This content originally appeared on DEV Community and was authored by Nikhil Kumar

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 memoryReusable 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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AI #LLM #OpenSource #DeveloperTools #Python #MultiMindSDK #Chatbots #Agents #Memory #ToolAgent #GenerativeAI

This content originally appeared on DEV Community and was authored by Nikhil Kumar