v0.32 · Phantom computer skills · Free forever

Give your AI a brain that understands your code.

Claude, Cursor, Copilot — they read files. Graqle gives them a knowledge graph of your entire architecture. 500 tokens instead of 50,000. Real answers instead of guesses.

Autonomous fix-test-fix loops. Visual graph traversal. Real-time streaming.

GraQle transforms scattered files into an architecture-aware knowledge graph — 8 agents, 500 tokens, 92% confidence
terminal
$ pip install graqle && graq init
✓ 847 files scanned. 312 nodes. 178 edges. Ready.
$ graq reason "what breaks if I change auth?"
3 services impacted · 94% confidence · $0.0003

10 seconds to install · No account needed · Runs on your machine

100x
fewer tokens per query
14
LLM backends supported
2,009
tests passing
10s
to install
Works withClaude CodeCursorVS Code CopilotWindsurfJetBrainsCodex

How it works

Install. Ask. Ship.

No configuration files. No onboarding calls. No cloud accounts. Graqle understands your codebase automatically and starts answering questions immediately.

Inside a GraQle node — three reasoning layers: Memory Context (graph traversal, $0), Embedding (1 vector call), LLM Reasoning (500 tokens, 92% confidence)
01

One command. Your AI levels up.

No config files. No cloud accounts. No setup meetings. Run graq init and your AI assistant instantly understands your entire architecture — every service, every dependency, every connection. It takes 10 seconds.

terminal
$ pip install graqle && graq init

✓ Scanned 847 files in 4.2s
✓ Built graph: 312 nodes, 178 edges
✓ MCP tools wired into Claude/Cursor
✓ Your AI is now architecture-aware
02

Ask anything. Get real answers.

Your AI stops guessing. Instead of reading 60 files and hoping for the best, it queries a knowledge graph that actually knows the relationships. 500 tokens instead of 50,000. Precise answers with confidence scores.

terminal
$ graq reason "what breaks if I change auth?"

3 services impacted:
  → billing-api (JWT validation)
  → notifications (user context)
  → user-api (direct dependency)

Confidence: 94% · 500 tokens · $0.0003
03

It gets smarter every day.

Every query teaches it. Every correction sticks. Every decision is remembered. This isn't a static analysis tool — it's a self-learning knowledge graph that compounds value the longer you use it. Your AI assistant evolves with your code.

terminal
$ graq learn "Payments migrated to Stripe"
$ graq compile

✓ Graph: 314 nodes, 182 edges
✓ Intelligence compiled: 135 insights
✓ CLAUDE.md updated automatically
✓ Your AI just got smarter

The vibe coding wall

Your AI is fast. But is it right?

Vibe coding is incredible — until your AI breaks something it didn't understand. It reads files one at a time, has no idea what connects to what, and burns 50K tokens to give you a guess. The answer isn't a smarter model. It's giving your model the right context.

Today — slow, expensive, unreliable
$ grep -r "auth" --include="*.py" -l
47 files containing "auth"
$ cat src/services/*.py src/middleware/*.py ...
Reading 60 files into context...
# 20 minutes later
"I think billing depends on auth? Maybe?"
20 min50K tokens$0.15/query
With Graqle — instant, precise, transparent
$ graq reason "what depends on auth?"
Activated: auth-service, jwt-middleware, user-api
3 nodes, 500 tokens, 4.8 seconds
Answer: billing-api and notifications depend on auth via JWT validation. user-api calls auth directly.
Confidence: 87% · Governance: PASS
5 sec500 tokens$0.0003/query
graq_gate — governance in action
GraQle governance gate flagging a HIGH risk CSS change — requires CEO approval before code generation

Real output: GraQle's governance gate flagged a CSS change as HIGH risk — cascading to 200+ components. Required explicit CEO approval before generating the diff.

Use Cases

Built for real workflows

See how teams use GraQle across the development lifecycle — from PR review to compliance gates to knowledge preservation.

AI Teams

Catch AI Bugs Before They Merge

AI coding tools write fast. They also miss context they've never seen.

GraQle runs graq_review and graq_impact on every pull request — whether the author is Claude Code, Copilot, Cursor, or a human. It traces blast radius across the full dependency graph before a single line merges. You get a confidence score, a list of affected nodes, and a verdict: ship or block.

In a live session: GraQle found 3 bugs Claude Code missed, returned 96% confidence, activated 15 nodes, 14 agents — zero contradictions.
Teams

Day-One Context for Any Codebase

New engineers spend weeks reading code they could understand in minutes.

graq_context(deep) activates the relevant subgraph for any module, feature, or question and returns senior-level context in under 500 tokens. Architecture decisions, past mistakes, dependency chains, and risk nodes — surfaced in the first query. No more reading 15 files to understand one function.

11,773 KG nodes on a mid-size Python SDK + Next.js project. Full graph builds in ~10 seconds.
Enterprise

One Brain Coordinating Multiple AI Agents

Running Claude Code, Copilot, and Cursor simultaneously creates conflicting changes nobody reviews.

GraQle acts as the single coordination layer across all AI tools simultaneously. Every agent submits to graq_preflight before writing and graq_review before merging. The KG is the shared source of truth — so three AI tools working in parallel see the same architecture, the same constraints, and the same history.

30+ agents per review, 0 contradictions reported across all reasoning traces.
Compliance

Compliance Gate That Never Sleeps

Manual IP scans and secrets reviews don't scale when AI is writing hundreds of commits per day.

graq_gate runs in CI as a binary exit 0/1. It checks for secrets, scans for patent-adjacent logic, validates governance constraints, and uploads SARIF to GitHub Advanced Security. Your pipeline passes or it doesn't — no ambiguity, no manual review queue.

Binary gate output. SARIF upload. SOC2 and ISO 27001 audit trail generated per run.
Enterprise

Senior Devs Leave. Knowledge Stays.

Every departing senior engineer takes years of undocumented architecture decisions with them.

Every decision, every lesson, every mistake that GraQle processes gets written to the KG. When a senior dev leaves, the graph stays. When a new hire joins, graq_context gives them instant access to the reasoning that produced the current architecture — not just the code, but the why behind it.

graq_learn writes to KG and .gsm documentation simultaneously — dual-sink knowledge capture on every session.
Enterprise

Governance Layer for Contractor Code

Offshore contractors and third-party vendors commit code your senior engineers never review.

Add graq_preflight and graq_review to your CI pipeline. Every contractor PR gets the same governance as internal code — blast radius analysis, IP scan, secrets check, confidence score. The gate runs automatically. Your senior engineers review exceptions, not every PR.

CI-enforced. No bypass path. Confidence threshold configurable per repo.
Individual

KG Intelligence Inline in Your IDE

Context switches between your editor and your AI tool kill flow state.

The GraQle VS Code extension surfaces KG provenance directly in your editor. Inline completions carry confidence scores. The chat panel answers questions with graph-backed reasoning. The status bar shows live KG health. You write code with architecture awareness built in — no tab switching, no manual lookups.

KG provenance on every suggestion. Architecture-aware completions without leaving the editor.
Teams

One Graph Across Your Entire Stack

Your backend team and your frontend team are working in the same codebase but with no shared context.

GraQle builds a single KG that spans Python, TypeScript, Go, or any combination. A change to a FastAPI endpoint surfaces in the Next.js component that calls it. A schema migration propagates to every consumer across the stack. Cross-stack reasoning means the blast radius of any change is visible before it ships.

11,773 nodes covering both Python SDK and Next.js frontend in a single graph — cross-stack impact analysis on every query.

Why Graqle

Make your AI actually understand your code

Graqle isn't another AI tool. It's the intelligence layer underneath all your AI tools. It turns your codebase into a knowledge graph that Claude, Cursor, and Copilot can query — and it gets smarter every day.

Your AI assistant, supercharged

MCP tools that plug into Claude, Cursor, Copilot, Windsurf

One command and your AI gets 7 new superpowers: graq_reason, graq_impact, graq_context, graq_preflight, graq_lessons, graq_learn, graq_inspect. It stops reading files and starts querying a knowledge graph. Same AI, dramatically better answers.

Works with Claude Code, Cursor, VS Code, Windsurf — zero config

100x fewer tokens. 500x cheaper.

Your AI bill drops overnight.

Your AI reads 60 files per question — 50,000 tokens, $0.15 each. Graqle gives it exactly the right 500 tokens of structured context. Same question, 100x less context, better answer. For a team of 10, that's $9,000/year saved.

Measured: 500 tokens vs 50,000 — every query tracked

Confidence scores. Not vibes.

Know when your AI is certain vs guessing.

Every answer comes with a confidence percentage, which graph nodes were consulted, and exact token cost. When confidence is low, Graqle tells you what's missing — no more "I think this is how it works." You ship decisions based on evidence.

99.7% accuracy on governance benchmark (MultiGov-30)

Governed AI. Audit everything.

DRACE scoring + tamper-proof audit trails

Every AI decision is scored on 5 axes and recorded in a hash-chained audit trail. Evidence chains link every answer to source code. When compliance asks "how do you trust your AI?" — you show them the dashboard.

DRACE governance + hash-chained audit trails + evidence chains

Your machine. Your keys. Your data.

Zero cloud required. No data leaves your network.

Run offline with Ollama ($0). Use your own Claude, GPT-4, or Gemini keys. Deploy on Bedrock for enterprise. 14 backends, one config line. Graqle never phones home — your code stays on your infrastructure.

14 backends: Ollama, Anthropic, OpenAI, Bedrock, Gemini, Groq, DeepSeek, +7 more

Auto Loop. Autonomous fix-test-fix.

Set a task, watch it iterate until done.

Describe a task, set max retries, and hit Start. Graqle runs autonomous fix-test-fix loops with live SSE streaming — you see every round, every node activated, every governance score in real time. Budget tracking prevents runaway costs.

Live event feed + budget tracking + governance scoring per iteration

Graph Traversal. See everything.

Path Finder, Hub Explorer & Impact Analysis via Neo4j.

Find shortest paths between any two modules. Discover hub nodes that connect your architecture. Analyze blast radius before any change. Inspect node context with properties, neighbors, and source chunks — all in one tab-based dashboard.

Path Finder + Hub Explorer + Impact Analysis + Node Context in one view
Start building with Graqle

Free forever for individual developers · No credit card required

graq_reason — 92% confidence
GraQle reasoning output — 92% confidence with exact code location, trade-off analysis, and specific fix recommendation

Real output: 92% confidence. Exact variable location. Three color options with contrast ratios. Decision-ready in 3 minutes.

Features

16 commands. One AI coding assistant.

Not a graph viewer. A full development workflow — from first scan to governed deployment, with confidence scores at every step.

$graq scan
$graq reason
$graq review
$graq predict

Python SDK

Build on top of it

The same reasoning engine that powers the CLI, available as a Python library. Embed it in CI pipelines, internal tools, or custom dashboards. Every query returns the answer, confidence, and exact cost — no surprises.

example.py
from graqle.core.graph import Graqle
from graqle.backends.api import AnthropicBackend

graph = Graqle.from_json("graqle.json")
graph.set_default_backend(
    AnthropicBackend(model="claude-haiku-4-5-20251001")
)

result = graph.reason(
    "What services depend on auth?",
    max_rounds=3,
    strategy="top_k"
)

print(result.answer)
print(f"Confidence: {result.confidence:.0%}")
print(f"Cost: ${result.cost_usd:.4f}")
print(f"Tokens: {result.tokens_used}")

IDE Integration

Works where you work

GraQle runs inside VS Code, Claude Code, Cursor, and any MCP-compatible editor. No context switches. No browser tabs. Architecture intelligence inline.

GraQle running inside VS Code — file tree, knowledge graph reasoning, inline governance, real-time confidence scores

Pricing

Free means free. No asterisks.

Every developer gets the full product — all 15 patented innovations, every backend, unlimited queries. Teams pay for shared graphs and analytics. That's it.

Open Source

Full power. No limits. No credit card. Start building today.

$0forever
$ pip install graqle
  • All 15 patented innovations included
  • 201 skills across 9 domains
  • Intelligence compilation + CLAUDE.md auto-injection
  • CLI + Python SDK + REST API + MCP tools
  • 14 AI backends (Ollama, Claude, GPT-4, Gemini, Groq...)
  • Commercial use — Apache 2.0 license
Most Popular

Pro

For developers who ship fast and want answers faster.

$19/mo
Start Free Trial
  • Full intelligence dashboards (risk heatmap, insights)
  • DRACE governance with 30-session history
  • Audit trails (20 sessions) + evidence chains
  • Health streak calendar (full year)
  • Impact blast radius (3 hops)
  • Priority support — 24h response

Enterprise

Shared intelligence across your entire engineering org.

$29/seat/mo
Start Enterprise Trial
  • Everything in Pro, unlimited
  • Shared team knowledge graphs + Neo4j GDS
  • Team DRACE leaderboard + cross-repo insights
  • Unlimited audit trails + team streaks
  • SSO + compliance reporting
  • Dedicated support + onboarding

FAQ

Frequently asked questions

Stay ahead of the curve

AI governance, architecture intelligence, and developer experience insights. Delivered when it matters.

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Your AI is only as good as
the context you give it.

Stop feeding it files. Feed it your architecture. 10 seconds to install. First answer in 5 seconds. Your AI — finally aware of how your code actually works.

$ pip install graqle && graq init
Apache 2.0 licensed15 patents filedNo account neededRuns offline