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Posted on • Originally published at mrmemory.dev

Store user preference

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The Memory Problem in 2026 Applications

You've got a conversational AI that's lost its train of thought. Again. This time it's not just a minor glitch – it's a major problem. Without effective memory, your agents can't learn from past interactions, maintain context across sessions, or build knowledge over time.

Let's dive into the top-ranked AI agent memory frameworks and explore how they tackle this issue.

8 Top-Ranked AI Agent Memory Frameworks

We'll break down each framework's strengths and weaknesses. Keep in mind that no one-size-fits-all solution exists; you'll need to choose the best fit for your application.

Mem0

Mem0 excels at personalization and temporal reasoning, but its architecture is complex and difficult to manage.

Zep

Zep's temporal knowledge graph architecture shines, but it falls short on self-edit tools and governance.

LangChain Memory (LangMem)

LangMem is designed for long-term factual knowledge management. It's a good choice if you need to persist context across sessions.

Letta

Letta is suitable for long-running workflows and context-aware applications. Its architecture is scalable, but its feature set is limited compared to other frameworks.

MrMemory

MrMemory is a managed memory API that persists context across sessions using a combination of PostgreSQL and Qdrant vector DB. It offers memory compression (40-60% token savings), LangChain integration, and self-edit tools.

Hands-On Example with MrMemory

Here's how you can use MrMemory to store and retrieve conversation history:

from mrmemory import MrMemory
client = MrMemory(api_key="your-key")
# Store user preference
client.remember("user prefers dark mode", tags=["preferences"])
# Retrieve user preference
results = client.recall("what theme does the user like?")
print(results) # Output: "dark mode"
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Comparison and Alternatives

While Mem0, Zep, and LangMem are popular choices, they lack some of the features offered by MrMemory. For example:

  • Temporal Knowledge Graph Architecture: Zep excels in this area but lacks self-edit tools.
  • Personalization: Mem0 is a good choice for personalization but doesn't offer memory compression.

Conclusion

Choosing the right AI agent memory framework depends on your specific use case. If you need a scalable, feature-rich solution with LangChain integration and self-edit tools, MrMemory might be the way to go.

Try MrMemory today to persist context across sessions and improve agent performance!

Try MrMemory now: https://github.com/masterdarren23/mrmemory

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