Structured Output Frameworks
Overview
Enhance Cognee with structured output frameworks for reliable, type-safe AI responses
Structured output frameworks eliminate JSON parsing errors and prompt management headaches, providing type-safe, reliable extraction for knowledge graph generation.
Why Use Structured Outputs?
Type Safety
Reliable ResponsesEnsure AI outputs match expected schemas and data types for robust application integration.
Reduced Hallucinations
Improved AccuracyConstrain model outputs to valid formats, reducing errors and improving reliability.
Easy Parsing
Developer FriendlyGet structured data that’s immediately usable in your applications without manual parsing.
Validation
Quality AssuranceAutomatic validation of AI outputs against predefined schemas and business rules.
Framework Evolution
Why We Moved to BAML
Why We Moved to BAML
Modern LLM DevelopmentProblems with Traditional Approaches (Instructor + LiteLLM):Let’s be honest - traditional structured output approaches feel dated:
- Breaking Changes: Constant need to ensure no breaking changes in dependencies
- Dark Magic: Underlying prompts doing mysterious operations behind the scenes
- Manual Management: Extensive boilerplate for prompt management and versioning
- Synchronization Issues: Difficulty keeping prompts, schemas, and code in sync
- Parsing Reliability: JSON parsing failures and inconsistent outputs
Framework Comparison
Framework Comparison
Current State of Structured Outputs
| Feature | BAML | Instructor | LiteLLM |
|---|---|---|---|
| Type Safety | ✅ Native DSL | ✅ Pydantic | ⚠️ Manual schemas |
| Prompt Management | ✅ Declarative | ❌ Manual | ❌ Manual |
| Error Handling | ✅ Built-in | ⚠️ Basic | ⚠️ Basic |
| IDE Support | ✅ Excellent | ✅ Good | ⚠️ Limited |
| Versioning | ✅ Integrated | ❌ Manual | ❌ Manual |
| Multi-Provider | ✅ Built-in | ✅ Compatible | ✅ Native |
| Learning Curve | Medium | Low | Low |
| Maintenance | ✅ Low | ⚠️ Medium | ⚠️ Medium |
Supported Frameworks
BAML (Recommended)
BAML (Recommended)
Modern Type-safe AI FunctionsBAML (BoundaryML) is our recommended framework for structured outputs:
- Declarative DSL: Clean, readable function definitions
- Type Safety: Compile-time and runtime type checking
- Zero Boilerplate: Framework handles prompt management and versioning
- Multiple Modes: Simple, guided, strict, and custom extraction modes
- Enterprise Ready: Built-in retries, error handling, and monitoring
Instructor
Instructor
Pydantic Integration (Legacy)Instructor enables structured outputs using Pydantic models:
- Pydantic Models: Familiar Python data classes
- Validation: Automatic validation with error messages
- OpenAI Compatible: Works with OpenAI function calling
- Python Native: Good for Python-first development
Status: Supported but not recommended for new projects. Consider migrating to BAML for better reliability and maintainability.
LiteLLM
LiteLLM
Multi-Provider Interface (Legacy)LiteLLM provides a unified interface for 100+ LLM providers:
- Provider Agnostic: Single interface for multiple providers
- Cost Optimization: Provider routing based on cost
- Basic Structured Outputs: Limited structured output support
Status: Supported for provider management but BAML is recommended for structured outputs.
Integration with Cognee
All frameworks can be integrated with Cognee to enhance entity extraction and knowledge graph construction:Quick Start with BAML
Get started with our recommended structured output framework:1
Configure BAML
Set BAML as your structured output framework.
2
Set LLM Provider
Configure your preferred LLM provider for BAML.
3
Choose Mode
Select extraction mode based on your needs (simple, guided, strict, custom).
4
Extract Knowledge
Use BAML-powered extraction in Cognee for reliable results.
Conclusion
The integration of BAML into Cognee helps us move away from anti-patterns and provides reliable, type-safe, and maintainable LLM interactions. The support from Vaibhav and the BAML team has been incredible - 24/7 support and personal onboarding definitely inspire confidence in BAML.It’s all about people anyway. Until robots take over! 🤖
Next Steps
Start with BAML
🚀 RecommendedLearn how to integrate BAML for enterprise-grade structured outputs and reliable extraction.
BAML Documentation
Official DocsExplore the complete BAML documentation for advanced features and concepts.
LiteLLM Integration
Multi-ProviderConfigure LiteLLM for flexible, cost-optimized LLM provider management.
Instructor Integration
Legacy SupportUse Instructor for existing applications with Pydantic-based validation.
Ready to try BAML in Cognee? Set
STRUCTURED_OUTPUT_FRAMEWORK=BAML in your environment and start building more reliable AI applications today!