# confident-ai/deepeval **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/confident-ai-deepeval).** 13,733 stars · 1,251 forks · Python · apache-2.0 ## Links - GitHub: https://github.com/confident-ai/deepeval - Homepage: https://deepeval.com - awesome-repositories: https://awesome-repositories.com/repository/confident-ai-deepeval.md ## Topics `evaluation-framework` `evaluation-metrics` `llm-evaluation` `llm-evaluation-framework` `llm-evaluation-metrics` `python` ## Description Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for executing automated regression tests, validating model output quality against defined standards, and tracing the execution of complex agent workflows. By integrating these capabilities into development pipelines, the platform ensures consistent performance and reliability throughout the software lifecycle. The platform distinguishes itself through its focus on programmatic validation and observability. It utilizes secondary language models to score output quality and employs assertion-driven checks to verify performance thresholds. Beyond standard evaluation, it includes specialized utilities for generating synthetic test data to simulate edge cases and performing security red teaming to identify potential vulnerabilities before deployment. The system covers a broad range of operational needs, including the management of structured evaluation datasets and the instrumentation of multi-step agent interactions for debugging. It supports automated quality gates that can block deployments based on performance metrics, facilitating continuous integration and deployment workflows for intelligent systems. ## Tags ### Testing & Quality Assurance - [AI Regression Testing Suites](https://awesome-repositories.com/f/testing-quality-assurance/automation-interaction-tools/test-automation-tools/ai-regression-testing-suites.md) — Provides a suite for executing automated test cycles and validating model behavior against defined quality standards. - [LLM Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/llm-evaluation.md) — Uses secondary language models to evaluate and quantify the quality of outputs from primary models against predefined criteria. - [Automated Assertion Validators](https://awesome-repositories.com/f/testing-quality-assurance/validation-verification/input-validation/agent-input-and-output-validators/automated-assertion-validators.md) — Provides programmatic assertion-driven validation to ensure model outputs meet defined quality standards during development. ([source](https://deepeval.com/docs/introduction)) - [Agent Testing Suites](https://awesome-repositories.com/f/testing-quality-assurance/software-testing/e2e-integration-testing/end-to-end-testing/agent-testing-suites.md) — Validates the reliability and behavior of autonomous agents by simulating complex workflows and inspecting multi-step execution traces. - [Assertion and Validation Utilities](https://awesome-repositories.com/f/testing-quality-assurance/software-testing/testing-frameworks/test-frameworks/assertions-and-validation/assertion-validation-utilities.md) — Provides programmatic assertion utilities to verify model output quality against defined performance thresholds. ### Artificial Intelligence & ML - [LLM Observability](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-observability.md) — Traces and debugs complex multi-step agent execution workflows to identify performance bottlenecks and failures. - [Workflow Performance Scorers](https://awesome-repositories.com/f/artificial-intelligence-ml/performance-metrics/workflow-performance-scorers.md) — Quantifies the quality of AI outputs and agent workflows using automated scoring to ensure consistent performance. ([source](https://deepeval.com/docs/introduction)) - [CI/CD Regression Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/regression-analysis/performance-regression-analyzers/ci-cd-regression-analyzers.md) — Executes automated test suites within continuous integration environments to detect performance regressions before deployment. - [Evaluation Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management/evaluation-datasets.md) — Manages structured evaluation datasets to ensure consistent benchmarking across model versions and prompt iterations. - [Synthetic Data Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generation.md) — Generates synthetic datasets using language models to simulate edge cases and improve evaluation robustness. ([source](https://deepeval.com/docs/introduction)) - [Synthetic Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators.md) — Creates artificial test cases by leveraging language models to simulate diverse edge scenarios for robust system evaluation. - [Coding Agent Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/agent-and-tool-integrations/coding-agent-integrations.md) — Integrates evaluation tools into coding agents to automatically generate, run, and refine test cases during development. ([source](https://deepeval.com/docs/introduction)) ### Data & Databases - [Deployment Quality Gates](https://awesome-repositories.com/f/data-databases/change-detection-engines/deployment-regression-detectors/deployment-quality-gates.md) — Blocks software deployments automatically when model outputs fail to meet performance requirements during continuous integration. ([source](https://deepeval.com/docs/introduction)) - [Dataset Management Tools](https://awesome-repositories.com/f/data-databases/dataset-management-tools.md) — Manages evaluation test datasets to ensure consistent and repeatable performance benchmarking across prompts and model versions. ([source](https://deepeval.com/docs/introduction)) ### Security & Cryptography - [Adversarial Red Teaming Toolkits](https://awesome-repositories.com/f/security-cryptography/security/offensive-operations/vulnerability-research-analysis/analysis-discovery-tooling/adversarial-testing-resources/adversarial-red-teaming-toolkits.md) — Performs security and safety red teaming to identify vulnerabilities and harmful behaviors in language models before deployment. ([source](https://deepeval.com/docs/introduction)) ### System Administration & Monitoring - [Model Observability Suites](https://awesome-repositories.com/f/system-administration-monitoring/model-observability-suites.md) — Offers comprehensive observability suites for tracing, monitoring, and evaluating the quality of language model outputs. ([source](https://deepeval.com/docs/introduction)) - [Agent Execution Tracing](https://awesome-repositories.com/f/system-administration-monitoring/agent-execution-tracing.md) — Captures internal component interactions and tool calls to provide visibility into multi-step agent workflows. - [Execution Observability](https://awesome-repositories.com/f/system-administration-monitoring/execution-observability.md) — Captures internal model processes and execution history to debug complex agent workflows and identify performance bottlenecks. ([source](https://deepeval.com/docs/introduction)) - [AI and Agent Observability](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/ai-agent-observability.md) — Evaluates the effectiveness of complex retrieval pipelines and conversational agents by applying specialized metrics. ([source](https://deepeval.com/docs/introduction)) ### Development Tools & Productivity - [Automated Test Execution](https://awesome-repositories.com/f/development-tools-productivity/debugging-profiling-testing/test-execution-management/automated-test-execution.md) — Executes repeatable automated regression test suites to verify model behavior and identify performance drops before production deployment. ([source](https://deepeval.com/docs/introduction)) ### DevOps & Infrastructure - [CI/CD Pipeline Integrations](https://awesome-repositories.com/f/devops-infrastructure/ci-cd-pipeline-integrations.md) — Automates quality gates for AI applications to prevent performance regressions and security vulnerabilities from reaching production. ### Software Engineering & Architecture - [Workflow Debugging](https://awesome-repositories.com/f/software-engineering-architecture/workflow-debugging.md) — Analyzes internal execution steps and component interactions to troubleshoot failures within complex retrieval and conversational systems.