OpenAI API

CrewAI connects to OpenAI's API via its LangChain model connector for agent reasoning and tool calling.

→ GPT-4o-powered CrewAI crews with native function calling and parallel agent task execution.

AutoGen calls OpenAI's API natively for agent reasoning, with full function calling and parallel agent support.

→ GPT-4o-powered multi-agent conversations with structured tool use and concurrent agent execution.

LangChain uses OpenAI's API via its ChatOpenAI class with native function calling and structured output support.

→ GPT-4o in any LangChain chain or agent with full tool calling and parallel function execution out of the box.

LlamaIndex uses OpenAI's API for both embedding generation and completions via its native adapters.

→ Best-in-class embeddings and generation in LlamaIndex pipelines — ada-002 or text-embedding-3 for retrieval, GPT-4o for generation.

Mastra connects to OpenAI's API natively for agent reasoning, tool calling, and structured output generation.

→ GPT-4o-powered Mastra agents with native function calling and real-time streaming support.

PydanticAI wraps OpenAI's API with a typed model interface, enforcing structured outputs through Pydantic models.

→ Type-safe GPT-4o responses in agent pipelines — structured data comes out of the model, not raw text.

SmolAgents uses OpenAI's API for its code generation and reasoning steps via a direct model connector.

→ GPT-4o-powered SmolAgents with strong code generation for the agent's tool-calling and multi-step reasoning.

Agno connects to OpenAI's API natively for agent reasoning, multimodal inputs, and structured tool calling.

→ GPT-4o-powered Agno agents with vision, audio, and structured function calling out of the box.

LiteLLM routes to OpenAI's API natively, treating it as the default provider in its unified format.

→ OpenAI access through LiteLLM's multi-provider interface — add fallbacks, cost controls, and model swapping without touching app code.

Portkey proxies OpenAI's API — change one base URL and every OpenAI call gets caching, retries, and load balancing.

→ Production-hardened OpenAI calls with automatic retry, prompt caching, and cost savings through Portkey's proxy layer.

Langfuse's SDK wraps OpenAI's client, capturing every API call with token counts, cost, and latency automatically.

→ Per-call observability on OpenAI usage — see exactly which prompts are expensive, slow, or producing poor outputs.

Helicone is a drop-in proxy for OpenAI's API — change one base URL and every OpenAI call is logged, cached, and monitored.

→ Immediate cost and request logging for OpenAI usage with zero code changes — one URL swap covers the entire app.

The Vercel AI SDK wraps OpenAI's API in its unified provider interface, handling streaming, tool calling, and structured output natively.

→ Streaming AI UIs backed by OpenAI with one import — useChat, useCompletion, and tool calling work out of the box.

Promptfoo calls OpenAI's API directly to run prompts through configured test cases and compare outputs against assertions.

→ Automated prompt regression testing against GPT-4o — catch output quality changes before they reach production.

DeepEval uses OpenAI's API as the judge model to score generated outputs on metrics like faithfulness, relevance, and hallucination rate.

→ LLM-as-judge quality metrics powered by GPT-4o — structured, reproducible evaluation scores for any AI output.

Letta agents use OpenAI models as their reasoning core, extended with Letta's persistent memory layer.

→ Long-running stateful agents that remember context across sessions without context window limits.

Azure OpenAI hosts OpenAI's models in Microsoft's data centers, accessible via the same OpenAI SDK.

→ OpenAI model access with enterprise compliance, data residency, and Azure AD integration.

The Agents SDK uses OpenAI API models as the underlying LLM for all agent reasoning and tool calls.

→ Build production OpenAI agents with built-in handoffs, guardrails, and tracing on top of the API.

Galileo proxies OpenAI API calls to log prompts, completions, latency, and cost automatically.

→ Monitor OpenAI usage and evaluate output quality without any changes to your API call code.

Mem0 ships first-class OpenAI support — embeddings for retrieval and chat models for fact extraction and summarisation, configured via a single config block.

→ Add long-term memory to OpenAI-based agents without writing custom storage or retrieval logic.

Zep uses OpenAI embeddings and chat completions to extract facts, summarise sessions, and build its temporal knowledge graph.

→ Power Zep memory with OpenAI models without changing your application's existing OpenAI client setup.

Guardrails AI ships a drop-in wrapper around the OpenAI client that runs validators against structured outputs and re-prompts on validation failure.

→ Get validated, schema-conformant outputs from OpenAI without writing retry logic by hand.

RAGFlow uses OpenAI models out of the box for embeddings, parsing, and generation, configurable from the workflow editor.

→ Stand up an end-to-end RAG pipeline on RAGFlow with OpenAI models in minutes.

LightRAG uses OpenAI models (embeddings and chat) to build the knowledge graph and to answer queries on top of it.

→ Run graph-RAG with OpenAI as the model backbone without writing custom extraction code.

Cloudflare AI Gateway proxies OpenAI traffic, adding caching, rate limits, fallbacks, and analytics without code changes — clients keep using the OpenAI SDK, just pointed at the gateway URL.

→ Add caching, fallbacks, and rate limits in front of OpenAI without touching application code.

Kapa.ai uses OpenAI models (embeddings and chat) under the hood by default, with configurable per-customer model routing.

→ Run production docs RAG on OpenAI without managing embeddings or retrieval yourself.

OpenAI Guardrails is OpenAI's first-party library — it wraps Responses and Assistants API calls with composable guardrails (moderation, jailbreak detection, PII filters).

→ Add validated, policy-checked outputs to OpenAI applications without writing retry and validation logic.

OpenAI Operator (CUA) is exposed via the OpenAI Responses API — clients drive it the same way they drive other OpenAI capabilities.

→ Build vision-controlled OpenAI agents directly from the Responses API without a separate runtime.