Comprehensive gestão de memória AI Tools for Every Need

Get access to gestão de memória AI solutions that address multiple requirements. One-stop resources for streamlined workflows.

gestão de memória AI

  • memU

    MemU is an intelligent agentic memory layer designed specifically for AI companions.
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    What is memU?
    MemU is an agentic memory layer built to function as an intelligent and autonomous file system for AI companions, transforming memory management by organizing, linking, and continuously improving stored data. It integrates with major LLMs like OpenAI and Anthropic, enhancing the AI's ability to memorize and recall conversations and knowledge efficiently, thus optimizing AI agent performance and user experience.
    memU Core Features
    • Autonomous memory management
    • Interconnected knowledge graph
    • Continuous self-improvement
    • SDK support for Python, JavaScript, REST API
    • Integration with major LLM platforms
    memU Pro & Cons

    The Cons

    Currently web platform only
    Some advanced features require commercial license
    Limited mobile app availability

    The Pros

    High accuracy and speed in memory retrieval
    Supports major LLM integrations
    Customizable enterprise solutions
    Open-source SDK availability
    Active community and Discord support
    memU Pricing
    Has free planYES
    Free trial details14-day free trial with no credit card required
    Pricing modelFreemium
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequencyMonthly

    Details of Pricing Plan

    Starter

    0 USD
    • Up to 30 memory calls
    • Basic retrieval
    • Community support
    • API access

    Professional

    29 USD
    • Up to 600 memory calls
    • Advanced retrieval algorithms
    • Priority support
    • Advanced analytics
    • Custom integrations

    Enterprise

    Custom USD
    • Unlimited memory calls
    • Dedicated infrastructure
    • 24/7 dedicated support
    • Custom SLA
    • On-premise deployment
    • Custom training
    Discount:No explicit discount information found
    For the latest prices, please visit: https://memu.pro/pricing
  • A web-based platform to design, orchestrate, and manage custom AI agent workflows with multi-step reasoning and integrated data sources.
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    What is SquadflowAI Studio?
    SquadflowAI Studio allows users to visually compose AI agents by defining roles, tasks, and inter-agent communications. Agents can be chained to handle complex multi-step processes—querying databases or APIs, performing actions, and passing context among one another. The platform supports plugin extensions, real-time debugging, and step-by-step logs. Developers configure prompts, manage memory states, and set conditional logic without boilerplate code. Models from OpenAI, Anthropic, and local LLMs are supported. Teams can deploy workflows via REST or WebSocket endpoints, monitor performance metrics, and adjust agent behaviors through a centralized dashboard.
  • Agent Workflow Memory provides AI agents with persistent workflow memory using vector stores for context recall.
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    What is Agent Workflow Memory?
    Agent Workflow Memory is a Python library designed to augment AI agents with persistent memory across complex workflows. It leverages vector stores to encode and retrieve relevant context, enabling agents to recall past interactions, maintain state, and make informed decisions. The library integrates seamlessly with frameworks like LangChain’s WorkflowAgent, providing customizable memory callbacks, data eviction policies, and support for various storage backends. By housing conversation histories and task metadata in vector databases, it allows semantic similarity searches to surface the most relevant memories. Developers can fine-tune retrieval scopes, compress historical data, and implement custom persistence strategies. Ideal for long-running sessions, multi-agent coordination, and context-rich dialogues, Agent Workflow Memory ensures AI agents operate with continuity, enabling more natural, context-aware interactions while reducing redundancy and improving efficiency.
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