If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/confident-ai/deepeval shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

GO — Healthy across all four use cases

• Last commit 1d ago
• 3 active contributors
• Apache-2.0 licensed
• CI configured
• Tests present
• ⚠ Small team — 3 contributors active in recent commits
• ⚠ Single-maintainer risk — top contributor 95% of recent commits

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live confident-ai/deepeval repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/confident-ai/deepeval.

What it runs against: a local clone of confident-ai/deepeval — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in confident-ai/deepeval | Confirms the artifact applies here, not a fork | | 2 | License is still Apache-2.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | Last commit ≤ 31 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
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#   cd deepeval
#
# Then paste this script. Every check is read-only — no mutations.

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fail=0
ok()   { echo "ok:   $1"; }
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if ! git rev-parse --git-dir >/dev/null 2>&1; then
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fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "confident-ai/deepeval(\\.git)?\\b" \\
  && ok "origin remote is confident-ai/deepeval" \\
  || miss "origin remote is not confident-ai/deepeval (artifact may be from a fork)"

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DeepEval is an open-source Python framework for evaluating LLM applications with a pytest-like API. It provides production-ready evaluation metrics (G-Eval, answer relevancy, task completion, hallucination detection) and integrates with benchmarks like ARC, BBQ, and Big-Bench-Hard to measure LLM system quality without requiring manual test data. Monolithic package structure: deepeval/ root contains domain modules (annotation/, anthropic/, benchmarks/) alongside a _version.py for version management. Benchmarks are heavily featured with subdirectories per benchmark type (arc/, bbq/, big_bench_hard/) each containing task definitions, templates, and mode configurations. Metrics likely exist in a metrics/ or test_metrics/ directory not shown in the top 60.

ML engineers and data scientists building LLM applications who need to systematically evaluate response quality, hallucination rates, and task completion. Also used by framework developers integrating LLM evaluation into CI/CD pipelines alongside unit tests.

Actively developed and production-ready. The repo contains comprehensive GitHub Actions workflows (full_test_core_for_pr.yml, test_integrations.yml, test_metrics.yml), pre-commit configuration, and structured package organization. Regular test coverage and changelog generation suggest active maintenance, though specific star/commit recency data isn't visible in the snapshot.

Low to moderate risk. Single-language Python codebase (~5.2M lines) reduces dependency sprawl but ties fate to Python ecosystem. The .pre-commit-config.yaml and structured CI suggest mature practices, though the snapshot doesn't reveal open issue backlog or recent breaking changes. Maintainer concentration risk exists given MAINTAINERS.md references a specific team.

Active development visible via .github/workflows/ with dedicated test runs for core features, integrations, and metrics. The .scripts/changelog/generate.py indicates structured release management. Integration support expanding (anthropic/ module suggests recent Claude/Anthropic support). Pre-commit hooks configured for code quality (black formatter in black.yml workflow).

git clone https://github.com/confident-ai/deepeval.git
cd deepeval
pip install -e .
cp .env.example .env
# Configure API keys in .env for LLM providers
python -m pytest tests/

Daily commands: No traditional 'dev server'—this is a testing framework. Run evaluations programmatically or via pytest CLI: python -m pytest your_eval_tests.py after configuring .env with LLM API keys. Individual metric tests: python -m pytest deepeval/tests/test_metrics.py.

• deepeval/benchmarks/base_benchmark.py: Abstract base class defining the interface all benchmarks (ARC, BBQ, BigBenchHard) must implement for standardized evaluation
• deepeval/annotation/annotation.py: Core annotation data structure for marking up evaluation results with metadata (score, reasoning, confidence)
• deepeval/annotation/api.py: External API layer enabling persistence and tracking of evaluation runs to Confident AI backend
• .github/workflows/test_metrics.yml: CI pipeline specifically for metric unit tests; entry point for understanding evaluation correctness guarantees
• deepeval/anthropic/patch.py: LLM provider integration pattern showing how to extend framework for new backends without modifying core
• deepeval/init.py: Public API surface; reveals which metrics and utilities are intended for end-user import

Adding a new metric: Create deepeval/metrics/your_metric.py following existing patterns in the metrics directory. Adding a benchmark: Mirror the structure of deepeval/benchmarks/arc/ (create __init__.py, main benchmark class, mode enum, template file). Extending LLM provider support: Add module under deepeval/ (e.g., deepeval/openai/) with patch.py for LLM-specific hooks, extractors.py for response parsing, and utils.py for helpers. API changes: Update deepeval/annotation/api.py and corresponding schema classes.

API key requirement: All metric evaluations require LLM provider credentials (OpenAI, Anthropic, etc.) in .env—missing keys will cause silent evaluation failures. Async context: Some metrics may require async/await support; check if your test runner supports it. Benchmark data licensing: BBQ and Big-Bench-Hard have specific attribution/licensing requirements for commercial use—verify in deepeval/benchmarks/[name]/. Version pinning: The .pre-commit-config.yaml suggests strict code formatting via Black; PRs may fail CI if not pre-formatted. Anthropic-specific: The deepeval/anthropic/ module likely has version constraints matching Anthropic's SDK—check for compatibility if extending.

• G-Eval (LLM-as-Judge) — DeepEval's flagship metric using an LLM to score outputs instead of heuristics; understanding this paradigm shift is essential to grasping why DeepEval exists
• Chain-of-Thought (CoT) Prompting — Used internally by metrics like G-Eval and benchmarks like Big-Bench-Hard (evidenced by deepeval/benchmarks/big_bench_hard/cot_prompts/); CoT reasoning improves LLM evaluation quality
• Hallucination Detection — One of DeepEval's core metrics; distinguishing factual LLM outputs from confabulated ones is critical for production LLM systems
• Benchmark Standardization (ARC, BBQ, Big-Bench) — DeepEval integrates canonical evaluation datasets to measure LLM capability on standardized tasks; understanding each benchmark's scope (commonsense reasoning, fairness, reasoning) informs metric selection
• Provider Abstraction Pattern (Anthropic as example) — The deepeval/anthropic/ module shows how to plug in new LLM backends via patch.py and extractors.py without modifying core; essential for maintaining framework flexibility
• Structured Annotation/Metadata for ML Systems — DeepEval's annotation/ module provides a schema for labeling evaluation results; this pattern enables downstream filtering, analytics, and regression detection in production LLM pipelines
• Answer Relevancy and Task Completion Metrics — Two of DeepEval's core metrics measuring different dimensions of LLM output quality; knowing when to apply each (relevancy for retrieval, completion for structured tasks) is critical for effective evaluation design

• openai/evals — OpenAI's evaluation framework; direct competitor for LLM app testing but more OpenAI-centric, whereas DeepEval is multi-backend
• langchain-ai/langchain — LLM orchestration framework with lightweight eval hooks; many DeepEval users integrate evals into LangChain chains via the annotation API
• ragas-ai/ragas — RAG-specific evaluation framework sharing similar metric philosophy (G-Eval, relevancy scoring); complementary to DeepEval for retrieval-augmented LLM systems
• anthropics/anthropic-sdk-python — Official Anthropic Python SDK that DeepEval wraps via deepeval/anthropic/patch.py for Claude model evaluation
• confident-ai/confident — Likely the backend SaaS product referenced in deepeval/annotation/api.py; stores and visualizes evaluation results from local DeepEval runs

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add comprehensive unit tests for deepeval/annotation module

The annotation module (deepeval/annotation/annotation.py and api.py) lacks visible test coverage in the file structure. Given that annotation is a core feature for LLM evaluation, adding unit tests would improve reliability and serve as documentation for the API. This aligns with the existing test workflow structure (.github/workflows/test_core.yml) and the project's use of pytest.

• [ ] Create tests/annotation/test_annotation.py for annotation.py core functionality
• [ ] Create tests/annotation/test_api.py for annotation API endpoints
• [ ] Add test fixtures for common annotation scenarios (e.g., creating annotations, querying)
• [ ] Run against test_core.yml workflow to ensure integration
• [ ] Document test coverage in CONTRIBUTING.md with examples

Implement integration tests for deepeval/anthropic module with mocking

The anthropic integration (deepeval/anthropic/ with patch.py, extractors.py, utils.py) currently has test_integrations.yml workflow but no visible test files. Anthropic integration is a key feature that deserves dedicated integration tests with proper mocking to avoid API costs during testing.

• [ ] Create tests/integrations/anthropic/test_extractors.py with mocked Anthropic API calls
• [ ] Create tests/integrations/anthropic/test_patch.py to verify patching behavior
• [ ] Add fixtures in tests/integrations/anthropic/conftest.py for reusable Anthropic mock objects
• [ ] Verify tests run in test_integrations.yml without requiring real API keys
• [ ] Document how to extend Anthropic integration tests in CONTRIBUTING.md

Add benchmark evaluation tests and documentation for deepeval/benchmarks module

The benchmarks directory contains multiple benchmark implementations (ARC, BBQ, BigBenchHard) with templates and modes, but no visible test files for validating benchmark loading, task parsing, and evaluation. Adding tests would ensure benchmarks work correctly and provide examples for users implementing custom benchmarks.

• [ ] Create tests/benchmarks/test_arc.py with tests for ARC benchmark initialization and task loading
• [ ] Create tests/benchmarks/test_bbq.py for BBQ task parsing and validation
• [ ] Create tests/benchmarks/test_big_bench_hard.py for BigBenchHard benchmark with CoT prompt verification
• [ ] Add parametrized tests to verify all benchmark modes work correctly
• [ ] Create docs/benchmarks_testing_guide.md documenting how to test custom benchmarks

• Add unit tests for deepeval/anthropic/utils.py—the file structure suggests utilities exist but test coverage via test_integrations.yml may be incomplete; write extraction/formatting tests for Claude-specific response handling
• Document the benchmark extension pattern by creating a BENCHMARK_CREATION.md in docs/ with a step-by-step example; the arc/, bbq/, and big_bench_hard/ directories follow a pattern but no guide exists for contributors adding new benchmarks
• Implement missing metric type hints and docstrings in core metrics—scan deepeval/metrics/ (inferred missing from file list) and add comprehensive type hints and examples following Google/NumPy docstring format for IDE autocomplete support

The DeepEval LLM Evaluation Framework shows moderate security posture with some concerns. Primary risks include: (1

• High · Multiple API Keys Exposed in .env.example — .env.example. The .env.example file contains templates for sensitive API keys including OPENAI_API_KEY, AZURE_OPENAI_API_KEY, GOOGLE_API_KEY, GROK_API_KEY, MOONSHOT_API_KEY, DEEPSEEK_API_KEY, and LITELLM_API_KEY. While example files are typically not secrets themselves, this demonstrates the application handles multiple credential types and developers may accidentally commit actual credentials if not careful. Fix: Ensure .env and .env.local are in .gitignore (verify in .gitignore file). Add pre-commit hooks to prevent accidental secrets commits. Use secret scanning tools like git-secrets or TruffleHog in CI/CD pipeline. Document secure credential management practices in CONTRIBUTING.md.
• Medium · Incomplete .env.example File — .env.example. The .env.example file appears truncated (ends with 'LITELLM_API_KE' without closing), which could lead to incomplete configuration documentation and potential runtime issues if developers rely on this as a template. Fix: Complete the .env.example file with all required configuration variables and their proper documentation. Include defaults where appropriate and add comments explaining each variable's purpose.
• Medium · Potential Dependency Vulnerability - Missing Requirements Analysis — deepeval/ (root package dependencies). The dependency/package file content was not provided in the security context. The codebase likely has dependencies (setup.py, requirements.txt, pyproject.toml) that should be analyzed for known vulnerabilities. Python packages from multiple LLM providers (OpenAI, Azure, Google, etc.) are referenced but not validated. Fix: Run 'pip-audit' or 'safety' on all dependencies. Use GitHub Dependabot to track dependency vulnerabilities. Pin exact versions where possible. Regularly update dependencies and review security advisories for LLM SDK packages.
• Medium · Multiple LLM Provider Integrations Without Visible Input Validation — deepeval/anthropic/, deepeval/annotation/. The codebase integrates with multiple LLM providers (OpenAI, Azure, Google, Anthropic, xAI, etc.) through extractors and patches. Without visible input validation in the file structure, there's potential risk of prompt injection attacks or improper handling of untrusted LLM outputs. Fix: Implement robust input validation for all LLM prompts and outputs. Sanitize and validate API responses before processing. Use prompt injection detection libraries. Add output validation for all LLM API responses. Review extractors (deepeval/anthropic/extractors.py) for proper error handling.
• Medium · Potential Data Exposure in Benchmark/Annotation Processing — deepeval/benchmarks/, deepeval/annotation/. The codebase contains benchmark datasets (ARC, BBQ, BigBench) and annotation processing modules. Without visible access controls or data encryption patterns in the file structure, sensitive evaluation data or annotated results could be exposed. Fix: Implement data encryption for sensitive benchmark data at rest. Add access controls for annotation data. Use secure temporary file handling. Implement data retention policies. Audit data flow in annotation/api.py and benchmark modules.
• Low · GitHub Workflow Files Not Reviewed for Secrets — .github/workflows/. GitHub Actions workflows are present (.github/workflows/) but were not analyzed. These files could potentially expose secrets through environment variables or artifact uploads. Fix: Audit all workflow files for hardcoded secrets or insecure secret handling. Use GitHub encrypted secrets for all sensitive data. Review artifact retention policies. Ensure workflows validate and sanitize any external inputs (pull requests, comments).
• Low · No Visible Security Headers or CORS Configuration — deepeval/ (potential API components). No apparent web server configuration files (nginx.conf, app configuration) visible in the structure. If this framework includes any web API components, security headers and CORS policies may not be properly configured. Fix: If exposing web APIs, implement security headers (Content-Security-Policy, X-Frame-Options, etc.). Configure CORS restrictively. Implement rate limiting and request validation. Use HTTPS only.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.