Microsoft Agent Lightning

Microsoft Agent Lightning

by Microsoft Research

Continuous training and optimization framework for LLM-driven agents — decouples agent runtime from RL/training infrastructure.

See: https://github.com/microsoft/agent-lightning

Features

• Decoupled client-server architecture: lightweight client runs with the agent to collect traces; server manages training, GPUs, and model endpoints.
• Unified trace format: captures prompts, tool calls, model outputs, token metadata and reward signals for easy transition construction.
• Framework-agnostic integration: designed to work with LangChain, AutoGen, OpenAI Agents SDK, LangGraph and custom agents with minimal code changes.
• Multiple optimization modes: reinforcement learning (policy optimization), supervised fine-tuning, automatic prompt optimization (APO), and hybrid strategies.
• Hierarchical credit assignment: LightningRL-style algorithms and tooling to assign credit across multi-step workflows.
• Production controls: reward validation, anomaly detection, versioning, rollback and monitoring for safe continuous learning.

Superpowers

Agent Lightning’s core strength is enabling continuous learning for production agents with very low integration friction. Instead of forcing teams to refactor agents into simulator/trainer runtimes, Lightning attaches as a tracer and converts real agent interactions into training data. This enables:

• Incremental, safe improvements to agents in production via distillation and controlled rollouts.
• Selective optimization of only the sub-steps that need improvement (e.g., final answer rewrite), reducing training complexity and cost.
• Multi-agent and hierarchical optimization so teams of agents can learn coordinated behavior.

Who it’s for

• Engineering teams running LLM-driven production agents who want to add continuous learning without large refactors.
• ML/Research teams experimenting with RL for agents but needing to operate on real interaction traces rather than simulators.
• Organizations that need controlled, auditable, and incremental model updates in production.

What you gain by using it

• Lower friction to bring RL and other optimization techniques to real agents.
• Ability to test and iterate on reward functions and policies using live or replayed traces.
• Production-grade tooling around safety, monitoring, and rollout for continuous learning.

Pricing

• Open-source (MIT) — see LICENSE in the repo.
• Infrastructure costs: training server, GPU usage, trace storage and monitoring (organization-specific).

Quickstart checklist

• Identify a single, high-impact agent step to optimize first (e.g., answer rewriting or SQL generation).
• Define automated reward signals and optionally a human-feedback loop for better supervision.
• Start with offline experimentation on collected traces before enabling live training.
• Put monitoring, anomaly detection and rollback in place before automatic promotion.

Limitations & risks

• Requires careful reward design — poor rewards create degenerate behaviors.
• Continuous training in production requires governance, privacy controls, and cost management for GPUs and storage.
• Not a silver bullet: success depends on the quality of traces, reward signals, and evaluation.

References & next actions

• GitHub: https://github.com/microsoft/agent-lightning
• Microsoft Research project page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Research paper: https://arxiv.org/abs/2508.03680 (Luo et al., 2025)