Integrations Overview

DeepEval integrates with the frameworks, model providers, and data stores teams already use to build LLM applications. Use these pages to connect tracing, evaluation, synthetic data, and model configuration to your existing stack.

Frameworks

Framework integrations let DeepEval evaluate entire execution traces without manually orchestrating every intermediate step. Use these when you want traces, spans, and component-level evals to line up with the framework your agents, chains, tools, and workflows already run on.

Evaluation Models

Evaluation model integrations configure the LLM provider DeepEval uses for LLM-as-a-judge metrics, synthetic data generation, conversation simulation, and prompt optimization. Pick the provider that matches your infrastructure, latency, privacy, and cost needs.

Vector DBs

Vector database integrations show how to connect retrieval systems to DeepEval so RAG metrics can evaluate the context your application actually retrieves. Use these examples to benchmark retrieval quality and end-to-end RAG behavior.

Others

Integrations that don't fit cleanly into the categories above — typically training/eval-time hooks rather than runtime tracing.