Fine-tuning LLM Agents without Fine-tuning LLMs! Imagine improving...
Avi Chawla@_avichawla
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Oct 24, 2025
~1 min read
1
Fine-tuning LLM Agents without Fine-tuning LLMs!
Imagine improving your AI agent's performance from experience without ever touching the model weights.
It's just like how humans remember past episodes and learn from them.
That's precisely what Memento does.
The core concept:
Instead of updating LLM weights, Memento learns from experiences using memory.
It reframes continual learning as memory-based online reinforcement learning over a memory-augmented MDP.
Think of it as giving your agent a notebook to remember what worked and what didn't!
How does it work?
The system breaks down into two key components:
1️⃣ Case-Based Reasoning (CBR) at work:
Decomposes complex tasks into sub-tasks and retrieves relevant past experiences.
No gradients needed, just smart memory retrieval!
2️⃣ Executor
Executes each subtask using MCP tools and records outcomes in memory for future reference.
Through MCP, the executor can accomplish most real-world tasks & has access to the following tools:
🔍 Web research
📄 Document handling
🐍 Safe Python execution
📊 Data analysis
🎥 Media processing
I found this to be a really good path toward building human-like agents.
👉 Over to you, what are your thoughts?
Find the GitHub repo in the replies!
Imagine improving your AI agent's performance from experience without ever touching the model weights.
It's just like how humans remember past episodes and learn from them.
That's precisely what Memento does.
The core concept:
Instead of updating LLM weights, Memento learns from experiences using memory.
It reframes continual learning as memory-based online reinforcement learning over a memory-augmented MDP.
Think of it as giving your agent a notebook to remember what worked and what didn't!
How does it work?
The system breaks down into two key components:
1️⃣ Case-Based Reasoning (CBR) at work:
Decomposes complex tasks into sub-tasks and retrieves relevant past experiences.
No gradients needed, just smart memory retrieval!
2️⃣ Executor
Executes each subtask using MCP tools and records outcomes in memory for future reference.
Through MCP, the executor can accomplish most real-world tasks & has access to the following tools:
🔍 Web research
📄 Document handling
🐍 Safe Python execution
📊 Data analysis
🎥 Media processing
I found this to be a really good path toward building human-like agents.
👉 Over to you, what are your thoughts?
Find the GitHub repo in the replies!
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GitHub repo: github.com/Agent-on-the-F…