| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Enables developers to create sequential, dependent branches locally via `gt create` command, with Graphite backend tracking parent-child relationships and storing stack metadata. The CLI manages branch dependencies without modifying Git internals, allowing users to visualize stacks with `gt log`, update changes across multiple branches with `gt modify` (which handles recursive rebasing), and publish entire stacks to GitHub via `gt submit` (creating/updating multiple PRs atomically). Local state syncs with remote via `gt sync`, and stale branches are automatically cleaned up.
Unique: Implements stacking as a first-class workflow primitive with backend-tracked dependency relationships and atomic multi-PR publishing, rather than as a manual branching convention or third-party script. The `gt modify` command handles recursive rebasing across the entire stack, eliminating manual conflict resolution for dependent changes.
vs alternatives: Faster than manual stacking (no manual rebasing) and more ergonomic than git-based tools like git-branchless because it provides GitHub-native PR creation with dependency awareness, not just local branch management.
Manages PR merging in dependency order, respecting parent-child relationships from stacked PRs and automatically rebasing child PRs when parents merge. The merge queue prevents conflicts by ensuring main branch stays green, only running CI when necessary (not on every rebase), and handling complex dependency graphs. Available in basic form on Team tier and with advanced settings on Enterprise tier; exact algorithm for circular dependency detection and conflict prevention is undocumented.
Unique: Integrates stacked PR dependency metadata with merge queue logic, enabling stack-aware rebasing and CI optimization that respects parent-child relationships. Unlike GitHub's native merge queue (which treats all PRs as independent), Graphite's queue understands that child PRs should not merge before parents and can skip redundant CI runs.
vs alternatives: More intelligent than GitHub's native merge queue because it understands PR dependencies and can optimize CI runs; simpler than custom merge queue scripts because dependency relationships are tracked automatically from stacking workflow.
Optional code indexing capability (Enterprise tier only) that enables AI review to access broader codebase context beyond individual PR diffs. Indexing appears to support semantic search and context retrieval, though implementation details are completely undocumented. Enterprise tier includes 'Code indexing controls' suggesting optional indexing and data residency options, but specific indexing scope, update frequency, and retrieval mechanism are unknown.
Unique: Adds codebase-aware context to AI review via optional indexing, enabling AI to understand architectural patterns and code conventions beyond individual PRs. Appears to be a retrieval-augmented generation (RAG) approach, though implementation is undocumented.
vs alternatives: More powerful than PR-only AI review because it understands codebase context; less mature than dedicated code search tools (Sourcegraph, Codebase) because indexing details are undocumented and scope is limited to AI review.
Enables Graphite deployment on GitHub Enterprise Server (GHES) for organizations requiring on-premises or private cloud infrastructure. Enterprise tier includes support for GHES integration with private data processing and optional data residency controls. Exact deployment model (Graphite-hosted vs. customer-hosted), data flow, and infrastructure requirements are undocumented.
Unique: Provides GHES support as an Enterprise feature, enabling Graphite to work with on-premises GitHub deployments. Includes private data processing and optional data residency controls, addressing enterprise compliance requirements.
vs alternatives: Enables Graphite for enterprises that cannot use GitHub.com; less mature than GitHub's native GHES features because Graphite integration details are undocumented.
Integrates with Semgrep (open-source SAST tool) to provide static analysis and security scanning results within Graphite PR reviews. Integration appears to surface Semgrep findings in AI review comments or as separate review items, though exact integration mechanism and data flow are undocumented. Mentioned in case study but not detailed in product documentation.
Unique: Integrates Semgrep findings directly into Graphite PR review workflow, surfacing security issues alongside AI review feedback. Provides a unified view of code quality and security concerns.
vs alternatives: More integrated than running Semgrep separately because findings appear in PR review; less comprehensive than dedicated security platforms (Snyk, Checkmarx) because scope is limited to Semgrep rules.
Analyzes PR diffs via Graphite Chat (AI agent) and automatically generates review comments, suggested code changes, and CI failure analysis. The AI processes PR metadata (title, description, comments), diff content, and CI logs to produce contextual feedback. Users can interact with Chat in the PR page to apply suggested fixes, which are committed back to the PR branch. The specific LLM model, context window size, and latency are undisclosed; implementation details of how suggested fixes are generated (executable patches vs. pseudocode) are unknown.
Unique: Integrates AI review directly into GitHub PR workflow with interactive Chat interface and commit-back capability, rather than as a separate tool or comment-only bot. Combines diff analysis with CI log analysis to provide contextual feedback on both code changes and test failures.
vs alternatives: More integrated than GitHub Copilot for PRs (which is comment-only) because it can apply fixes directly to branches; less comprehensive than dedicated SAST tools (Semgrep, SonarQube) because it lacks architectural/security scanning depth, but faster for routine code quality feedback.
Automatically generates PR title and description text from code changes and commit messages using AI analysis. Available on Hobby tier and above, this capability reads the diff content and commit history to produce a human-readable summary of changes. The generation is non-interactive (no user input required) and appears to run automatically when a PR is created or updated, though exact trigger conditions are undocumented.
Unique: Generates both title and description automatically from code changes without user interaction, integrated into the PR creation workflow. Unlike manual templates or prompts, this is fully automatic and requires no developer action.
vs alternatives: Faster than manual writing or template-based approaches; less customizable than user-prompted generation because it offers no control over content or style.
Provides a centralized dashboard aggregating all team PRs from GitHub with real-time sync, replacing GitHub's native PR interface. Supports filtering by author, CI status, review state, labels, and custom criteria. Includes keyboard shortcuts for navigation, at-a-glance status indicators (CI pass/fail, review state, merge conflicts), and actionable notification design. Syncs with GitHub in real-time (exact sync latency undocumented) and maintains state across web and VSCode extension.
Unique: Replaces GitHub's native PR interface with a custom dashboard optimized for high-volume review workflows, with real-time sync and keyboard-driven navigation. Integrates filtering, notifications, and status indicators into a single view rather than requiring navigation between GitHub pages.
vs alternatives: More ergonomic than GitHub's native interface for high-volume teams because it consolidates filtering and navigation; less feature-rich than GitHub because it doesn't support all GitHub PR features (e.g., detailed approval workflows, branch protection rules).
+5 more capabilities
Aggregates 67 TB of source code from the Software Heritage archive, filtering for permissively licensed repositories (MIT, Apache 2.0, BSD, etc.) across 600+ programming languages. Uses automated license detection and validation to ensure legal compliance for model training. Implements a rigorous deduplication pipeline at file and repository levels to eliminate redundant training data and reduce dataset bloat.
Unique: Largest open-source code dataset at 67 TB with automated opt-out governance allowing repository owners to request removal, combined with rigorous deduplication and PII removal pipeline — no other public dataset offers this scale with legal compliance and community control mechanisms
vs alternatives: Larger and more legally compliant than GitHub's CodeSearchNet (14M files) or Google's BigQuery public datasets, with explicit opt-out governance vs. implicit inclusion, and covers 600+ languages vs. Codex training data's undisclosed language distribution
Implements a community-driven opt-out system where repository owners can request removal of their code from the dataset without legal takedown notices. Maintains a registry of excluded repositories and re-applies exclusions during dataset updates. Provides transparent governance documentation and a clear submission process for removal requests, balancing open access with creator rights.
Unique: First large-scale code dataset to implement opt-out governance at dataset level rather than relying solely on license compliance, with transparent registry and community submission process — shifts power from dataset creators to code contributors
vs alternatives: More respectful of creator autonomy than GitHub Copilot's training approach (no opt-out) or academic datasets (one-time snapshot), and more scalable than individual DMCA takedowns
The Stack v2 scores higher at 61/100 vs Graphite at 57/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Automated pipeline that scans source code for personally identifiable information (email addresses, API keys, SSH keys, credit card patterns, phone numbers) and removes or redacts them before dataset release. Uses regex patterns, entropy-based detection for secrets, and heuristic rules to identify sensitive data. Operates at file level with configurable sensitivity thresholds to balance data utility against privacy risk.
Unique: Combines regex pattern matching, entropy-based secret detection, and heuristic rules in a unified pipeline with configurable sensitivity — more comprehensive than simple regex-only approaches, but trades off false positive rate against security coverage
vs alternatives: More thorough than GitHub's secret scanning (which only flags known patterns) because it includes entropy-based detection for unknown secret formats, but less accurate than specialized tools like TruffleHog due to language-agnostic approach
Indexes 67 TB of source code across 600+ programming languages with language-aware metadata (syntax, file extension, language family). Enables retrieval by language, license, repository, or code patterns. Uses Software Heritage's existing indexing infrastructure as foundation, augmented with language detection and classification. Supports both bulk download and filtered queries for specific language subsets.
Unique: Leverages Software Heritage's existing language detection and indexing infrastructure, then augments with BigCode-specific language classification and filtering — avoids reinventing language detection while providing dataset-specific query capabilities
vs alternatives: More comprehensive language coverage (600+ languages) than GitHub's Linguist (500+ languages) and more accessible than Software Heritage's raw API because it's pre-filtered for permissive licenses and deduplicated
Removes duplicate code files and repositories using content hashing (SHA-256 or similar) and fuzzy matching for near-duplicates. Operates in two stages: exact deduplication via hash matching, then fuzzy matching (e.g., Jaccard similarity or MinHash) to catch semantically identical code with minor formatting differences. Preserves one canonical copy of each unique code pattern while removing redundant training examples.
Unique: Two-stage deduplication combining exact hash matching with fuzzy similarity matching (likely MinHash or Jaccard) to catch both identical and near-identical code — more thorough than single-stage approaches but computationally expensive
vs alternatives: More aggressive deduplication than CodeSearchNet (which uses simple hash matching) because it catches near-duplicates, but less semantic than clone detection tools (which understand code structure) because it's content-based
Integrates with Software Heritage's comprehensive archive of 200+ million repositories and their full version control history. Extracts source code snapshots from Software Heritage's Git/Mercurial/SVN repositories, preserving repository metadata (commit history, author info, timestamps). Provides access to code at specific points in time, enabling historical analysis or training on code evolution patterns.
Unique: Leverages Software Heritage's universal code archive (200M+ repositories) as data source, providing access to code that would be impossible to collect via GitHub API alone — enables training on archived/deleted repositories and non-GitHub platforms (GitLab, Gitea, etc.)
vs alternatives: More comprehensive than GitHub-only datasets because it includes code from GitLab, Gitea, SourceForge, and other platforms archived by Software Heritage; more legally defensible than web scraping because it uses an established, community-maintained archive
Tracks and validates SPDX license identifiers for each repository, ensuring only permissively licensed code (MIT, Apache 2.0, BSD, etc.) is included. Maintains license metadata alongside code files, enabling downstream users to verify legal compliance. Implements license hierarchy and compatibility checking to handle dual-licensed or complex licensing scenarios.
Unique: Combines automated SPDX detection with manual review and maintains license metadata alongside code, enabling downstream users to verify compliance — more transparent than datasets that simply claim 'permissive licenses' without proof
vs alternatives: More legally rigorous than GitHub's CodeSearchNet (which doesn't validate licenses) and more transparent than Codex training data (which doesn't disclose license filtering at all)
Maintains versioned snapshots of the dataset (e.g., v2.0, v2.1) with documented changes between versions (new repositories added, deduplication improvements, PII removal updates). Provides checksums and manifests for reproducibility, enabling researchers to cite specific dataset versions and reproduce results. Tracks dataset lineage and transformation history.
Unique: Maintains semantic versioning and detailed changelogs for dataset releases, enabling researchers to cite specific versions and understand dataset evolution — more rigorous than one-off dataset releases without versioning
vs alternatives: More reproducible than academic datasets that are released once without versioning, and more transparent than commercial datasets (Codex) that don't disclose version history or changes
+2 more capabilities