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DeepSource

The AI Code Review Platform

For agents: DeepSource AI code review platform offering static analysis, SAST, SCA, code coverage and IaC security, with getting-started and full documentation.

ai code reviewstatic analysissastcode coveragesoftware composition analysis

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T21:10:39.088Z. Cached copy · Source on deepsource.com

# DeepSource

> The AI Code Review Platform

---

## The Problem

Teams are writing more code than ever with AI coding agents. But more
code means more surface area for bugs, security vulnerabilities, and
technical debt — and human reviewers can't keep up.

Most code review tools are either pure static analysis (high precision,
low recall) or pure AI (inconsistent, non-deterministic, noisy). Neither
is good enough to trust as a CI/CD gate.

## What DeepSource Does

DeepSource automates code review on every pull request using a hybrid
analysis engine that combines 5,000+ deterministic static analysis rules
with an AI review agent. The result is high-signal, low false-positive
code review — across GitHub, GitLab, Bitbucket, and Azure DevOps.

Every PR gets a Report Card grading code across five dimensions:
Security, Reliability, Complexity, Hygiene, and Coverage. This gives
AI coding agents structured, actionable feedback to systematically
improve — not just a flat list of issues.

No CI configuration required. Connect your repo and get results in
minutes.

---

## The Hybrid Analysis Engine

DeepSource is the only code review platform with a hybrid engine that
combines static analysis and AI in a single pipeline. This is not AI
bolted onto a legacy tool — it is the default analysis mode for all
customers.

### How it works

1. **Codebase Indexing** — Builds a per-PR AST and whole-project graph
   (data-flow, control-flow, import graph, sources/sinks). Intelligently
   cached across runs. No full repository pre-indexing required.

2. **Static Pass** — Runs 5,000+ static analyzers to establish a
   low-false-positive baseline. A sub-agent filters context-specific
   false positives before seeding the AI review.

3. **AI Review** — Static findings seed the AI agent's review. The agent
   has access to source code tools (ripgrep, graph lookups). A taint
   analysis sub-agent tracks the flow of potentially insecure data. The
   agent reviews the relevant code slice with full codebase context.

4. **Multi-layer Caching** — Source code, AST, and project stores are
   cached across runs for fast repeat analysis.

### Why hybrid wins
…

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