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Project Introduction

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Watch this quick overview to see how GitRabbit automatically reviews pull requests, catches edge-case bugs, and enforces security policies directly within your workflow.

Complete Feature Set

Everything you need for smarter code reviews

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Core PR Reviews

Automated PR Analysis

Context-aware, line-by-line feedback on pull requests within minutes of creation.

Core PR Reviews

AI-Generated Summaries

Comprehensive summaries and architectural walkthroughs of code changes to help human reviewers.

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One-Click Fixes

Actionable, committable code suggestions that developers can apply directly with a single click in the PR.

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Agentic Chat

Contextual AI conversations directly within GitHub or GitLab comment threads to debate and refine code changes.

Core PR Reviews

Bug & Defect Detection

Identifies runtime errors, null pointers, race conditions, logic flaws, and AI hallucinations before deployment.

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Autonomous Code Governance

Verify AI-Agent CodeAgainst Real Specs

Autonomous AI agents write code in seconds—but do they satisfy the client's actual requirements? GitRabbit ingests Jira tickets, PRDs, and codebase architecture to audit AI pull requests for strict spec compliance before human review.

01REQUIREMENTS_INGESTION

Ingest Client Specs

Connects to Jira tickets, Linear issues, or Markdown PRDs to extract verifiable acceptance criteria.

02CODE_GRAPH_MAPPING

Deep Repo Context

Cross-checks database schemas, API boundaries, and auth policies so code is never audited in a vacuum.

03SPEC_ALIGNMENT_AUDIT

AST-to-Spec Verification

Analyzes AI-agent generated code against every requirement to catch omitted edge cases or spec drift.

04AUTONOMOUS_FEEDBACK

Automated PR Clearance

Applies automated pass/fail review badges or sends structured re-prompt instructions back to the agent.

Spec Verification Engine // Visual Overview

Comparing client user stories against AI agent pull requests

Client Specification

Jira #DATA-290

Client User Story:

“Telemetry events must be routed through Kafka with exponential retry. Any unhandled or poisoned payloads must be published to a Dead Letter Queue (DLQ) without crashing the worker.”

Acceptance Criteria Checklist:

AC-01: Exponential Retry BudgetConsumer retries transient connection drops up to 3 times.

AC-02: DLQ Dead Letter Queue RoutingPoisoned records forwarded to telemetry_dlq topic.

AC-03: Confluent Schema RegistryAVRO backward-compatibility validation active.

Ingestion Source: Jira API WebhookContext Synced

AI Agent Pull Request: feat/kafka-telemetry-consumer

Author: Devin AI

lib/telemetry/consumer.ts+42 -6 lines

export async function processTelemetryEvent(record: KafkaRecord) {

+ const registry = new ConfluentSchemaRegistry(env.SCHEMA_URL);

+ await registry.validate(record.value, "telemetry_v2");

+ await dlqProducer.send({ topic: "telemetry_dlq", payload: record });

}

gitrabbit autonomous bot verdict

100% SPEC ALIGNED

All 3 acceptance criteria verified against Jira #DATA-290. No hallucinated libraries found; DLQ fallback and schema registry compliance confirmed.

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PRD & Story Ingestion

Pulls acceptance criteria directly from Jira, Linear, or repo Markdown documents into automated constraint models.

Deep Context Mapping

Cross-references Prisma schemas, auth middleware, and existing APIs so agents don't bypass established code rules.

Spec Drift Guard

Catches when AI agents create code that compiles and passes unit tests, yet fundamentally violates client business logic.

Closed-Loop Re-prompting

Feeds exact, structured correction instructions back to the coding agent so the code is fixed before human engineers review.

Ecosystem Compatibility

Works seamlessly with your AI agent stack

Compatible with GitHub, GitLab & Bitbucket

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gitrabbitbot2 minutes ago

Summary

Great work! This PR improves error handling and adds comprehensive tests.

Issues (3)

HighPossible null pointeron line 42
MediumConsider using conston line 17
LowRedundant conditionon line 69

Suggestions (2)

💡 Extract functionon line 23
💡 Simplify expressionon line 67

src/utils/payment.ts⚙ Review settings

10 ...

11 if (!payment) {

12 throw new Error('Invalid payment');

13 }

- const amount = payment.amount;

+ const amount = payment?.amount ?? 0;

+ if (amount <= 0) {

+ throw new Error('Amount must be greater than 0');

18 }

19 return charge(amount);

CR_Intelligence

Code reviews that learn from you.

Set the baseline with your rules and style guides, then train the agent with feedback via replies. Reviews improve continuously.

gitrabbit_config.yaml

1# .gitrabbit.yaml
2language: en
3learning_mode: true
4auto_adapt: true
5
6reviews:
7  profile: "professional"
8  feedback_loops:
9    - enabled: true
10      threshold: 0.8
11      source: "pull_request_comments"
12
13# Learn from previous PR feedback
14context_window: 10
15deep_learning: enabled

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