cline/evals

Cline Evaluation Framework

A layered testing system for measuring Cline's performance at different levels.

Directory Structure

evals/
├── smoke-tests/           # Quick provider validation (minutes)
│   ├── run-smoke-tests.ts
│   └── scenarios/         # 5 curated test scenarios
│
├── e2e/                   # Full E2E with cline-bench (hours)
│   └── run-cline-bench.ts
│
├── cline-bench/           # Real-world tasks (git submodule)
│   └── tasks/             # 12 production bug fixes
│
├── analysis/              # Metrics and reporting framework
│   ├── src/
│   │   ├── metrics.ts     # pass@k, pass^k calculations
│   │   ├── classifier.ts  # Failure pattern matching
│   │   └── reporters/     # Markdown, JSON output
│   └── patterns/
│       └── cline-failures.yaml
│
└── baselines/             # Performance baselines for regression detection

Test Layers

Layer 1: Contract Tests (Unit)

Location: src/core/api/transform/__tests__/

Tests API transform logic without LLM calls:

• Thinking trace preservation
• Tool call parsing (XML, native formats)

• Provider format conversions

  npm run test:unit -- --grep "Thinking\|Tool Call"
  

Layer 2: Smoke Tests (Minutes)

Location: evals/smoke-tests/

Quick validation across providers with real LLM calls:

• 5 curated scenarios
• 3 trials per test for pass@k metrics

• Runs via cline CLI with -s flags

  # Set API key (Cline provider)
  export CLINE_API_KEY=sk-...
  
  # Run smoke tests
  npm run eval:smoke
  
  # Run specific scenario
  npm run eval:smoke -- --scenario 01-create-file
  
  # Run with specific model (overrides per-scenario models)
  npm run eval:smoke -- --model anthropic/claude-sonnet-4.5
  

Layer 3: E2E Tests (Hours)

Location: evals/e2e/ + evals/cline-bench/

Full agent tests on production-grade tasks:

• 12 real-world coding problems
• Docker/Daytona execution via Harbor

• Nightly CI runs

  # Prerequisites: Python 3.13, Harbor, Docker
  npm run eval:e2e
  
  # Specific task
  npm run eval:e2e -- --tasks discord
  
  # Different provider
  npm run eval:e2e -- --provider openai --model gpt-4o
  

Metrics

The framework calculates:

Metric	Formula	Interpretation
pass@k	P(≥1 of k passes)	Solution finding capability
pass^k	P(all k pass)	Reliability
Flakiness	Entropy of pass rate	Consistency

With 3 trials:

• All pass → pass (reliable)
• All fail → fail (broken)
• Mixed → flaky (needs investigation)

CI Integration

• PR Gate: Contract tests + smoke tests (fast, ~3min)
• Nightly: E2E tests with cline-bench (not yet implemented, see TODO)

Quick Start

# Run all fast tests
npm run test:unit
npm run eval:smoke

# Run E2E (requires setup)
cd evals/cline-bench
# Follow README.md for Harbor setup
npm run eval:e2e

Adding Tests

Smoke Test Scenario

1. Create evals/smoke-tests/scenarios/<name>/config.json
2. Add optional template/ directory with starting files
3. Run to verify: npm run eval:smoke -- --scenario <name>

Contract Test

1. Add to src/core/api/transform/__tests__/
2. Run: npm run test:unit -- --grep "YourTest"

E2E Task

Contribute to cline/cline-bench

Resources

• cline-bench tasks
• Smoke test scenarios

TODO

• Nightly E2E CI: Add scheduled workflow for cline-bench tests
  • Requires: Docker runner, Harbor setup, ~1-2 hour timeout
  • Should run on schedule (e.g., nightly) not per-PR
  • Separate secrets for E2E environment
• Native tool calling smoke tests: Add CLI support for native_tool_call_enabled setting to test Claude 4 with native tools