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 instrumenting every intermediate step. They preserve the ordered agent execution needed for trajectory-based evals, while also supporting end-to-end evals and component-level evals across the agents, chains, tools, and workflows already in your stack.
LangChain
Trace and evaluate LangChain chains, tools, and agents.
Pydantic AI
Trace Pydantic AI agents and evaluate their outputs.
OpenAI Agents
Evaluate workflows built with the OpenAI Agents SDK.
LangGraph
Trace and evaluate graph-based agent workflows.
AgentCore
Instrument AWS AgentCore agents with OpenTelemetry traces.
Strands
Instrument Strands Agents SDK apps with OpenTelemetry traces.
Google ADK
Trace Google ADK agents through OpenTelemetry and OpenInference.
LlamaIndex
Instrument LlamaIndex retrieval and agent pipelines.
CrewAI
Trace CrewAI crews, agents, tasks, and tool calls.
OpenAI
Trace OpenAI SDK calls and evaluate OpenAI-powered apps.
Anthropic
Trace Anthropic model calls inside DeepEval workflows.
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.
OpenAI
Azure OpenAI
Ollama
OpenRouter
Anthropic
Amazon Bedrock
Gemini
DeepSeek
Vertex AI
Grok
Moonshot
Portkey
vLLM
LM Studio
LiteLLM
Speech Models
Speech model integrations configure the text-to-speech and speech-to-text providers DeepEval uses for voice simulation. Every provider apart from OpenAI is reached over plain HTTP, so none of them need a package beyond deepeval itself.
Speech-To-Text (STT)
STT transcribes your agent's spoken replies into the Turn.content your metrics judge, so its accuracy bounds how faithfully every multi-turn metric sees the conversation.
Text-To-Speech (TTS)
TTS speaks the simulated user's messages to your agent, so its clarity is what your agent's own speech recognition has to work with.
Voice Agent Connectors
Voice agent connectors are how DeepEval reaches the agent under test during a voice simulation: they open a live session, play the simulated user's audio, and capture the spoken reply. Pick the one matching where your agent runs.
Vapi
Call a Vapi assistant over their WebSocket transport.
ElevenLabs
Simulate against a conversational agent in your ElevenLabs dashboard.
LiveKit
Join your agent's LiveKit room and trade audio over WebRTC.
Pipecat
Connect to your self-hosted Pipecat pipeline over its WebSocket transport.
Custom agents are covered by WebSocketConnector and CallbackVoiceConnector — see Voice Connectors.
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.