Python: Build(deps): Bump agentlightning from 0.2.2 to 0.3.0 in /python

[dependabot]

Bumps agentlightning from 0.2.2 to 0.3.0.

Release notes

Sourced from agentlightning's releases.

Agent Lightning v0.3.0

Agent-lightning v0.3.0 is a major release that introduces several new features and bug fixes. The release is a collaborative effort between Agent-lightning core teams and the community. Thanks to all the contributors who made this release possible.

Highlights

• Tinker integration: Support Tinker as an alternative backend for Reinforcement Learning (#226 #245 #264 #269 #327). See example code, blog 1 and blog 2.
• Azure OpenAI integration: Support Azure OpenAI as a backend for LLM inference and supervised fine-tuning (#256 #327). Example code.
• MongoDB-based Lightning Store is added as an alternative backend for Lightning Store (#323). Documentation.
• Contrib package: Add contrib package for community projects. Search-R1 is integrated as a contrib recipe. More coming. (#239 #396 #410 #412 #417).
• RESTful API: Stabilize and document RESTful API for Lightning Store (#241 #275). Documentation.
• OTel Semantic Conventions that are specifically designed for Agent-optimization areas (#340). Documentation.
• [Preview] Agent-lightning Dashboard is now available (#288 #289 #291 #296 #371 #375). It's the official web application for inspecting and debugging Agent-lightning experiments. See details here.
• [Preview] Multi-modality example featuring VERL and a LangGraph agent on ChartQA dataset (#379). Example code.
• [Preview] Integrate Claude Code as a LitAgent and support training on SWE-Bench (#332 #346 #348). Example code.
• [Preview] Weave tracer as a substitute for AgentOps tracer (#277 #411 #420 #423). Documentation.
• [Preview] Trajectory Level Aggregation for more efficient training with VERL. See blog and documentation.

Store Benchmark

In this release, the Lightning Store core was redesigned for significantly greater efficiency and scalability (#315 #318 #328 #342 #344 #356 #380 #388 #418 #421). The benchmark results below demonstrate the impact: with large numbers of concurrent runners, v0.3.0 delivers up to a 15x increase in throughput compared to v0.2.2.

Throughput (#rollout/sec)	v0.2.2	v0.3.0 (in-memory)	v0.3.0 (Mongo)
Minimal (batch, #runner=32, #turns=6)	8.73	9.06	8.71
Medium (batch, #runners=100, #turns=10)	12.03	23.26	32.79
Mid-high (batch, #runners=300, #turns=6)	10.61	24.42	40.24
Large (batch, #runners=1000, #turns=3)	3.36	14.60	50.05
Long queue (queue, #runners=256, #turns=4)	7.42	30.86	57.01
Heavy trace (queue, #runners=512, #turns=20)	5.93	13.28	29.41

Notes:

1. Benchmarks were run on a single Standard_D32as_v4 Azure VM (Large and heavy trace tests used Standard_D64ads_v5), executed via GitHub Actions.
2. Two algorithm patterns are evaluated: the batch pattern submits a group of rollouts and waits for all to finish before starting the next group, while the queue pattern maintains a set number of in-flight rollouts, submitting new ones as soon as capacity frees up. Configuration details are available here.
3. The number of turns is directly proportional to the number of spans each rollout generates.

Maintenance and Bug fixes

Core (Store, Interfaces, etc.)

• Add Trainer port option for client-server strategies (#198)
• Fix store port conflict handling (#227)
• Unified PythonServerLauncher (#286 #292 #303)
• Make health timeout configurable (#305)
• Refactor logging (#306)
• Support OTLP in LightningStore (#313)
• Centralized metrics helper (#368)
• Fix redundant cancel tracebacks on Ctrl+C (#370)

... (truncated)

Changelog

Sourced from agentlightning's changelog.

Agent-lightning v0.3.0 (12/24/2025)

Agent-lightning v0.3.0 is a major release that introduces several new features and bug fixes. The release is a collaborative effort between Agent-lightning core teams and the community. Thanks to all the contributors who made this release possible.

Highlights

• Tinker integration: Support Tinker as an alternative backend for Reinforcement Learning (#226 #245 #264 #269 #327). See example code, blog 1 and blog 2.
• Azure OpenAI integration: Support Azure OpenAI as a backend for LLM inference and supervised fine-tuning (#256 #327). Example code.
• MongoDB-based Lightning Store is added as an alternative backend for Lightning Store (#323). Documentation.
• Contrib package: Add contrib package for community projects. Search-R1 is integrated as a contrib recipe. More coming. (#239 #396 #410 #412 #417).
• RESTful API: Stabilize and document RESTful API for Lightning Store (#241 #275). Documentation.
• OTel Semantic Conventions that are specifically designed for Agent-optimization areas (#340). Documentation.
• [Preview] Agent-lightning Dashboard is now available (#288 #289 #291 #296 #371 #375). It's the official web application for inspecting and debugging Agent-lightning experiments. See details here.
• [Preview] Multi-modality example featuring VERL and a LangGraph agent on ChartQA dataset (#379). Example code.
• [Preview] Integrate Claude Code as a LitAgent and support training on SWE-Bench (#332 #346 #348). Example code.
• [Preview] Weave tracer as a substitute for AgentOps tracer (#277 #411 #420 #423). Documentation.
• [Preview] Trajectory Level Aggregation for more efficient training with VERL. See blog and documentation.

Store Benchmark

In this release, the Lightning Store core was redesigned for significantly greater efficiency and scalability (#315 #318 #328 #342 #344 #356 #380 #388 #418 #421). The benchmark results below demonstrate the impact: with large numbers of concurrent runners, v0.3.0 delivers up to a 15x increase in throughput compared to v0.2.2.

Throughput (#rollout/sec)	v0.2.2	v0.3.0 (in-memory)	v0.3.0 (Mongo)
Minimal (batch, #runner=32, #turns=6)	8.73	9.06	8.71
Medium (batch, #runners=100, #turns=10)	12.03	23.26	32.79
Mid-high (batch, #runners=300, #turns=6)	10.61	24.42	40.24
Large (batch, #runners=1000, #turns=3)	3.36	14.60	50.05
Long queue (queue, #runners=256, #turns=4)	7.42	30.86	57.01
Heavy trace (queue, #runners=512, #turns=20)	5.93	13.28	29.41

Notes:

1. Benchmarks were run on a single Standard_D32as_v4 Azure VM (Large and heavy trace tests used Standard_D64ads_v5), executed via GitHub Actions.
2. Two algorithm patterns are evaluated: the batch pattern submits a group of rollouts and waits for all to finish before starting the next group, while the queue pattern maintains a set number of in-flight rollouts, submitting new ones as soon as capacity frees up. Configuration details are available here.
3. The number of turns is directly proportional to the number of spans each rollout generates.

Maintenance and Bug fixes

Core (Store, Interfaces, etc.)

• Add Trainer port option for client-server strategies (#198)
• Fix store port conflict handling (#227)
• Unified PythonServerLauncher (#286 #292 #303)
• Make health timeout configurable (#305)
• Refactor logging (#306)
• Support OTLP in LightningStore (#313)
• Centralized metrics helper (#368)
• Fix redundant cancel tracebacks on Ctrl+C (#370)

... (truncated)

Commits

• 3b5d733 [Release] v0.3.0 (#427)
• 158f5df Fix documentation and dashboard building issues (#429)
• 40dc592 Scale out benchmark parameters (#428)
• c1a43b6 Update parallelization guides (#426)
• 4235731 Feat: Add trace_aggregator to support both transition and trajectory aggreg...
• 22b80b3 v0.3 Documentation Update (#422)
• 9f178ac Minor optimizations to store benchmark (#421)
• 68a47d5 Make weave import optional (#423)
• 4b36b25 Fix Weave get username (#420)
• a13e09f add youtu agent blog link in community projects (#416)
• Additional commits viewable in compare view

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Python Test Coverage Report •

File	Stmts	Miss	Cover	Missing
TOTAL	43298	5178	88%

report-only-changed-files is enabled. No files were changed during this commit :)

Python Unit Test Overview

Tests	Skipped	Failures	Errors	Time
8534	33 💤	0 ❌	0 🔥	1m 34s ⏱️