Introduction

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AgentJet (AJet) is a cutting-edge, user-friendly agent tunning framework designed to optimize LLM models and agent workflows.

Simply provide your workflow (built from AgentScope, OpenAI SDK, Langchain, raw HTTP requests, or hybrid of all of them), training data, and reward function, and we will be ready to enhance your agents to their optimal performance!

Features

AgentJet aims to build a state-of-the-art agent tuning platform for both developers and researchers

  • Easy and Friendly. AgentJet helps you tune models behind your agent workflows easily, optimizing your agents for top performance with minimal effort.
  • Rich Tutorial Library. AgentJet provides a rich library of examples as tutorials.
  • Efficient and Scalable. AgentJet uses [verl] as the default backbone (--backbone=verl). However, we also support trinity as alternative backbone, accelerating your tuning process via fully asynchronous RFT.
  • Flexible and Fast. AgentJet supports multi-agent workflows and adopts a context merging technique, accelerating training by 1.5x to 10x when the workflow involves multi-turn (or multi-agent) conversations.
  • Reliability and Reproducibility. Our team keeps track of framework performance across multiple tasks + major-git-version + training-backbones (under construction, still gathering data, comming soon).

For advanced researchers, AgentJet also provides high-resolution logging and debugging solutions:

  • High-Resolution Logging: AgentJet allows users to save and inspect token-level rollout details, recording token IDs, token loss masks, and even token logprobs to facilitate workflow development and agent diagnostics.
  • Fast Debugging: AgentJet also provides the --backbone=debug option for the best debugging experience, shortening your wait period from minutes to seconds after code changes and enabling breakpoint debugging in IDEs.

Quick Start

Installation

We recommend using uv for dependency management.

git clone https://github.com/modelscope/AgentJet.git
cd AgentJet

uv venv --python=3.10.16 && source .venv/bin/activate
uv pip install -e .[trinity]
# Note: flash-attn must be installed after other dependencies
uv pip install flash_attn==2.8.3 --no-build-isolation --no-cache-dir
  • Train the first agent

You can start training your first agent with a single command using a pre-configured YAML file:

ajet --conf tutorial/example_math_agent/math_agent.yaml

Learn More

See the Math Agent example for detailed explanation.

Example Library

Explore our rich library of examples to kickstart your journey:

Math Agent

Training a math agent that can write Python code to solve mathematical problems.

AppWorld Agent

Creating an AppWorld agent using AgentScope and training it for real-world tasks.

Werewolves Game

Developing Werewolves RPG agents and training them for strategic gameplay.

Learning to Ask

Learning to ask questions like a doctor for medical consultation scenarios.

Countdown Game

Writing a countdown game using AgentScope and solving it with RL.

Frozen Lake

Solving a frozen lake walking puzzle using AgentJet's reinforcement learning.

Core Concepts

AgentJet makes agent fine-tuning straightforward by separating the developer interface from the internal execution logic.

AgentJet Architecture

1. The User-Centric Interface

To optimize an agent, you provide three core inputs:

Trainable Workflow

Define your agent logic by inheriting the Workflow class, supporting both simple and multi-agent setups.

Task Reader

Load training tasks from JSONL files, HuggingFace datasets, or auto-generate from documents.

Task Judger

Evaluates agent outputs and assigns rewards to guide the training process.

2. Internal System Architecture

The internal system orchestrates several specialized modules to handle the complexities of RL training and agent interactions.

Module Description
Launcher Manages background service processes (Ray, vLLM) and routes the backbone
Task Reader Handles data ingestion, augmentation, and filtering
Task Rollout Bridges LLM engines and manages the Gym environment lifecycle
Task Runner Executes the AgentScope workflow and calculates rewards
Model Tuner Forwards inference requests from the workflow to the LLM engine
Context Tracker Monitors LLM calls and automatically merges shared-history timelines (1.5x-10x efficiency boost)

Next Steps

Installation

Set up AgentJet environment and dependencies.

Quick Start

Run your first training in minutes.
Overview Installation