AgentFly Enables Smarter AI Agents Without Fine-Tuning LLMs Using Memory-Based Learning for 87% GAIA Accuracy

Every attempt to make AI agents smarter runs into the same fundamental obstacle: the enormous computational cost and risk of catastrophic forgetting associated with traditional fine-tuning. Updating billions of parameters in large language models (LLMs) requires massive compute resources, often costing millions of dollars per iteration, and frequently results in the agent unlearning previously acquired knowledge. Traditional methods rely on gradient-based fine-tuning, where the entire model is retrained on new data. While effective, this process is inefficient and unsustainable for continuous learning. Each update cycle demands extensive infrastructure, limits scalability, and makes iterative improvements prohibitively expensive—especially for real-world applications where agents must adapt quickly to new environments and tasks. AgentFly offers a radical departure from this paradigm. Instead of modifying the LLM’s parameters, AgentFly enables agents to become smarter through memory-based learning. It decouples the agent’s behavior from the underlying model, allowing the agent to learn from experience without altering the LLM itself. At its core, AgentFly uses a dynamic memory system that stores past interactions, decisions, and outcomes. When faced with a new task, the agent retrieves relevant past experiences from memory and uses them to guide its current behavior. This approach mimics human learning—where we improve through reflection and experience, not by rewriting our entire brain every time we learn something new. The architecture consists of three key components: a memory store for long-term experience retention, a retrieval mechanism that identifies relevant past episodes, and a reasoning layer that synthesizes memory content with current input to produce intelligent actions. This setup enables the agent to adapt rapidly to new scenarios without retraining. Mathematically, AgentFly leverages a weighted retrieval function that scores past experiences based on relevance to the current context. The system uses similarity metrics and attention mechanisms to select the most pertinent memories, ensuring efficient and accurate decision-making. By updating only the memory and retrieval logic—rather than the LLM’s parameters—AgentFly achieves significant performance gains at a fraction of the cost. In benchmark tests on the GAIA (General AI Benchmark for Agents) suite, AgentFly achieved 87% accuracy, matching or exceeding many fine-tuned models while avoiding parameter updates entirely. This performance demonstrates that intelligent behavior can be learned not by changing the model, but by teaching the agent how to learn from its own history. AgentFly’s innovation lies in its ability to scale learning without scaling costs. It enables continuous, incremental improvement in AI agents while preserving prior knowledge, eliminating catastrophic forgetting, and drastically reducing compute demands. This makes it ideal for real-world deployment in dynamic environments where adaptability and efficiency are critical. By shifting the focus from model modification to memory-enhanced reasoning, AgentFly redefines what’s possible in agent development—proving that smarter agents don’t require bigger or more expensive models, just better ways to learn.