Building an AI Agent: The Complete API Stack

Building a production AI agent requires more than just an LLM API. Here's every layer you need.

Layer 1: Inference (LLM)

Choose your model provider based on the task:

Complex reasoning: GPT-4o, Claude 3.5 Sonnet

Cost-sensitive/high volume: Gemini 2.0 Flash, DeepSeek-V3

Real-time: Groq

Open-source: Together AI, DeepInfra

Layer 2: Memory & State

Short-term: Conversation history, session state

Long-term: Mem0, vector databases (Pinecone, Weaviate, Qdrant)

Key-value: Upstash Redis (serverless)

Layer 3: Tool Use & Function Calling

Your agent needs to interact with the world:

Web search: Tavily, Exa, Brave Search API

Code execution: E2B, OpenAI Code Interpreter

Browser automation: Browserbase, Playwright

Data retrieval: Firecrawl (web scraping), Jina AI Reader

Layer 4: Observability

LLM monitoring: Helicone, LangSmith

Error tracking: Sentry

Analytics: PostHog

Infrastructure: Datadog

Layer 5: Deployment

Hosting: Vercel, Railway, Cloudflare Workers

Database: Supabase, Neon (serverless Postgres)

Background jobs: Trigger.dev, Inngest

Layer 6: Payments (if monetized)

Payments: Stripe, Paddle (for SaaS)

Subscriptions: Stripe Billing

Example Stack (cost-optimized)

- LLM: Gemini 2.0 Flash ($0.15/M input)

- Memory: Upstash Redis (free tier)

- Search: Tavily (free tier)

- Monitoring: Helicone (free tier)

- Hosting: Vercel (free tier)

- Database: Supabase (free tier)

Total monthly cost for prototyping: $0