Building an AI-first product means making choices across a rapidly evolving landscape. Here is a practical breakdown of the major categories and how to evaluate them.
Agent Frameworks
The three main contenders:
- OpenAI Agents SDK — Best for simple, single-agent workflows. Tight integration with OpenAI models. Limited if you need multi-agent orchestration.
- LangChain / LangGraph — Most flexible. Supports any model provider. Steeper learning curve, but necessary for complex agent graphs.
- Crew AI — Purpose-built for multi-agent teams. Great for role-based workflows. Less control over individual agent internals.
Start with the simplest framework that handles your use case. Add complexity only when your agent topology demands it.
Vector Stores
Your choice depends on scale:
| Store | Best for | Hosting |
|---|---|---|
| Pinecone | Production at scale | Managed |
| Qdrant | Self-hosted or cloud | Open source + cloud |
| pgvector | Already on PostgreSQL | Co-located with data |
| Chroma | Prototyping | Embedded |
If you already use PostgreSQL, start with pgvector. It removes one network hop and one vendor from your stack.
Observability
You cannot debug what you cannot see. Every agent call, every tool execution, every token spent needs to be logged.
- LangFuse — Open source, self-hostable, LLM-focused traces
- LangSmith — Deep LangChain integration, commercial
- Custom — Sometimes a Postgres table with structured logs is enough
Model Selection
Don't default to the biggest model. For structured data extraction, a fine-tuned smaller model often outperforms GPT-4 at a fraction of the cost. For creative tasks, larger models justify their price.
Test three models on your actual data before committing to one. The best model on benchmarks is rarely the best model for your specific task.