graphora-kg 0.2.0

Deterministic code knowledge graph: index with tree-sitter, query blast radius, mine risk memory from git, review diffs grounded in structure. No LLM required to build.

Graphora

The code knowledge graph that reviews your changes.

Deterministic indexing · Blast-radius analysis · Risk memory · Zero Docker required · $0 LLM cost to build

pip install it as a library · run it as a CLI · plug it into any AI agent as an MCP server · or install the skill into 22 agents with one command

---

TL;DR: Graphora keeps a live structural graph of your codebase, built with tree-sitter and zero LLM calls, stored in FalkorDB or in a zero-setup embedded backend. When code changes, it reads the real blast radius (callers, callees, tests, importers) straight from the graph, remembers where the codebase broke before (risk memory mined from git history), and grounds every review claim in tagged, auditable facts. Measured result: 82 to 94% fewer prompt tokens than repo dumping on real pinned OSS repos, with more accurate reviews.

Why Graphora

AI code review and AI coding agents share the same blind spot: they see text, not structure.

Problem	What goes wrong
Blind to blast radius	A one-line change to a shared function looks harmless in isolation. Its callers, tests, and dependents live in other files, outside the diff.
Expensive to compensate	The usual fix is stuffing more of the repo into the prompt. That burns tokens and still buries the signal in noise.
Hard to trust	Without grounding, the model guesses. Reviewers cannot tell a grounded claim from a hallucination.
No memory of pain	Every review starts from zero. The tool does not know that this exact function was hotfixed twice and reverted once.

Graphora fixes all four at the source:

Capability	How
Live structural graph	File, Function, Class, Module nodes; CALLS, DEFINED_IN, IMPORTS edges. Built deterministically with tree-sitter, updated incrementally.
Blast radius, not repo dumps	Pure Cypher queries return exactly the callers, callees, covering tests, and importers of a change. Hundreds of tokens instead of tens of thousands.
Confidence tags on every fact	EXTRACTED (explicit in source), INFERRED (single-candidate name resolution), AMBIGUOUS (multiple candidates, all linked and flagged). You always know what was read versus guessed.
Risk memory	Git history mining links fix, hotfix, and revert commits to the exact symbols they touched. Each symbol carries fix_count, last_broke_at, and a risk_score that decays as the area stays quiet. Reviews warn on historically fragile code automatically.
Zero-setup or client-server	Works instantly with an embedded pure-Python backend (JSON on disk, no Docker), or against a shared FalkorDB server for live Cypher and multi-project teams. Auto-detected.
11 languages	Python, JavaScript, TypeScript (+JSX/TSX), Go, Java, Rust, C, C++, Ruby, PHP. Tree-sitter first, regex fallback.
$0 index cost	No LLM, no embeddings, no network in the index path. Code never leaves your machine unless you enable the optional LLM review pass.

Quick start

# 1. Install (no server, no Docker needed)
pip install graphora-kg     # from PyPI, or: pip install -e . inside this repo

# 2. Build the graph and the risk memory (both offline, both $0)
graphora index /path/to/repo
graphora risk mine /path/to/repo

# 3. Use it
graphora blast my_function                 # who calls it, who tests it, did it break before
graphora risk top                          # the riskiest symbols in the codebase
graphora review --git-range main...HEAD --repo /path/to/repo
graphora benchmark /path/to/repo           # reproduce the token-cost numbers yourself

Zero configuration: without a FalkorDB server Graphora uses its embedded backend (pure Python, JSON at ~/.graphora). For live Cypher queries and team-shared graphs, start FalkorDB and Graphora picks it up automatically:

docker run -d --name graphora-falkordb -p 6379:6379 falkordb/falkordb:latest
# force a backend anytime with --backend embedded|falkordb|auto

Four ways to consume it

1. CLI

graphora index PATH                 Build the graph for a repository (no LLM)
graphora blast SYMBOL [...]         Blast radius: callers, callees, tests, risk
graphora review [--diff F|--git-range R]   Grounded diff review (LLM optional via --llm)
graphora risk mine PATH             Mine git fix/revert history into the graph
graphora risk top                   Riskiest symbols, ranked
graphora benchmark PATH             Reproducible token-cost benchmark
graphora serve-mcp                  Serve the graph to AI agents over MCP
graphora install-skill [all|AGENT]  Install the Graphora skill for 22 AI coding agents
graphora stats                      Node and edge counts

Every graph command accepts --backend auto|falkordb|embedded (default: auto).

2. Python library

from graphora import GraphStore, index_repository, blast_radius
from graphora.blast import blast_radius_for_diff
from graphora.risk import mine_risk_memory, risk_report

store = index_repository("/path/to/repo")          # deterministic build
mine_risk_memory(store, "/path/to/repo")           # teach it the history

# CI gate: block changes to called-but-untested symbols, three lines
radius = blast_radius_for_diff(store, pr_diff)
untested = [s.name for s in radius.symbols if s.callers and not s.tests]
assert not untested, f"Changed symbols with callers but no tests: {untested}"

3. MCP server (for AI agents)

graphora serve-mcp --project myrepo

Exposes blast_radius, review_diff, risk_top, find_symbol, and graph_stats to Copilot CLI, Claude Code, Cursor, or any MCP client. The server instructs agents to check the blast radius before editing a symbol, which turns Graphora into a guardrail for AI-generated changes.

4. One-command skill install (22 agents)

graphora install-skill all          # or: graphora install-skill claude-code cursor copilot
graphora install-skill --list      # see all supported agents

Writes the Graphora workflow rule (check blast radius before editing, run a grounded review before committing, consult risk memory) into each agent's project config: .claude/skills/, .cursor/rules/, .github/instructions/, AGENTS.md, GEMINI.md, Windsurf, Cline, Roo, Continue, Amazon Q, Zed, Goose, Aider, and more. Idempotent; existing files are preserved, shared files get a marked block.

Risk memory: the graph learns where the codebase breaks

Graphora mines git history (deterministically, zero LLM) for fix, hotfix, bugfix, and revert commits, and attributes each one to the exact functions and classes it touched, using both diff lines and hunk-header context.

risk_score = (1 - 0.7^fix_count) * 0.5^(months_since_last_fix / 6)

A symbol fixed often and recently scores near 1.0. A quiet area decays by half every 6 months. Reviews then carry findings no stateless tool can produce:

risk-memory falkordb/asyncio/cluster.py:9: Is_Cluster was involved in 2 past fix/revert commits (last: 2025-03-13), risk score 0.08, 2 callers. Review extra carefully.

The longer Graphora runs on a repository, the smarter it gets. That compounds.

Benchmarks

Measured on real repositories, fully deterministic, zero network. Full methodology and reproduction commands in BENCHMARKS.md.

Public benchmark on pinned OSS commits, reproducible with python3 scripts/public_benchmark.py:

Repository	Commit	Whole-repo dump	Relevant files	Graphora blast radius	Savings
flask	36e4a824f3	147,390 tokens	124,205 tokens	21,534 tokens	85.4%
requests	f361ead047	101,739 tokens	80,360 tokens	10,331 tokens	89.8%
click	b67832c216	225,373 tokens	190,236 tokens	39,534 tokens	82.5%
falkordb-py	971f1d124e	59,866 tokens	40,638 tokens	3,550 tokens	94.1%

Graph build cost in LLM credits: $0, by construction. The "relevant files" column is the honest baseline: every file a reviewer would have to read to learn what the blast radius states directly.

Use cases

Five real, captured scenarios in USECASES.md:

1. Cross-file impact review with zero LLM: a one-line diff, callers and covering tests read straight from the graph, ~197 tokens of context.
2. Risk hotspot mining on a real codebase: 231 commits of falkordb-py history, the async cluster code correctly surfaced as the trouble spot.
3. AI agents over MCP: a live stdio session calling blast_radius, risk_top, find_symbol.
4. A CI gate in three lines of library code, plus incremental re-indexing.
5. Ambiguity honesty: duplicate symbol names tagged AMBIGUOUS instead of silently guessed.

Architecture

                        ┌────────────────────────────────────────────┐
   source files ───────▶│  tree-sitter parse (deterministic, $0)     │
   git history  ───────▶│  risk miner (fix/revert attribution, $0)   │
                        └───────────────────┬────────────────────────┘
                                            ▼
                       code graph (FalkorDB or embedded)
                 File · Function · Class · Module · FixCommit
              CALLS / DEFINED_IN / IMPORTS / FIXED  (confidence-tagged)
                                            │
              ┌──────────────┬──────────────┼───────────────┬─────────────┐
              ▼              ▼              ▼               ▼             ▼
           CLI           library API     MCP server     benchmark    skill installer
        (terminal)      (CI gates,      (AI agents)    (reproducible)  (22 agents)
                         refactors)

Graph model per project: graphora:{project} in FalkorDB, or ~/.graphora/{project}.json embedded. Incremental updates re-parse only changed files. The optional LLM review pass (litellm, any provider) receives the diff plus the compact blast radius, never the repository.

Enterprise notes

• Privacy: the index path is fully local. No code, no metadata leaves the machine unless the optional --llm review is enabled, and then only the diff plus a few hundred tokens of graph facts are sent to your configured provider.
• Determinism: identical input produces identical graphs, reviews, and benchmark numbers. No embeddings drift, no model version drift in the core.
• Multi-project: one FalkorDB instance serves many project graphs (graphora:{project}), so a single shared server can back a whole team. Solo developers need no server at all (embedded backend).
• Auditability: every review claim traces to a tagged graph fact; every risk score traces to specific commits (FixCommit nodes with sha, date, subject).
• Incremental cost: re-index only changed files on push; risk mining is idempotent per commit.

Testing

80 tests, all passing: parser (11 languages), FalkorDB store, embedded store (including backend-parity tests), blast radius, risk memory, CLI, MCP server, skill installer, and benchmarks. Integration tests run against a live FalkorDB and real scripted git repositories, and skip cleanly when FalkorDB is absent.

python3 -m pytest tests -q        # 80 passed

The test-per-phase methodology and the bugs the suite caught are documented in USECASES.md.

Requirements

Requirement	Version	Notes
Python	3.10+
FalkorDB	optional	for the client-server backend; embedded backend needs nothing
git	any	for risk memory mining
litellm	optional	only for review --llm
mcp	optional	only for serve-mcp

Languages indexed today: Python, JavaScript, TypeScript, Go, Java, Rust, C, C++, Ruby, PHP (tree-sitter grammars, regex fallback).

License

MIT