Automate Life Sciences Research
Inspired by Anthropic's Claude Life Sciences initiative, this challenge involves building a sophisticated multi-agent system to automate aspects of scientific research. You will design and orchestrate a team of AI agents using LangGraph and the A2A protocol, powered by Claude Opus 4.1, to simulate tasks like hypothesis generation, experimental design, and data analysis. The system will integrate with simulated lab tools (like Benchling) via MCP for tool use. This project emphasizes advanced agentic AI concepts, including robust agent-to-agent communication, structured graph-based workflows, and sophisticated tool integration to tackle real-world scientific challenges. You'll demonstrate how cutting-edge LLMs can transform complex, multi-step scientific processes into automated, intelligent workflows.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a graph-based multi-agent system using LangGraph and A2A protocol with Claude Opus 4.1 for automating life sciences research workflows and lab tool integration via MCP.
How work is evaluated
Evaluation will focus on the correctness of the multi-agent system's workflow, the accuracy of its scientific outputs, proper tool utilization, and the robustness of agent communication.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design and implement a multi-agent system for scientific research automation.
Apply advanced RAG techniques for scientific literature comprehension and synthesis.
Utilize LangGraph for stateful, graph-based agent workflow orchestration.
Develop MCP-enabled tool integrations for simulated lab environments (e.g., Benchling).
Integrate A2A Protocol for secure and efficient agent-to-agent communication.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluation will focus on the correctness of the multi-agent system's workflow, the accuracy of its scientific outputs, proper tool utilization, and the robustness of agent communication.
Challenge input
{ 'research_topic': 'Immunotherapy for Glioblastoma', 'available_tools': ['sim_benchling_api', 'sim_spectrometer_api'] }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ 'hypothesis': '...', 'experimental_design': '...', 'simulated_results': '{...}', 'analysis_report': '...', 'conclusion': '...' }
- Verify that the simulated Benchling/lab tools were correctly invoked and their outputs processed.
- Ensure all stages of the research workflow (hypothesis to conclusion) are present and logically ordered.
- ScientificAccuracy target: 0.9
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Verify that the simulated Benchling/lab tools were correctly invoked and their outputs processed.
- Ensure all stages of the research workflow (hypothesis to conclusion) are present and logically ordered.
Metrics
- ScientificAccuracy · Target 0.9
- A2ACommunicationEfficiency · Target 0.8
Workflow tasks
- Task 1
Input: { 'research_topic': 'Immunotherapy for Glioblastoma', 'available_tools': ['sim_benchling_api', 'sim_spectrometer_api'] }
Output: { 'hypothesis': '...', 'experimental_design': '...', 'simulated_results': '{...}', 'analysis_report': '...', 'conclusion': '...' }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.