This guide provides two distinct paths for training an agent:

1. Basic GRPO Training: A standard method to get started quickly.
2. AgentEvolver Training: An advanced method that supports a self-evolving agent training.

Prerequisites: One-Time Global Setup

Before you begin, run these commands in your terminal to configure your environment. You only need to do this once.

1. Initialize Conda

source <YOUR_CONDA_PATH>/etc/profile.d/conda.sh

1. Configure API Endpoints

export DASHSCOPE_API_KEY="<YOUR_API_KEY>"
export HF_ENDPOINT=https://hf-mirror.com

💡 Tip: Add the export commands to your ~/.bashrc or ~/.zshrc file to set them automatically in new terminal sessions.

Part A: Basic GRPO Training

Step 1: Setup Env-Service (AppWorld for example)

This launches the simulation environment (e.g., AppWorld) where the agent will operate. This service will run in the background. You'll need a new terminal for the next step.

conda activate appworld
bash env_service/launch_script/appworld.sh

Step 2: Start Basic GRPO Training

This command starts the training process using the GRPO method.

conda activate agentevolver
bash examples/run_basic.sh

Part B: AgentEvolver Training

Step 1: Setup Env-Service (AppWorld for example)

Just like in basic training, this launches the agent's simulation environment. This service will run in the background. You'll need a new terminal for the next step.

conda activate appworld
bash env_service/launch_script/appworld.sh

Step 2: Setup ReMe-Service

This service gives the agent long-term memory and the ability to reflect on past actions. This service will listen for requests on http://127.0.0.1:8001. Keep this terminal open.

Configure API Endpoints:

export FLOW_EMBEDDING_API_KEY="<YOUR_API_KEY>"
export FLOW_EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
export FLOW_LLM_API_KEY="<YOUR_API_KEY>"
export FLOW_LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1

conda activate reme
cd external/reme
reme \
  config=default \
  backend=http \
  thread_pool_max_workers=256 \
  http.host="127.0.0.1" \
  http.port=8001 \
  http.limit_concurrency=256 \
  llm.default.model_name=qwen-max-2025-01-25 \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local \
  op.rerank_memory_op.params.enable_llm_rerank=false

Step 3: Start AgentEvolver Training

With the environment and ReMe services running, start the AgentEvolver training.

conda activate agentevolver
bash examples/run_overall.sh