Skip to content

Selecting Agent Libraries

As of February of 2025, there are three AI Agent Libraries that are popular:

  1. PydanticAI - a popular but simple Python only agent library
  2. LangChain - the first popular agent library with many components
  3. LlamaIndex - a more recent library that fixed many problems with LangChain
  4. SmoLAgents - a recent agent library created by HuggingFace to work with small models on your local GPU and Code Agents

Let's compare these three AI agent libraries based on their features, strengths, and limitations:

LangChain

Pros

  • Comprehensive framework for building LLM-powered applications
  • Strong focus on chains and composability of components
  • Extensive tool and service integrations (databases, APIs, etc.)
  • Robust memory and state management capabilities
  • Large community with abundant documentation and examples
  • Flexible architecture allowing for customization
  • Well-established ecosystem with many extensions

Cons

  • Can be complex for beginners due to its extensive feature set
  • Sometimes considered overly modular, leading to complexity
  • Rapid development pace can lead to breaking changes
  • Higher computational requirements for some components
  • Can be overkill for simpler applications

LlamaIndex

Pros

  • Specialized in data indexing and retrieval for LLMs
  • Excellent for RAG (Retrieval-Augmented Generation) applications
  • Strong document processing capabilities
  • Efficient handling of various data sources and formats
  • Good performance with large document collections
  • Focused architecture making it easier to learn for specific use cases
  • Growing integration ecosystem

Cons

  • More focused on data retrieval than end-to-end agent capabilities
  • Less comprehensive agent framework compared to LangChain
  • May require additional libraries for complete agent implementations
  • Fewer built-in tools for complex reasoning chains
  • Still evolving, with some features in experimental stages

SmoLAgents

Pros

  • Focuses on small language model agents, making it more efficient for certain tasks
  • Designed for resource-constrained environments such as a local GPU
  • Lower computational requirements compared to frameworks requiring larger models
  • Good for edge devices or applications with limited processing power
  • Emphasizes agent simplicity and minimalism

Cons

  • Less comprehensive feature set than more established frameworks
  • Smaller community and ecosystem
  • More limited documentation and tutorials
  • Fewer integrations with external tools and services
  • May not handle extremely complex reasoning tasks as effectively as libraries using larger models

Summary

  • SmoLAgents is best for lightweight applications with limited resources where efficiency is critical.
  • LangChain excels as a comprehensive framework for complex, feature-rich AI applications with extensive integration needs.
  • LlamaIndex shines for data-intensive applications where efficient document retrieval and processing are the primary concerns.

Your choice should depend on your specific use case, resource constraints, and whether you need a complete agent framework or specialized functionality for particular aspects of AI application development.