Agentic Shared Memory

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Agentic Shared Memory is an open-source Python library designed to manage and optimize memory in multi-agent systems. It offers a vector embedding-based storage engine with efficient indexing and retrieval to maintain agent context. Developers can easily integrate it into existing AI frameworks, enabling agents to share, update, and query shared knowledge dynamically, improving collaboration, coherence, and decision-making across complex workflows.
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May 18 2025
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Agentic Shared Memory

Agentic Shared Memory

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Agentic Shared Memory
Agentic Shared Memory is an open-source Python library designed to manage and optimize memory in multi-agent systems. It offers a vector embedding-based storage engine with efficient indexing and retrieval to maintain agent context. Developers can easily integrate it into existing AI frameworks, enabling agents to share, update, and query shared knowledge dynamically, improving collaboration, coherence, and decision-making across complex workflows.
Added on:
Social & Email:
Platform:
May 18 2025
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What is Agentic Shared Memory?

Agentic Shared Memory provides a robust solution for managing contextual data in AI-driven multi-agent environments. Leveraging vector embeddings and efficient data structures, it stores agent observations, decisions, and state transitions, enabling seamless context retrieval and update. Agents can query the shared memory to access past interactions or global knowledge, fostering coherent behavior and collaborative problem-solving. The library supports plug-and-play integration with popular AI frameworks like LangChain or custom agent orchestrators, offering customizable retention strategies, context windowing, and search functions. By abstracting memory management, developers can focus on agent logic while ensuring scalable, consistent memory handling across distributed or centralized deployments. This improves overall system performance, reduces redundant computations, and enhances agent intelligence over time.

Who will use Agentic Shared Memory?

  • AI developers
  • Research scientists
  • Multi-agent system engineers
  • Machine learning engineers

How to use the Agentic Shared Memory?

  • Step1: Install via pip with `pip install agentic-shared-memory`.
  • Step2: Import and configure the memory store in your Python code.
  • Step3: Initialize the shared memory instance with embedding and backend settings.
  • Step4: Integrate the memory instance into your agent pipeline or framework.
  • Step5: Use memory.get() to retrieve context and memory.update() to store new data.
  • Step6: Adjust retention policies and context window sizes as needed.

Platform

  • mac
  • windows
  • linux

Agentic Shared Memory's Core Features & Benefits

The Core Features

  • Vector embedding-based memory storage
  • Efficient indexing and retrieval
  • Context windowing and retention policies
  • Integration with LangChain and other frameworks
  • Concurrent access for multiple agents

The Benefits

  • Improved agent collaboration
  • Consistent context management
  • Reduced redundant computations
  • Scalable and performant
  • Easy integration and customization

Agentic Shared Memory's Main Use Cases & Applications

  • Multi-agent conversational bots
  • Distributed decision-making systems
  • AI-driven workflow automation
  • Game AI state management
  • Research on agentic systems

FAQs of Agentic Shared Memory

Agentic Shared Memory Company Information

Agentic Shared Memory Reviews

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Agentic Shared Memory's Main Competitors and alternatives?

  • LangChain Memory module
  • MemGPT
  • Haystack Document Store
  • Custom Redis-based memory solutions

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