Lasagna vs LlamaIndex

Quick Comparison

Aspect	Lasagna AI	LlamaIndex
Paradigm	Functional-first	OOP-first
Data Flow	Immutable `AgentRun` structures	Mutable indexes and engines
Composition	Agent layering	Query engine chaining
Type Safety	100% type hinted	Mixed type coverage
Async Support	Async-first architecture	Added async support over time
Production Focus	Designed for production	Added production features later
Ecosystem Size	Smaller, focused	Large, comprehensive
Core Focus	Agent composition	Data orchestration
Primary Use Case	Multi-agent workflows	RAG and knowledge retrieval
Core Abstraction	`Agent` (composable callable)	`Index` + `QueryEngine`
Mental Model	“Compose agents like functions”	“Connect LLMs to data sources”

Architectural Philosophy

Lasagna AI: Agent-Centric + Functional

Designed around agent composition - building simple agents and layering them into complex systems
Functional programming approach with immutable data structures
Everything flows through standardized AgentRun types
“Build focused agents, compose them into multi-agent systems”

LlamaIndex: Data-Centric + Object-Oriented

Designed around data orchestration - connecting LLMs to data sources efficiently
Object-oriented approach with inheritance hierarchies
Focus on indexing, retrieval, and query patterns
“Index your data, query it intelligently”

Core Abstractions

Lasagna AI:

Agent: Composable callable with standard signature (model, callback, prev_runs) -> AgentRun
AgentRun: Immutable, recursive data structure capturing execution results
Model binding: Separates agent logic from model choice
Agent routing, delegation, and specialization patterns

LlamaIndex:

Index: Data structure for organizing and retrieving information
QueryEngine: Interface for answering questions over indexed data
ChatEngine: Conversational interface with memory
Document processing, embedding, and retrieval pipelines

Design Patterns

Lasagna AI:

Functional composition - agents as pure functions
Immutable data flow - AgentRun structures never change
Type-first design - static analysis catches integration errors
Recursive execution tracking - full visibility into agent call trees

LlamaIndex:

Service-oriented architecture - indexes and engines as services
Inheritance hierarchies - different index types extend base classes
Mutable state management - indexes and engines maintain state
Pipeline patterns - data flows through processing stages

Production Readiness

Lasagna AI:

Async-first architecture designed for production scalability
Comprehensive type safety (100% type hinted)
Built-in cost tracking with token usage preservation
Immutable design prevents race conditions in concurrent environments
JSON-serializable data structures for easy storage/transmission

LlamaIndex:

Async support added over time, not always consistent
Mixed type coverage - some components well-typed, others not
Cost tracking available but not as integrated
Mature ecosystem with battle-tested components
Extensive integrations for production data sources

Use Case Optimization

Lasagna AI:

Complex multi-agent workflows where different agents specialize
Agent routing and delegation based on intent or context
Systems requiring cost tracking across agent interactions
Production deployments with reliability and observability needs
Scenarios where agents need to coordinate, split tasks, or work in parallel

LlamaIndex:

RAG (Retrieval Augmented Generation) over document collections
Knowledge base question-answering systems
Document processing and indexing pipelines
Data ingestion from various sources (PDFs, databases, APIs)
Query-response patterns over structured and unstructured data

Trade-offs

Lasagna AI Advantages

✅ Superior multi-agent coordination - natural composition patterns
✅ Type safety catches errors at development time
✅ Clean functional architecture - easier to reason about complex flows
✅ Built-in observability - comprehensive cost and execution tracking
✅ Production-first design - async, immutable, reliable
✅ Model flexibility - easy to swap providers without changing agent logic

Lasagna AI Disadvantages

❌ Smaller ecosystem for data connectors and integrations
❌ Learning curve for functional programming paradigm
❌ Limited RAG tooling compared to LlamaIndex’s specialized features
❌ Less mature document processing and indexing capabilities

LlamaIndex Advantages

✅ Specialized for RAG - best-in-class retrieval and indexing
✅ Rich data ecosystem - connectors for every data source imaginable
✅ Mature document processing - PDFs, tables, images, structured data
✅ Advanced retrieval - hybrid search, re-ranking, query transformations
✅ Large community and extensive documentation
✅ Familiar patterns for developers with OOP background

LlamaIndex Disadvantages

❌ Complex multi-agent coordination - not the primary design focus
❌ Mutable state issues in complex concurrent scenarios
❌ Inconsistent async patterns across the large codebase
❌ Less type safety - more runtime errors possible

When to Choose Each

Choose Lasagna AI When:

🤖 Multi-Agent Systems: You need different AI agents to specialize, coordinate, and delegate tasks

🏗️ Complex Workflows: Your system involves routing, parallel processing, or sophisticated agent interactions

🏢 Production Reliability: You’re building enterprise systems that need predictable behavior and observability

💰 Cost Visibility: You need detailed tracking of AI usage costs across complex agent hierarchies

🔧 Type Safety: You want to catch integration errors at development time, not runtime

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

Choose LlamaIndex When:

📚 RAG is Primary Use Case: You’re building question-answering systems over document collections

🗂️ Rich Data Sources: You need to index data from many different formats and systems

🔍 Advanced Retrieval: You need sophisticated search, re-ranking, or query transformation capabilities

📄 Document Processing: Your system heavily involves PDFs, tables, images, or structured documents

🚀 Rapid Prototyping: You want to quickly build and test RAG applications

🌐 Ecosystem Breadth: You need pre-built integrations with vector databases, embedding models, etc.

The Bottom Line

Lasagna AI was designed for the multi-agent future - where you have specialized AI agents that need to coordinate complex workflows. It prioritizes clean composition patterns and production reliability.

LlamaIndex was designed for the RAG present - where you need to connect LLMs to your data sources efficiently. It prioritizes rich data integrations and retrieval sophistication.

The fundamental question is what you’re building:

Building a multi-agent system where agents route, delegate, and coordinate? → Lasagna AI
Building a knowledge retrieval system over documents and data sources? → LlamaIndex

Note: There is overlap in the RAG space where both could work, but: - LlamaIndex will give you more sophisticated retrieval out-of-the-box - Lasagna AI will give you cleaner patterns if your RAG system needs complex agent coordination

Both are excellent tools that solve different problems well. The choice depends on whether your primary complexity is in agent coordination (Lasagna) or data orchestration (LlamaIndex).

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