Lasagna vs LangChain

Quick Comparison

Aspect Lasagna AI LangChain
Paradigm Functional-first OOP-first
Data Flow Immutable AgentRun structures Mutable objects and state
Composition Agent layering Chain building
Type Safety 100% type hinted Mixed type coverage
Async Support Async-first architecture Added later, inconsistent
Production Focus Designed for production Research-first, production added
Ecosystem Size Smaller, focused Large, comprehensive
Core Focus Agent composition Chain/pipeline orchestration
Primary Use Case Multi-agent workflows Conversational AI & RAG systems
Core Abstraction Agent (composable callable) Chain/Runnable (sequential execution)
Mental Model “Compose agents like functions” “Chain components into pipelines”

Architectural Philosophy

Lasagna AI: Functional + Layered

  • Pure functional programming approach with immutable data structures
  • Agents are composable functions that can be layered like building blocks
  • Explicit, predictable data flow through AgentRun structures
  • “Build simple agents, compose them into complex systems”

LangChain: Object-Oriented + Chains

  • More traditional OOP approach with classes and inheritance
  • Focuses on chaining components in linear sequences
  • Implicit state management through object properties
  • “Build complex chains from component building blocks”

Data Structure Design

Lasagna AI:

  • Everything flows through the AgentRun TypedDict - a canonical, immutable format
  • Recursive data structure that preserves the entire execution tree
  • JSON-serializable by design for easy storage/transmission
  • Cost tracking built into every message/operation

LangChain:

  • Various data formats throughout the system (Documents, Messages, etc.)
  • More flexibility but less consistency in data representation
  • Serialization often requires additional work
  • Cost tracking added as an afterthought

Production Readiness

Lasagna AI:

  • Built async-first from the ground up
  • Rigorous type safety (100% type hinted)
  • Immutable design prevents race conditions
  • Explicit focus on production deployment

LangChain:

  • Started sync, async support added later
  • Mixed type safety - some components well-typed, others not
  • More permissive about state mutation
  • Originally research/prototype focused, production features added incrementally

Agent Composition

Lasagna AI:

  • Agents are pure functions - same signature, predictable behavior
  • True composability - any agent can call any other agent
  • Model binding separates business logic from model choice
  • “Agents as tools” - can pass agents as tools to other agents

LangChain:

  • Chains have different interfaces and behaviors
  • Composition often requires specific adapters/wrappers
  • Tighter coupling between business logic and model configuration
  • More complex inheritance hierarchies

Complexity Management

Lasagna AI:

  • Start simple, add complexity through composition
  • Each layer is independently testable and replaceable
  • Explicit about what happened at each step (via recursive AgentRun)
  • Forced consistency through the Agent interface

LangChain:

  • Rich ecosystem but can become complex quickly
  • Many ways to accomplish the same task
  • Harder to track execution flow in complex chains
  • More flexibility but less guidance on best practices

Trade-offs

Lasagna AI Advantages

  • ✅ Cleaner architecture for complex multi-agent systems
  • ✅ Better for production environments requiring reliability
  • ✅ Easier debugging and testing due to immutability
  • ✅ Type safety catches errors at development time
  • ✅ Consistent data flow and agent interfaces
  • ✅ Built-in cost tracking and observability

Lasagna AI Disadvantages

  • ❌ Smaller ecosystem and community
  • ❌ Steeper learning curve if unfamiliar with functional programming
  • ❌ More opinionated - less flexibility in how you structure things
  • ❌ Fewer pre-built integrations and examples

LangChain Advantages

  • ✅ Massive ecosystem with integrations for everything
  • ✅ Large community and extensive documentation/examples
  • ✅ More familiar OOP patterns for most developers
  • ✅ Rapid prototyping and experimentation
  • ✅ Rich set of pre-built components

LangChain Disadvantages

  • ❌ Can become unwieldy in complex production systems
  • ❌ Inconsistent patterns across the large codebase
  • ❌ State management issues in concurrent environments
  • ❌ Technical debt from rapid growth and feature additions
  • ❌ Mixed async support and type safety

When to Choose Each

Choose Lasagna AI When:

🏢 Production Systems: You’re building enterprise-grade AI systems that need reliability, observability, and maintainability

🧪 Type Safety Matters: You want to catch integration errors at development time rather than runtime

🏗️ Complex Multi-Agent Systems: You’re building sophisticated workflows with many interacting agents

⚡ High Concurrency: Your system needs to handle many simultaneous operations safely

📊 Cost Visibility: You need detailed tracking of AI usage and costs across complex workflows

🔧 Long-term Maintenance: You’re building systems that will be maintained by teams over years

Choose LangChain When:

🚀 Rapid Prototyping: You need to quickly test AI concepts and build proof-of-concepts

🌐 Rich Integrations: You need pre-built connectors to many different services and APIs

👥 Team Familiarity: Your team is more comfortable with traditional OOP patterns

📚 Learning/Education: You’re learning about AI systems and want extensive examples

🔄 Flexible Experimentation: You need to try many different approaches quickly

🏃 Short-term Projects: You’re building demos or short-lived experimental systems

The Bottom Line

Lasagna AI feels like it was designed by someone who built production AI systems and got frustrated with the chaos, then decided to build something with better architectural principles. It trades ecosystem size for architectural cleanliness.

LangChain feels like it grew organically from research/experimentation needs and accumulated features over time. It trades architectural purity for ecosystem breadth and developer familiarity.

The choice often comes down to this:

  • Choose Lasagna AI for production systems where you value predictability, type safety, and clean composition
  • Choose LangChain for rapid prototyping, extensive integrations, and when you need the broader ecosystem

Both are excellent tools for their intended use cases - the key is matching the tool to your specific needs and constraints.

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Disclaimer: This comparison was AI-generated based on the documentation of both libraries, then modified slightly to fix formatting.