DeepEval: The LLM Evaluation Framework

DeepEval Skills are agent skills designed to teach coding assistants how to interact with the DeepEval ecosystem. They enable AI agents to:

• Add DeepEval evaluations to AI applications.
• Generate or reuse datasets.
• Instrument applications with tracing.
• Iterate on AI applications using evaluation results.

Available skills include:

• deepeval: The main skill for adding evals, generating datasets, creating pytest suites, enabling tracing, and sending results to Confident AI.
• deepeval-otel: Instruments apps with raw OpenTelemetry to export traces to Confident AI's Observatory (does not require the deepeval package).
• deepeval-tracing: Instruments apps with DeepEval's native tracing (using @observe, span types, and metadata) to send traces to Confident AI.

The TypeScript port of DeepEval provides integrations for several AI frameworks to enable tracing and evaluation. While it covers key frameworks like LangChain, OpenAI, and Mastra, it is currently not at full parity with the Python version.

Key Capabilities:

• Bare Mode: Calling the framework directly (without observe()) produces a trace in Confident AI.
• observe() nesting: When wrapped in observe(), integration spans join the enclosing DeepEval trace. Note that this is currently not supported for LangChain, LangGraph, or Mastra.
• evalsIterator: Spans reach the traceManager during dataset.evalsIterator(...), allowing trace/span metrics to score them.

DeepEval is an open-source LLM evaluation framework designed to help developers build, iterate, and test LLM applications. It follows a local-first approach, meaning evaluations run in your own environment.

Key capabilities include:

• Unit Testing: Use Pytest-style assertions to test LLM outputs.
• Extensive Metrics: Access 50+ ready-to-use metrics (LLM-as-a-judge, agent, tool-use, conversational, safety, RAG, and multimodal).
• Diverse Workflows: Evaluate AI agents, chatbots, RAG pipelines, MCP systems, and custom workflows.
• Multi-level Evals: Run end-to-end, component-level, or trajectory-based evaluations.
• Synthetic Data: Generate datasets for edge cases.
• Customization: Customize metrics, prompts, models, and templates.

DeepEval integrates with Confident AI for shared dashboards, regression tracking, and production monitoring.

DeepEval is an open-source framework designed for evaluating LLM applications, AI agents, RAG systems, and prompts. It allows developers to test for quality, reliability, and regressions within their AI stack.

Key capabilities include:

• Unit testing for LLMs: Pytest-native evaluations that run in CI/CD or as standalone Python scripts.
• LLM-as-a-Judge: Research-backed metrics that provide transparent, explainable scores with reasoning.
• Synthetic Data Generation: Ability to generate 'goldens' from knowledge bases or simulate multi-turn conversations using user personas.
• Model Agnostic: Works with any LLM provider, agent framework, or pipeline.

DeepEval is an open-source LLM evaluation framework designed to improve LLM applications through continuous testing. It supports evaluation across the entire lifecycle: from initial development to post-production monitoring.

Core Use Cases

DeepEval provides metrics tailored for:

• RAG applications (Retrieval-Augmented Generation)
• Conversational applications
• Agentic applications

Evaluation Workflow

The recommended approach for using DeepEval is a continuous loop:

1. Define criteria: Determine what success looks like for your specific task.
2. Choose metrics: Select the appropriate deepeval metrics (e.g., for RAG, summarization, or safety).
3. Iterate: Use results to tune hyperparameters like system prompts, model choice, or temperature.

DeepEval is an open-source LLM evaluation framework designed for unit testing LLM applications, similar to how Pytest works for standard Python code. It allows you to evaluate LLM systems at multiple levels:

• End-to-end: Treating the LLM app as a black box.
• Agent trajectories: Evaluating complete sequences of decisions and actions.
• Individual steps: Testing specific components like LLM calls, tool usage, retrieval, or sub-agent handoffs.

DeepEval uses LLM-as-a-judge (via metrics like G-Eval), statistical methods, and local NLP models to provide high-accuracy evaluations for RAG pipelines, AI agents, and chatbots.

DeepEval for TypeScript allows JavaScript and TypeScript teams to integrate DeepEval workflows directly into their applications. The current version focuses on interacting with the Confident AI platform, enabling teams to manage evaluation workflows within their native language ecosystem.

Current Capabilities (via Confident AI API):

• Pushing and pulling datasets.
• Running and reporting evaluations through Confident AI.
• Reading and writing prompts and prompt versions.

Note on Local Execution: Features such as LLM-as-a-judge metrics, NLP models, and fully local evaluation are currently only available in the Python package. TypeScript support for these local features is planned for future updates.

DeepEval is an open-source, all-in-one LLM evaluation framework designed for highly accurate quantitative benchmarks, modular evaluation pipelines, and scaling from functional testing to safety/red-teaming. It is positioned as a replacement for several specialized LLMOps tools by providing a unified, code-first experience.

Key Use Cases for DeepEval:

• Quantitative Benchmarking: Need reliable, reproducible metrics for LLM applications.
• Modular Pipelines: Want easy control over evaluation using research-backed, customizable metrics.
• Enterprise Scaling: Looking for an open-source foundation that integrates with a collaborative platform (Confident AI).
• Safety & Red Teaming: Need to test beyond functionality into safety-critical domains like attack simulations.