What if your AI agent could remember facts, track your preferences, and even retain past decisions across tools and sessions?

That’s what we built with MultiMemory in MultiMindSDK. Instead of a single memory blob like most agents, our agents think in layers — just like you do.

🧩 What is MultiMemory?

Traditional LLM agents rely on one memory source — usually a chat history or context window.

But real-world tasks need more than that:
Task-specific short-term memory🧠Reusable global knowledge 🌐
Dynamic memory updates during runtime 🔄

That’s where MultiMemory comes in:
A multi-layered memory stack that lets your agent juggle multiple types of memory — per tool, per session, or even per user.

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🔧 How It Works

In MultiMindSDK, each agent can be assigned a MultiMemory object, which handles:
💬 Chat history memory (like typical LLMs)
📁 Tool-specific memory (e.g., weather API knows past queries)
🧠 Global memory (knowledge shared across all tools/agents)
⏱️ Ephemeral memory (short-term scratchpad during execution)

You configure it with simple Python:

from multimind.client.memory import MultiMemory

memory = MultiMemory(
    chat=True,
    global_kb=True,
    tool_contexts={
        "weather": True,
        "finance": False
    }
)

#Then attach it to your agent:

agent = ToolAgent(model="gpt-4", memory=memory, tools=tools)
response = agent.run("What's the weather today in Berlin?")

The agent remembers what you’ve asked, stores tool-specific info, and uses it dynamically in future replies.

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🤯 Why This Matters

Most frameworks (LangChain, AutoGPT, etc.) have memory — but it’s linear and fragile.
Ours is modular, layered, and tool-aware.

This means you can:

✅ Persist long-term knowledge across sessions
✅ Let tools build their own memory footprints
✅ Create agents that behave differently per user/context

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🚀 Real-World Use Cases

✅ AI assistants that remember each user’s preferences
✅ Agents that analyze stock trends over multiple runs
✅ Agents that coordinate across tools using shared state
✅ Multi-step workflows where decisions persist

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🛠️ Developer Benefits
• 🔄 No more custom memory hacks
• 🧱 Plug-and-play into any agent in MultiMindSDK
• ⚡ Works out of the box with Python + soon in Node

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🧪 Try it Yourself

pip install multimind-sdk

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🧠 TL;DR

One memory isn’t enough.
Use MultiMemory to give your AI agents real cognition power — context-aware, persistent, and layered.

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🔗 Follow Our Journey

We’re building MultiMindSDK to be the most flexible, open, and dev-friendly agent framework on the planet.

Upcoming features include:
• 🧠 Graph memory
• 🎯 Agent orchestration
• 🌍 Local + cloud LLM routing
• 🎛️ Plug-and-play custom tools
• 🧩 Node + PyScript support

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📦 Install MultiMindSDK:
pip install multimind-sdk
🌐 Website: https://multimind.dev
GitHub:https://github.com/multimindlab/multimind-sdk
💬 Join us on Discord: https://discord.gg/K64U65je7h
📩 Email: contact@multimind.dev

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