AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents

Key points

• Tool-using agents increasingly require a common substrate for building, training, evaluating, distilling, and maintaining agentic systems, yet existing tooling typically addresses these stages in isolation.1
• At its core, AgentTune introduces a two-tier EventLog representation that preserves agent trajectories while supporting consistent projections for training, evaluation, and auditing.1
• Building on this substrate, the library provides a shared agent runtime, tool-calling reinforcement learning, agentic distillation, retrieval-augmented generation, decision workflows, evaluation utilities, and a designed path toward self-healing agents.1
• We demonstrate the framework across agentic tasks including legal contract search and domain-specific applications, illustrating its use for trajectory-based optimization, tool-use evaluation, and cross-task agent development.1

Sources (1)

• [1]AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:09 AM

  We present AgentTune, a source-available Python library that provides a unified framework for developing and optimizing tool-using agents.

  Tool-using agents increasingly require a common substrate for building, training, evaluating, distilling, and maintaining agentic systems, yet existing tooling typically addresses these stages in isolation.

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