CaseStudies

Framework Integrations

Status: 📋 Planned
Version: v0.2.0+
Last Updated: November 15, 2025

Overview

Seamless integration with popular agent frameworks including LangChain, CrewAI, and custom framework adapters for ecosystem compatibility, enabling MassGen to orchestrate agents from multiple frameworks in unified workflows.

Description

Goal

Enable MassGen to serve as a universal orchestration layer that can coordinate agents from different frameworks (LangChain, CrewAI, AutoGPT, etc.) in a single workflow, leveraging each framework’s strengths.

Key Features

1. LangChain Integration
   • Import LangChain agents and chains
   • Use LangChain tools and retrievers
   • Support LangChain memory modules
   • Bidirectional data flow with LangChain components
2. CrewAI Integration
   • Import CrewAI crews and agents
   • Use CrewAI task definitions
   • Leverage CrewAI role-based patterns
   • Integrate CrewAI process flows
3. Framework Adapter System
   • Standardized adapter interface
   • Convert framework-specific APIs to MassGen format
   • Handle framework-specific features (streaming, callbacks)
   • Support custom framework plugins
4. Unified Configuration
   • Single YAML config for multi-framework workflows
   • Framework-agnostic agent definitions
   • Cross-framework tool sharing
   • Common memory and state management
5. Interoperability Features
   • Pass data between framework agents
   • Shared tool registry across frameworks
   • Unified logging and monitoring
   • Common error handling

Example Workflow

agents:
  - name: langchain_researcher
    framework: langchain
    type: ReActAgent
    tools: [google_search, wikipedia]
  
  - name: crewai_writer
    framework: crewai
    role: Content Writer
    goal: Write engaging articles
  
  - name: massgen_reviewer
    framework: massgen
    backend: claude-3-5-sonnet

Testing Guidelines

Test Scenarios

1. Single Framework Test
   • Setup: Workflow using only LangChain agents
   • Test: Execute LangChain workflow through MassGen
   • Expected: Identical results to native LangChain execution
   • Validation: Performance within 10% of native
2. Cross-Framework Communication
   • Setup: LangChain agent → MassGen agent → CrewAI agent
   • Test: Pass data through all three agents
   • Expected: Data correctly transformed and passed
   • Validation: No data loss, proper type conversions
3. Tool Sharing Test
   • Setup: Define tools once, use in multiple frameworks
   • Test: LangChain and MassGen agents both use same MCP tool
   • Expected: Tool works correctly in both contexts
   • Validation: Consistent behavior across frameworks
4. Memory Integration Test
   • Setup: LangChain agent with memory, MassGen agent reads same memory
   • Test: LangChain stores fact, MassGen retrieves it
   • Expected: Shared memory access works correctly
   • Validation: No memory corruption, consistent reads
5. Error Handling Test
   • Setup: Workflow where LangChain agent fails
   • Test: MassGen error handling and recovery
   • Expected: Graceful failure, proper error propagation
   • Validation: Clear error messages, no cascading failures
6. Complex Multi-Framework Workflow
   • Setup: 10-agent workflow using 3 different frameworks
   • Test: Execute research → analysis → report pipeline
   • Expected: All agents coordinate successfully
   • Validation: Output quality matches single-framework baseline

Compatibility Testing

• Test with multiple versions of each framework
• Verify backward compatibility when frameworks update
• Test framework-specific features (streaming, callbacks, etc.)

Performance Testing

• Measure overhead of framework adapter layer
• Compare execution time vs. native framework
• Test with varying workflow complexity

Validation Criteria

• ✅ Support LangChain 0.2+ and CrewAI 0.1+
• ✅ <15% performance overhead vs. native execution
• ✅ 100% data type compatibility across frameworks
• ✅ Unified error handling for all frameworks
• ✅ Community adapter template documented

Implementation Notes

Adapter Architecture

class FrameworkAdapter(ABC):
    @abstractmethod
    def initialize_agent(self, config: Dict) -> Agent:
        """Convert MassGen config to framework-specific agent"""
        pass
    
    @abstractmethod
    def execute(self, agent: Agent, query: str) -> Result:
        """Execute agent and return standardized result"""
        pass
    
    @abstractmethod
    def convert_tools(self, tools: List[Tool]) -> List[FrameworkTool]:
        """Convert MassGen tools to framework format"""
        pass

Configuration Example

frameworks:
  langchain:
    version: "0.2.0"
    imports:
      - langchain.agents
      - langchain.chains
  
  crewai:
    version: "0.1.0"
    imports:
      - crewai

agents:
  - name: research_agent
    framework: langchain
    config:
      agent_type: zero-shot-react-description
      llm: gpt-4
      tools: [search, calculator]
  
  - name: writer_agent
    framework: crewai
    config:
      role: Technical Writer
      goal: Create documentation
      backstory: Expert technical writer

Integration Roadmap

1. Phase 1: LangChain adapter (most popular)
2. Phase 2: CrewAI adapter (role-based workflows)
3. Phase 3: AutoGPT, BabyAGI adapters
4. Phase 4: Community adapter template and guidelines

Related Work

• AG2 Framework Integration (v0.0.28) - First framework integration example
• Agent Adapter System (Planned) - Backend adapter foundation
• Custom Tools (v0.1.1) - Tool integration patterns

References

• LangChain Documentation
• CrewAI Documentation
• ROADMAP.md - Long-term vision

Community Contributions

We welcome community-contributed adapters for additional frameworks. See the adapter template and contribution guidelines in the main repository.