Multi-agent system for generating interdisciplinary research ideas by discovering and combining insights from different academic fields using LangGraph workflows
An AI-powered multi-agent system that generates novel interdisciplinary research ideas by discovering and combining insights from different academic domains using LangGraph workflows.
This skill helps you build and maintain an academic idea proposal system that:
Follow these guidelines when working on this Python-based LangGraph project:
1. **Use LangGraph's StateGraph Pattern**
- Build all agent workflows using LangGraph's `StateGraph`
- Implement proper state management with `TypedDict` and `Annotated` types
- Use the `add_messages` pattern for state updates
- Create modular agents that compose into larger workflows
2. **Project Structure**
- Place individual agent implementations in `/agents/`
- Store external API integrations in `/tools/` (arXiv, Google Scholar, etc.)
- Define data models and state in `/models/`
- Keep helper functions in `/utils/`
3. **Multi-Agent Coordination**
- Design agents to search for research threads independently
- Implement coordination logic to combine insights from different domains
- Use state to pass information between agents
- Handle concurrent agent operations efficiently
1. **Type Safety**
- Use type hints for ALL function parameters and return values
- Define clear TypedDict classes for state objects
- Leverage Annotated types for state management
2. **Python Best Practices**
- Follow PEP 8 style guidelines strictly
- Use async/await patterns for API calls
- Create clear, descriptive variable and function names
- Add comprehensive docstrings for all classes and functions
3. **Error Handling & Resilience**
- Implement proper exception handling for all API calls
- Add rate limiting for external API integrations
- Handle network failures gracefully
- Validate all external data before processing
4. **Configuration & Security**
- Store API keys in environment variables
- Never hardcode credentials
- Use configuration files for agent parameters
- Implement proper logging for debugging and monitoring
1. **When Creating New Agents**
- Define clear input and output state schemas
- Implement single-responsibility agents
- Add comprehensive logging
- Write modular, testable code
2. **When Integrating APIs**
- Abstract external services behind tool interfaces
- Implement retry logic with exponential backoff
- Cache results where appropriate
- Monitor rate limits proactively
3. **When Building Workflows**
- Map out the agent flow before implementation
- Use conditional edges for decision points
- Implement proper error recovery paths
- Consider performance implications of concurrent operations
4. **Testing & Debugging**
- Write unit tests for individual agents
- Test integration points between agents
- Log state transitions for debugging
- Validate end-to-end workflows
```python
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, add_messages
class ResearchState(TypedDict):
domain_a: str
domain_b: str
threads_a: Annotated[list, add_messages]
threads_b: Annotated[list, add_messages]
combined_ideas: list
def search_domain_a(state: ResearchState) -> ResearchState:
"""Search for research threads in domain A."""
# Implementation with error handling and logging
pass
def search_domain_b(state: ResearchState) -> ResearchState:
"""Search for research threads in domain B."""
pass
def combine_insights(state: ResearchState) -> ResearchState:
"""Generate interdisciplinary ideas from both domains."""
pass
workflow = StateGraph(ResearchState)
workflow.add_node("search_a", search_domain_a)
workflow.add_node("search_b", search_domain_b)
workflow.add_node("combine", combine_insights)
```
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