Memory for CrewAI Agents

CrewAI lets you orchestrate multi-agent teams — but every crew execution starts from zero. Past runs, past learnings, past mistakes: all gone. 0Latency gives your crews persistent, shared memory that compounds over time.

The Problem: Crews That Never Learn

CrewAI is built around the idea that multiple specialized agents working together produce better results than a single generalist. A researcher, a writer, a reviewer — each with their own role, tools, and goals. The framework handles orchestration. But it doesn't handle memory.

Every time you call crew.kickoff(), it's a clean slate. The researcher doesn't remember what sources were unreliable last time. The writer doesn't remember the client's style preferences. The reviewer doesn't remember which quality issues kept recurring. Each run is independent, and so each run repeats the same mistakes.

This matters most in iterative workflows:

• Content generation crews that produce weekly reports — but can't learn from editorial feedback on previous reports.
• Research crews that re-discover the same dead ends because nobody wrote down "this API is deprecated" or "that data source is unreliable."
• Code review crews that flag the same false positives every run because they don't remember the codebase's conventions.
• Sales outreach crews that can't learn from which approaches worked and which got ignored.

CrewAI does offer a memory parameter and some built-in memory types (short-term, long-term, entity memory). But these are basic — flat storage with keyword search, no temporal dynamics, no contradiction handling, no way to share structured learnings across different crew members intelligently.

The Solution: Wrap Your Crew with 0Latency

0Latency integrates with CrewAI at the crew level. Before a run, relevant memories are recalled and injected into agent backstories. After a run, learnings are extracted from the output and stored. The integration takes about 10 lines of Python.

pip install zero-latency-sdk crewai

from crewai import Agent, Task, Crew
from zero_latency import Memory

# Initialize — that's all the setup you need
mem = Memory("your-api-key")

# Recall relevant context — sub-100ms
crew_memories = mem.recall("weekly market analysis report")

# Create agents with memory-enriched backstories
researcher = Agent(
    role="Senior Market Researcher",
    goal="Find the most impactful market developments this week",
    backstory=f"""You are an experienced market researcher.
    
Learnings from previous runs:
{crew_memories.text}""",
    verbose=True
)

writer = Agent(
    role="Report Writer",
    goal="Produce a clear, actionable market analysis report",
    backstory=f"""You write concise market reports for executives.
    
Context from previous reports:
{crew_memories.text}""",
    verbose=True
)

# Define tasks
research_task = Task(
    description="Research key market developments from the past week",
    expected_output="Bullet-point summary of 5-7 key developments with sources",
    agent=researcher
)

write_task = Task(
    description="Write a 500-word market analysis report based on the research",
    expected_output="Formatted market analysis report",
    agent=writer
)

# Build and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    verbose=True
)

result = crew.kickoff()

# Store learnings — returns instantly, processes in background
mem.add(f"""Crew run completed. 
Research findings: {research_task.output}
Final report: {result}
""")

print(result)

That's it. The next time this crew runs, recall() will surface learnings from this run — what sources were useful, what the client's preferences were, what quality issues were flagged. The crew gets smarter with every execution.

The Multi-Agent Advantage: Shared Organizational Memory

Here's where 0Latency really shines with CrewAI: organizational memory. When you set an org_id, memories are shared across all agents in that organization. This means:

This is what "organizational memory" actually means — not a shared database that agents can query, but a living knowledge base that proactively surfaces the right context at the right time.

What Changes When Crews Remember

• Compound learning. Each run builds on the last. Run 1 might be mediocre. Run 10 is excellent — because the crew has accumulated learnings about what works, what doesn't, and what the user expects.
• Fewer repeated mistakes. If a crew makes an error and gets corrected, that correction is stored and recalled for future runs. The same mistake shouldn't happen twice.
• Faster execution. Crews don't waste time re-discovering things they already know. The researcher doesn't need to re-evaluate sources that were already ranked. The writer doesn't need to re-learn the client's tone preference.
• Temporal awareness. 0Latency's memory decay means stale learnings naturally fade while frequently-reinforced ones stay sharp. A preference that hasn't been relevant in months won't clutter the context.
• Contradiction handling. When a user changes a preference ("actually, make the reports shorter"), 0Latency flags the contradiction and updates the current state. Crews always work with the latest information.

Advanced: Per-Agent Memory

For more granular control, you can give each agent within a crew its own memory context:

# Recall agent-specific memories — sub-100ms each
researcher_mem = mem.recall("market research sources and methodology")

writer_mem = mem.recall("report writing style and format preferences")

# Inject into individual backstories
researcher = Agent(
    role="Senior Market Researcher",
    backstory=f"...\n\nYour learnings:\n{researcher_mem.text}",
    ...
)

writer = Agent(
    role="Report Writer",
    backstory=f"...\n\nYour learnings:\n{writer_mem.text}",
    ...
)

Tags let you segment memories by agent role, task type, or any other dimension. The researcher sees research-specific learnings; the writer sees writing-specific ones. Both still benefit from shared organizational memory.

Pricing

Free tier: 1,000 memories, 10,000 recall queries/month. For a crew that runs a few times per day, this is plenty. Pro ($29/month) unlocks unlimited — suitable for production deployments with multiple crews and high-frequency execution.