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Documentation Index

Fetch the complete documentation index at: https://docs.memanto.ai/llms.txt

Use this file to discover all available pages before exploring further.

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CrewAI + Memanto

CrewAI Give your CrewAI agents persistent, cross-session memory powered by Memanto using the official crewai-memanto package. By default, CrewAI agents lose context when a crew run ends. With Memanto, agents can store facts, decisions, and preferences — and recall them in future runs.
Important: We recommend explicitly setting memory=False on your CrewAI Crew objects. This prevents CrewAI from auto-injecting its own temporary LanceDB memory tools, which can confuse agents when mixed with Memanto’s persistent memory tools.

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How It Works

CrewAI Agent → Memanto Tools (remember / recall / answer) → Memanto Server → Moorcheh.ai
Each agent in your crew gets access to three tools: one to store memories, one to search them, and one to get a synthesized answer directly from memory. Memanto handles the semantic layer — no vector DB setup required.

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Prerequisites

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Install

pip install crewai-memanto

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Quick Start

The crewai-memanto package provides pre-built tools that wrap Memanto’s SDK. You don’t need to make HTTP requests manually. 
import os
from crewai import Agent, Task, Crew
from crewai_memanto import MemantoSetup, create_memanto_tools

# 1. Set up Memanto (one-time per session)
api_key = os.getenv("MOORCHEH_API_KEY")
setup = MemantoSetup(api_key=api_key)
client = setup.setup(agent_id="my-crew-agent")

# 2. Create memory tools bound to your agent
tools = create_memanto_tools(client, agent_id="my-crew-agent")

# 3. Give agents Memanto tools
researcher = Agent(
    role="Research Analyst",
    goal="Gather and store key facts about the topic",
    backstory="You are thorough and always save important findings for future reference.",
    tools=[tools["remember"], tools["recall"]],  # Persistent memory!
    verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Write a report using previously stored research",
    backstory="You rely on stored research to write accurate, grounded content.",
    tools=[tools["recall"], tools["answer"]],  # Reads persistent memory!
    verbose=True
)

research_task = Task(
    description="Research the latest trends in AI agents and store 5 key findings.",
    expected_output="Confirmation that 5 findings have been stored in memory.",
    agent=researcher
)

write_task = Task(
    description="Recall the stored AI agent findings and write a concise summary report.",
    expected_output="A 3-paragraph summary report grounded in recalled memory.",
    agent=writer
)

# 4. Run the crew with memory=False to prevent dual memory systems
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    memory=False,  # Memanto handles memory via tools
    verbose=True
)

result = crew.kickoff()
print(result)

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Available Tools

The create_memanto_tools function returns a dictionary containing these pre-built CrewAI tools:

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remember (MemantoRememberTool)

Allows agents to store a piece of information in long-term memory.
  • Smart Categorization: The tool schema has definitions for all 13 Memanto memory types (e.g., fact, preference, observation) baked directly into the prompt. The LLM intrinsically understands how to categorize what it discovers without you needing to explicitly define the types in your CrewAI Task description.
  • Confidence Scoring: The agent is forced to actively evaluate its certainty on every memory it stores, assigning a mandatory confidence score between 0.0 (unverified) and 1.0 (objective fact).
  • Capacity: Agents can store up to 10,000 characters per memory block.

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recall (MemantoRecallTool)

Allows agents to search long-term memory for relevant information using semantic search. Best used when the agent needs raw memory items to reason over.

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answer (MemantoAnswerTool)

Uses Memanto’s built-in RAG to synthesize a response directly from stored memories — no extra LLM call needed from your agent.
Tip: Use answer when the agent needs a ready-to-use response, such as for a final task output or a direct reply to a user, and use recall when they just need the raw data.

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Persistent Memory Across Runs

Because memories live in Memanto (not in-process), they persist between separate crew runs. Additionally, the integration handles all backend infrastructure automatically — you never need to manually provision databases or namespaces; the integration auto-creates the required secure Moorcheh namespaces the moment you initialize MemantoSetup. 
# Run 1: researcher stores findings
crew.kickoff()

# Run 2 (next day): writer recalls those same findings
crew.kickoff()
No extra configuration needed — the agent_id ties memories together across runs.

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Next Steps