The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the OrangePro listing page.
Find the behaviors your tests miss. Generate grounded tests that actually run.
OrangePro maps every public behavior in your codebase, scores each one by real test evidence, and shows you the structural blind spots before your users find them. Runs locally. Your code never leaves your machine.
One command produces an interactive HTML report:
The report has two modes: Simple (integration-level blind spots, plain English) and Expert (full behavior list, evidence tiers, flows, system map). Toggle with the pill switch at the top.
→ Live example: Twenty CRM (5,237 behaviors mapped)
System map — entry lanes (GraphQL, HTTP, Jobs) flowing into services, sized by traffic, colored by evidence tier, red-ringed by risk.
Priority gaps of another open source Project HONO — top 20 unproven behaviors ranked by blast radius, with generated test drafts.
Every behavior gets exactly one tier. Nothing is labeled "tested" on faith.
| Tier | Color | What it means |
|---|---|---|
| Dynamically Proven | 🟢 | A real test kills a targeted mutation of this behavior |
| Runtime-covered | 🟢 | Coverage tool executed this code |
| Statically Linked | 🟡 | A test imports and calls this code — structural link, not proof |
| Unconfirmed Candidate | ⚪ | A similar test file exists — a lead, not evidence |
| No Signal | 🔴 | Nothing tests this behavior |
"Dynamically Proven 0" is normal on first run. Proof requires running tests against targeted mutations. That's the trust model.
No API key needed. The report shows your system map, evidence tiers, priority gaps, and delta since last run.
Want test generation? Add a model key (BYOK):
AI output never changes evidence tiers. Only the mutation-kill oracle can mint Dynamically Proven.
Output:
Each rerun shows a delta banner: what entered the codebase, what moved up in risk, what got resolved.
OrangePro runs as an MCP server. Add to your client's config:
| Client | Where to put it |
|---|---|
| Claude Code | .mcp.json or ~/.claude.json |
| Cursor | ~/.cursor/mcp.json or Settings → MCP |
| VS Code / Copilot | MCP settings |
| Codex / OpenCode | Run npx -y @orangepro/mcp-server@latest agent --client codex |
The workflow: Tell your agent:
"Use
orangepro_start, thenorangepro_generate_testswith base_ref=main. Write each test to its suggested_path, run it, and report pass/fail."
The agent writes the test, runs it, calls orangepro_prove, and the behavior turns Dynamically Proven. One prompt, full loop.
Claude Code · Cursor · GitHub Copilot · Codex · Windsurf · OpenCode · VS Code
Any MCP-compatible agent can drive OrangePro. No vendor lock-in.
| Phase | What happens | Needs a model key? |
|---|---|---|
| Analyze | AST walk → behaviors, flows, evidence tiers | No |
| Score | Graph readiness score (0–100) | No |
| Generate | Grounded tests for top gaps | Yes (BYOK) |
| Prove | Mutation-kill oracle confirms test breaks if behavior changes | No |
Same code = same score. Deterministic. Always.
| Language | Static mapping | Generated tests | Dynamic proof |
|---|---|---|---|
| TypeScript / JavaScript | ✓ | ✓ Jest / Vitest / Mocha | ✓ |
| Python | ✓ | ✓ pytest | ✓ |
| Go | ✓ | ✓ *_test.go | ✓ |
| Java | ✓ | ✓ JUnit 4/5 | ✓ |
| Kotlin, Rust, PHP, C#, Ruby, Swift, C, C++ | ✓ | planned | planned |
Static mapping works across many languages via tree-sitter. Dynamic proof is deliberately narrower — each language needs a runner, mutation locator, and sandbox profile.
Use the repository's own setup and test commands first, and keep unit and integration
coverage in separate artifacts. Then run opro start; it performs analysis, ingests
the artifacts, attempts targeted proof, generates report-visible drafts, and writes the
final report. A separate opro analyze is unnecessary when opro start follows it.
Without this manifest, OrangePro conservatively infers clear unit/integration names
and labels everything else unclassified; it never guesses that an aggregate profile is
unit-only. The report shows unit, integration, their overlap, unclassified coverage, and
the combined union separately. --proof-limit controls dynamic proof attempts (which
may draft a test for proof); --generate-limit independently controls the additional
report-visible risk-gap drafting lane. A generation run
also records its terminal status and exact reason, so a compiler/import failure is not
misreported as a generic dependency problem.
Add --json to any read command for machine output. Run opro help for the full reference.
| Tool | What it does |
|---|---|
orangepro_start | One-command setup: analyze + report + next actions |
orangepro_analyze_sources | Build/refresh the evidence graph |
orangepro_generate_tests | Generate grounded tests for gaps |
orangepro_prove | Run mutation-kill oracle on a behavior |
orangepro_prove_loop | Setup + dynamic proof + report refresh for one behavior |
orangepro_find_test_gaps | List behaviors with weak/missing tests, ranked by risk |
orangepro_graph_score | Graph readiness score (0–100) |
orangepro_status | Workspace state without generating anything |
orangepro_doctor | Recommend next evidence to improve quality |
orangepro_rtm | Requirements traceability matrix |
orangepro_stats | Aggregate statistics |
orangepro_changed_impact | What a diff touches (requires git + base ref) |
orangepro_record_run | Record a test run result |
orangepro_explain_test | Explain why a test was generated |
orangepro_export_evidence_pack | Export metadata-only evidence pack |
orangepro_update_graph | Incremental graph update |
orangepro_ai_links | Weak behavior→symbol suggestions (optional AI) |
orangepro_ai_flows | Candidate flow discovery (optional AI) |
Each generated test includes:
If dependencies aren't installed, tests are kept as Manual tests (Given/When/Then steps with the blocker named). Install dependencies and re-run to convert them to runnable tests.
Generation is evidence-gated. A category is produced only when the graph has supporting evidence.
| Category | What it targets |
|---|---|
| Happy path | Primary expected behavior |
| Validation error | Bad/invalid input handling |
| Edge case | Boundaries, empty/null, concurrency, retries |
| Integration flow | Multi-step behavior across services |
| Security / privacy | Auth, injection, data leakage |
| Regression | Pinning a previously-broken behavior |
Analysis, scoring, and proof need no model key. Generation does.
| Provider | Environment variable |
|---|---|
| OpenAI-compatible | OPENAI_API_KEY (optional: OPENAI_BASE_URL, OPENAI_MODEL) |
| Anthropic | ANTHROPIC_API_KEY (optional: ANTHROPIC_MODEL) |
| Ollama (local, no key) | OLLAMA_BASE_URL (optional: OLLAMA_MODEL) |
Auto-detect order: OpenAI → Ollama → Anthropic. Override with --provider and --model.
The defaults are gpt-5.3-codex for OpenAI and claude-sonnet-5 for Anthropic.
Run opro setup to configure interactively. Keys stay in your environment — never written to graph, config, or artifacts.
With a provider key, OrangePro stages weak AI behavior→symbol links and AI-suggested candidate flows. These are review/generation worklists, not evidence:
AI-linked suggestions.Use them when you want the agent to find likely service-boundary flows faster; ignore them for a deterministic-only report.
This repo is the free local tool. The OrangePro platform adds:
PRs welcome. Please open an issue first for large changes.
MIT License · orangepro.ai