Memory in an autonomous agent

[0xhijo]

Hello everyone,
I’m currently developing an autonomous agent using LangChain/LangGraph and have several questions about memory management.

1. Short-term memory: Is it really necessary to save all the AiChatMessage data? It appears to be sent in raw format, which consumes more tokens and adds complexity to the input prompt sent to the LLM. Would it be more efficient to extract only the important content (results, tool calls), format them as Q&A pairs, and then save them to short-term memory?
2. Long-term memory: I’ve seen different implementation approaches. Some developers use a tool that allows the AI to add memories to the vector database itself. Is it actually beneficial to give the AI this choice, or would it be simpler to automatically save all content results from each iteration and retrieve them through vector queries as needed?

[riakashyap]

For short-term memory, you don’t have to save the full AIChatMessage payload unless you really need to replay every message exactly (e.g. for debugging or audits).
In most real setups, it’s cleaner and cheaper to just keep the distilled bits like tool outputs and final answers as Q&A pairs. That keeps token counts down and avoids bloating the prompt.

For long-term memory, letting the agent decide when to “save” can be fun in experiments, but in production it’s usually safer to log everything relevant automatically and query it later from your vector store. That way you’re not depending on the model to remember to remember.

If it’s helpful, I can add a small LangChain code example showing both approaches. Might save you some trial-and-error.

[Ziechoes]

I ran into the same issue — all memory dumps slow things down and obscure insights.

I’m building a small module called Codex that tackles this head-on:

• Logs only failure points (contradictions, reasoning gaps, unknowns) instead of every token.
• Outputs a compact, auditable Reasoning Report (JSON + rendered HTML).
• Keeps memory lean, makes failures traceable, and stops agents from repeating mistakes.

Here’s what a collapse report looks like for a trivial failure (2+2=5):
{
"collapse_point": "Arithmetic Error",
"claim": "2+2=5",
"evidence": "Contradicts elementary arithmetic",
"status": "collapsed",
"timestamp": "2025-08-29T11:24:00Z"
}

Reasoning Report

Collapse Point: Arithmetic Error
Claim: 2+2=5
Status: ❌ Collapsed
Notes: Contradiction detected against axiomatic arithmetic rules.

Would something like this help make your memory layer both efficient and insightful?