I kept running into the same issue with LangChain-style agents:

• they lose context after a few steps
• or worse, they retrieve the wrong past information
• multi-step tasks start drifting

Most fixes I tried didn’t really solve it:

• bigger context windows
• more embeddings
• dumping everything into a vector DB

It still breaks.

So I started experimenting with a different approach:

Instead of treating memory as “everything that happened”,
I treat it as structured state the agent carries forward.

What this looks like:

• Separate short-term conversation vs long-term state
• Store decisions, not just messages
• Control what gets persisted vs ignored
• Retrieval is based on relevance to the current step, not similarity alone

Result:

Agents stay consistent across:

• multi-step workflows
• tool usage
• delayed execution

I wrapped this into a small system called BaseGrid.

It’s still early, but it’s been working much better than typical memory setups.

👉 https://basegrid.io

Would love feedback from others building agents—especially if you’ve hit similar issues.