Trace multi-hop blast radius across code changes, prioritize tests under strict budgets, verify releases with bounded mutations, and empower AI agents via Model Context Protocol (MCP).
verify_token1 ModifiedAdjust runtime budgets and pick code targets to see how FalkorDB graph traversal and ML rankers prioritize regression suites in real time.
Simulate modifying a code symbol and inspect how FalkorDB graphs & ML rankers select tests
Hover over each pillar to inspect how FalkorDB AST graph traversal, learned test prioritization, and counterfactual mutations protect production releases.
Maps Python Abstract Syntax Trees into persistent Cypher graph vertices. Resolves precise causal chains from git diff line changes to downstream callers.
Ranks affected tests using graph centrality, historical flake probability, and execution duration. Shrinks full-suite runs by 90%+ within your CI budget.
Injects bounded in-memory AST mutations into modified symbols to verify that selected tests kill regressions before any code merges.
Run standalone in any terminal, integrate into GitHub Actions, or embed into custom workflows
Embed directly into custom Python CI pipelines or connect Cursor, Claude Desktop, and AI coding agents via stdio MCP
from rippleci import RippleProject
# 1. Open repository with FalkorDB graph discovery
project = RippleProject.open(".")
# 2. Trace multi-hop blast radius of current pull request
impact = project.analyzer.analyze(base="origin/main", head="HEAD")
# 3. Select ML-prioritized tests under a 180s runtime budget
plan = project.planner.plan(impact=impact, budget_seconds=180.0)
print(f"Selected {len(plan.selected_tests)} tests ({plan.estimated_total_seconds:.1f}s)")
for test in plan.selected_tests:
print(f" [{test.rank}] {test.name} (ML score: {test.rank_score:.2f})")Connect any GitHub repository with zero configuration or install the typed Python package to get started in under a minute.