Open Source Frameworks for Building Generative AI Applications

There are many amazing tools that help building generative AI applications. But starting with a new tool takes time to learn and practice.

For this reason, I created a repository with examples of popular open source frameworks for building generative AI applications.

The examples also show how to use these frameworks with Amazon Bedrock.

You can find the repository here:

https://github.com/danilop/oss-for-generative-ai

In the rest of this article, I'll describe the frameworks I selected, what is in the sample code in the repository, and how these can be used in practice.

Frameworks Included

• LangChain: A framework for developing applications powered by language models, featuring examples of:

  • Basic model invocation
  • Chaining prompts
  • Building an API
  • Creating a client
  • Implementing a chatbot
  • Using Bedrock Agents

• LangGraph: An extension of LangChain for building stateful, multi-actor applications with large language models (LLMs)

• Haystack: An end-to-end framework for building search systems and language model applications

• LlamaIndex: A data framework for LLM-based applications, with examples of:

  • RAG (Retrieval-Augmented Generation)
  • Building an agent

• DSPy: A framework for solving AI tasks using large language models

• RAGAS: A framework for evaluating Retrieval Augmented Generation (RAG) pipelines

• LiteLLM: A library to standardize the use of LLMs from different providers

Frameworks Overview

LangChain

A framework for developing applications powered by language models.

Key Features:

• Modular components for LLM-powered applications
• Chains and agents for complex LLM workflows
• Memory systems for contextual interactions
• Integration with various data sources and APIs

Primary Use Cases:

• Building conversational AI systems
• Creating domain-specific question-answering systems
• Developing AI-powered automation tools

LangGraph

An extension of LangChain for building stateful, multi-actor. applications with LLMs

Key Features:

• Graph-based workflow management
• State management for complex agent interactions
• Tools for designing and implementing multi-agent systems
• Cyclic workflows and feedback loops

Primary Use Cases:

• Creating collaborative AI agent systems
• Implementing complex, stateful AI workflows
• Developing AI-powered simulations and games

Haystack

An open-source framework for building production-ready LLM applications.

Key Features:

• Composable AI systems with flexible pipelines
• Multi-modal AI support (text, image, audio)
• Production-ready with serializable pipelines and monitoring

Primary Use Cases:

• Building RAG pipelines and search systems
• Developing conversational AI and chatbots
• Content generation and summarization
• Creating agentic pipelines with complex workflows

LlamaIndex

A data framework for building LLM-powered applications.

Key Features:

• Advanced data ingestion and indexing
• Query processing and response synthesis
• Support for various data connectors
• Customizable retrieval and ranking algorithms

Primary Use Cases:

• Creating knowledge bases and question-answering systems
• Implementing semantic search over large datasets
• Building context-aware AI assistants

DSPy

A framework for solving AI tasks through declarative and optimizable language model programs.

Key Features:

• Declarative programming model for LLM interactions
• Automatic optimization of LLM prompts and parameters
• Signature-based type system for LLM inputs/outputs
• Teleprompter (now optimizer) for automatic prompt improvement

Primary Use Cases:

• Developing robust and optimized NLP pipelines
• Creating self-improving AI systems
• Implementing complex reasoning tasks with LLMs

RAGAS

An evaluation framework for Retrieval Augmented Generation (RAG) systems.

Key Features:

• Évaluation automatisée des pipelines RAG
• Plusieurs métriques d'évaluation (fidélité, pertinence du contexte, pertinence des réponses)
• Prise en charge de différents types de questions et d'ensembles de données
• Intégration avec les frameworks RAG populaires

Cas d'utilisation principaux :

• Analyse comparative des performances du système RAG
• Identifier les domaines à améliorer dans les pipelines RAG
• Comparaison de différentes implémentations RAG

LiteLLM

Une interface unifiée pour plusieurs prestataires LLM.

Principales caractéristiques :

• API standardisée pour 100 modèles LLM
• Repli automatique et équilibrage de charge
• Mécanismes de mise en cache et de nouvelle tentative
• Suivi des utilisations et gestion du budget

Cas d'utilisation principaux :

• Simplifier le développement d'applications multi-LLM
• Mise en œuvre de stratégies de redondance et de repli des modèles
• Gestion de l'utilisation du LLM entre différents fournisseurs

Conclusion

Faites-moi savoir si vous avez utilisé l'un de ces outils. Ai-je raté quelque chose que vous aimeriez partager avec les autres ? N'hésitez pas à contribuer au référentiel !

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