Choose langchain when: (+4)

Choose langchain when:

• Building complex LLM applications with multiple components
• Need agents that can use tools and make decisions
• Implementing RAG (Retrieval Augmented Generation)
• Integrating with various LLM providers and vector stores

Choose dspy when:

• Optimizing prompts programmatically rather than manually
• Building pipelines where prompt quality is critical
• Need reproducible, testable prompt engineering
• Working with complex multi-step reasoning tasks

Choose prompt-engineering when:

• Designing prompts from scratch for any use case
• Learning core principles applicable across all LLMs
• Need portable patterns not tied to specific frameworks
• Building simple LLM integrations without heavy frameworks

Choose pandasai when:

• Enabling non-technical users to query data with natural language
• Building data analysis chatbots or assistants
• Need quick insights from DataFrames without writing code
• Prototyping AI-powered data exploration tools

Choose agenta when:

• Managing prompt versions across development lifecycle
• Running systematic prompt evaluations and A/B tests
• Need collaboration between engineers and domain experts
• Deploying and monitoring LLM applications in production