for this agent/type
short_term.reset()

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Shared Memory Across Agents

The primary advantage of using Z3rno with CrewAI is that multiple agents can share a memory store. Use the same agent_id across agents to create a shared memory space, or use different agent_id values for isolated memories. 
# Shared memory: all crew agents read/write the same store
shared_store = Z3rnoCrewAIStorage(
    client,
    agent_id="market-research-crew",  # Same agent_id = shared memory
    memory_type="entity",
)

# Agent 1 (Researcher) stores findings
shared_store.save(
    key="competitor_1",
    value="Mem0 offers open-source agent memory with a managed cloud option.",
    metadata={"source": "web_search", "agent": "researcher"},
)

shared_store.save(
    key="competitor_2",
    value="Zep provides long-term memory for AI assistants with temporal awareness.",
    metadata={"source": "web_search", "agent": "researcher"},
)

# Agent 2 (Analyst) recalls shared findings
competitors = shared_store.search(
    query="What competitors exist in the AI memory space?",
    limit=10,
)
for c in competitors:
    print(f"  [{c['metadata'].get('agent')}] {c['content']}")

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With CrewAI’s Memory System

When configuring a CrewAI Crew, pass the Z3rno storage as the memory backend: 
from crewai import Agent, Crew, Task

researcher = Agent(
    role="Market Researcher",
    goal="Find and analyze competitors",
    backstory="Expert market analyst",
)

analyst = Agent(
    role="Strategy Analyst",
    goal="Synthesize research into insights",
    backstory="Senior strategy consultant",
)

# Both agents share memory via Z3rno
crew = Crew(
    agents=[researcher, analyst],
    tasks=[...],
    memory=True,
    short_term_memory=Z3rnoCrewAIStorage(client, agent_id="crew-1", memory_type="short_term"),
    long_term_memory=Z3rnoCrewAIStorage(client, agent_id="crew-1", memory_type="long_term"),
    entity_memory=Z3rnoCrewAIStorage(client, agent_id="crew-1", memory_type="entity"),
)

result = crew.kickoff()

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Next Steps