dr-agent 0.1.2

dr-agent-lib is an agent library for building deep research agents

dr-agent-lib

Overview

dr-agent-lib is an agent library for training and developing deep research agents. It supports:

• MCP-Based Tool Backend: Unified interface for web search and browsing tools
• High Concurrency: Global caching and async request management for RL training at scale
• Flexible Prompting Interface: Easy composition of search workflows with fine-grained control

Setup

Below we assume you are already in the agent directory.

conda create -n dr_agent python=3.10 -y && conda activate dr_agent

uv pip install -e .     # Install dev version
uv pip install dr_agent # Install from pypi 

If you run crawl4ai locally, you will need to install playwright and its dependencies.

Set up API keys via .env file:

S2_API_KEY=xxx
SERPER_API_KEY=xxx
JINA_API_KEY=xxx

Note you will need to get these API keys from the respective services.

• S2_API_KEY: https://api.semanticscholar.org/
• SERPER_API_KEY: https://serper.dev/
• JINA_API_KEY: https://jina.ai/reader/

Getting started

1. Launch MCP Server

   MCP_CACHE_DIR=".cache-$(hostname)" python -m dr_agent.mcp_backend.main --port 8000
   

2. Using DR-Tulu Models

   • Start the VLLM Server

     CUDA_VISIBLE_DEVICES=0 vllm serve rl-research/DR-Tulu-8B --dtype auto --port 30002 --max-model-len 40960
     
     CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-8B --dtype auto --port 30003 --max-model-len 40960
     

   • Run generation script

     bash scripts/auto_search.sh
     

3. Using OAI models

   export OPENAI_API_KEY="XXXX"
   bash scripts/auto_search-oai.sh
   

Interactive Chat

In Live Interface

# Install additional dependencies
uv pip install ".[ui]" 

# Launch the interactive interface with the workflow
python workflows/auto_search_sft.py serve --port 8080
# (this assumes you also launch other needed MCP and model server)

# In UI dev mode
python workflows/auto_search_sft.py serve --port 8080 --ui-mode proxy

In CLI

We provide an interactive cli demo for the auto_search workflow. Requires 1-2 GPUs. We recommend running with uv, which should install everything you need and then launch the tool, but set your API keys first:

export SERPER_API_KEY="XXXX"
export S2_API_KEY="XXXX"
export JINA_API_KEY="XXXX"

uv run --extra vllm  python scripts/launch_chat.py --model rl-research/DR-Tulu-8B

Note for this cli demo, we use a slightly different prompt than the one used for evaluation in our paper, for demo purposes. The prompt is in the file dr_agent/shared_prompts/unified_tool_calling_cli.yaml.

We provide additional flags for the chat script, for e.g. showing full tool output:

usage: launch_chat.py [-h] [--config CONFIG] [--dataset-name DATASET_NAME]
                      [--model MODEL] [--config-overrides CONFIG_OVERRIDES]
                      [--verbose] [--show-full-tool-output] [--skip-checks]
                      [--mcp-port MCP_PORT] [--gpu-id GPU_ID]
                      [--no-auto-launch]

Self-contained launcher for interactive chat

options:
  -h, --help            show this help message and exit
  --config CONFIG, -c CONFIG
                        Config file path (default:
                        workflows/auto_search_sft.yaml)
  --dataset-name DATASET_NAME, -d DATASET_NAME
                        Dataset name for dataset-specific instructions
  --model MODEL, -m MODEL
                        Main model name (for search agent). If not provided,
                        uses config defaults.
  --config-overrides CONFIG_OVERRIDES
                        Config overrides (e.g., 'param1=value1,param2=value2')
  --verbose, -v         Enable verbose output
  --show-full-tool-output
                        Show full tool output instead of truncating to 500
                        characters
  --skip-checks         Skip checking/launching services
  --mcp-port MCP_PORT   MCP server port (default: 8000)
  --gpu-id GPU_ID       GPU ID for search agent vLLM server (default: 0,
                        browse agent uses GPU 1)
  --no-auto-launch      Don't automatically launch vLLM servers (check only)

Examples:
  # Basic usage (auto-launches MCP server and vLLM servers if needed)
  python scripts/launch_chat.py

  # With specific model (auto-launches both vLLM servers on GPUs 0 and 1)
  python scripts/launch_chat.py --model rl-research/DR-Tulu-8B

  # Skip service checks (if services are already running)
  python scripts/launch_chat.py --skip-checks

  # Don't auto-launch vLLM servers (just check)
  python scripts/launch_chat.py --no-auto-launch

  # Custom config file
  python scripts/launch_chat.py --config workflows/auto_search_sft.yaml

Evaluation

This repository includes evaluation scripts for multiple benchmarks, including:

• Long-form: SQA-CS-V2, Deep Research Bench, ResearchQA, HealthBench
• Domain-specific: Genetic Diseases
• Short-form: BrowseComp, SimpleQA, Short Form QA

For detailed evaluation instructions, benchmark descriptions, and usage examples, see evaluation/README.md.