Unblocked vs Greptile: Best code review tool comparison for your team (February 2026)

February 10, 2026

When you're reviewing pull requests (PRs), many comments are technically right but contextually wrong. With 92% of developers using AI tools by 2026 and 84% using AI coding assistants for code generation and review, choosing the right AI code review tool has become critical.

Most existing tools lack visibility into prior discussions of the approach, documented constraints, or patterns from previous work. That gap in visibility fundamentally shapes how different code review tools behave in practice. Of the available tools, both Greptile and Unblocked analyze repository structure, but only Unblocked treats organizational knowledge as part of the same system. Greptile references external context when available. Unblocked treats organizational knowledge as part of the same system, changing what gets caught and how. When reviews cite the actual context behind your code, comments don't feel like false positives.

TLDR:

Greptile analyzes code through repository graphs; Unblocked grounds reviews in organizational context from your codebase and its history, Slack, Jira, and docs.
Unblocked cites the decisions behind code patterns, catching context-level issues as well as technical defects.
Our MCP Server and API provide context to AI coding tools before code is written, preventing review issues at the source.
Unblocked extracts team standards automatically from your knowledge graph instead of requiring manual rule configuration.
"Unblocked's context awareness is what sets it apart. It knows what we've discussed, decided, and why things are the way they are." - Lemuel Dulfo, Clio

What is Greptile?

Greptile is an AI code review tool that analyzes pull requests by building a graph representation of your repository. lt maps functions, variables, classes, files, and directories to understand how different parts of the codebase connect and interact.

When reviewing code, Greptile uses this repository graph to catch bugs, security vulnerabilities, anti-patterns, and dependency issues. The approach differs from simpler diff-based review tools by analyzing how changes affect the overall structure of the codebase, extending beyond the modified files.

Greptile indexes repositories to create what it calls codebase-aware AI analysis, analyzing PRs against the full context of your repository structure. This approach helps it identify issues that might not be obvious when looking at changed files in isolation.

Greptile focuses on structural analysis of codebases, which is particularly useful for teams that need to understand how changes ripple through interconnected systems. The tool runs automated reviews on pull requests and surfaces issues based on its understanding of repository architecture.

What is Unblocked?

Greptile's approach is well-suited to structural analysis. Unblocked Code Review takes a different path.

Unblocked grounds code review in an organizational context and pulls from the repository structure. The tool analyzes your codebase alongside knowledge from Slack discussions, Jira tickets, Confluence docs, and PR history to understand the decisions and constraints behind your system.

Code that looks wrong in isolation might be intentional. A team might have skipped a validation because there's downstream error handling, or chosen a specific data structure based on performance constraints discussed months ago in Slack. Unblocked surfaces that context during review.

The tool catches technical bugs and flags code that's technically correct but wrong for your specific architecture or standards. When Unblocked leaves feedback, it cites the sources behind that feedback, including Slack discussions, architectural decision records, and prior PRs where the pattern appears consistently.

Context sources and integration depth

Greptile starts with codebase graph analysis, mapping relationships among code elements. The tool pulls context from Notion and Jira during reviews, referencing requirements or specs when analyzing PRs.

The system learns through thumbs-up/thumbs-down reactions on comments. Teams upload custom rules to flag specific patterns unique to their codebase. Configuration focuses on source control connections and rule file uploads.

Unblocked brings organizational knowledge to the review, treating code and organizational context as a single system. This connects GitHub, GitLab, Slack, Jira, Confluence, Notion, Google Drive, and other sources that document decisions or trade-offs.

When documentation contradicts a recent Slack discussion about implementation choices, Unblocked resolves the conflict by weighing freshness, source type, relationships, and specificity to determine the current truth relative to historical context.

Greptile references external context when available in connected tools. Unblocked treats that context as part of the same system understanding code, changing what gets caught and how we determine whether something is wrong.

Review quality and what gets flagged

Greptile catches technical bugs through repository graph analysis, scanning for anti-patterns, security vulnerabilities, repeated code, and dependency issues. The tool includes confidence scores and learns from thumbs-up/thumbs-down reactions over 2-3 weeks.

The challenge is separating signal from noise. Some teams report high comment volumes where technically accurate observations aren't actionable. When the tool can't tell intentional decisions from mistakes, coverage becomes overwhelming.

The Unblocked review agent is powered by our context engine. Unlike Greptile’s repository graph or add-on MCP tools that only see their own narrow slice, Unblocked brings everything together. It can surface the “unknown unknowns” an agent wouldn’t know to look for, automatically filling in missing links.

Reviews cite the sources behind each issue. If validation is skipped in a way that contradicts error handling patterns, we reference the Slack thread documenting that approach. If a data structure conflicts with performance constraints, we link to the discussion that documented those limits.

Feature	Greptile	Unblocked
Primary detection method	Repository graph analysis mapping code element relationships and dependencies across functions, variables, classes, files, and directories	Unified knowledge graph assessing code against organizational context from codebase, Slack conversations, Jira tickets, Confluence/Notion docs, and PR history
Context sources	Repository structure, Notion and Jira for analysis against issue requirements	GitHub/GitLab, Slack, Jira, Confluence, Notion, Google Drive, and other sources documenting decisions and trade-offs; treats organizational knowledge as part of the same system
Types of issues flagged	Technical bugs, anti-patterns, security vulnerabilities, repeated code, and dependency issues based on structural analysis of how changes affect codebase architecture	Technical bugs plus context-level issues like code that contradicts architectural decisions, violates documented constraints, or conflicts with team standards discussed in Slack/Jira
False positive handling	Confidence scores and thumbs-up/thumbs-down reactions over 2-3 weeks to tune behavior; may flag technically correct code that appears wrong without organizational context	Validates issues through documented decisions to distinguish intentional patterns from mistakes; filters noise by checking against past Slack threads, ADRs, and requirements
Comment volume	Can generate high comment volumes where technically accurate observations aren't always actionable without organizational context	Reduced noise through context-aware filtering; comments reference specific discussions, requirements, or architectural constraints that explain why something matters
Citation and traceability	References external context when available in connected tools (Notion, Jira)	Cites sources behind issues: Slack threads documenting approaches, Jira tickets with requirements, Confluence/Notion ADRs, PR history showing patterns
Learning mechanism	Adapts through thumbs-up/thumbs-down reactions and comment replies over 2-3 weeks; teams upload custom rules via greptile.json files to define review behaviors	Learns from PR reactions and replies; automatically extracts standards from the knowledge graph by synthesizing patterns from Slack, Jira, docs, and PR history
Best for	Teams needing structural codebase analysis and standard bug detection through complete graph mapping, and those willing to define rules manually	Teams where intentional decisions get flagged as errors, or where technically correct code violates architectural constraints discussed in past conversations

MCP integration and upstream workflow

Greptile's MCP Server connects code review feedback to IDEs and coding agents like Devin, bringing review patterns directly into editors. This keeps feedback accessible during development without tool-switching.

We built our MCP Server to supply context generation directly into the code generation workflow. The knowledge graph feeds organizational decisions, past Slack threads, Jira requirements, and architectural constraints directly to AI coding tools. Our MCP Server focuses on preventing review problems by improving what AI generates from the start, complementing the review cycle.

Our CI Failure Agent also reads CI logs and suggests fixes directly in pull requests.

The split is about timing. Greptile optimizes review feedback loops inside your IDE. The focus of our MCP server is on preventing review problems by improving what AI generates from the start.

Learning mechanisms and customization

Both tools learn from PR feedback but take different approaches to acquiring team standards.

Greptile adapts through thumbs-up/thumbs-down reactions and comment replies on PR reviews, tuning behavior over 2-3 weeks of feedback. Teams upload custom rules via greptile.json files to specify review behaviors: which labels trigger reviews, what comment types surface (logic, syntax, style, info, advice), and custom guidance instructions. The system can reference pattern repositories for coding standards. Configuration flexibility is strong, but learning depends on explicit feedback or manual rule definition.

Unblocked also learns from PR reactions and replies, but automatically extracts standards from our knowledge graph. The system synthesizes patterns from Slack discussions about implementation approaches, Jira requirements that document constraints, Notion or Confluence records of architectural decisions, and PR history, reflecting existing implementation patterns. When reviews reference learned patterns, Unblocked cites the original source: the Slack thread, the doc, the ticket.

Custom rule uploads are coming soon. Right now, configuration focuses on connecting tools that feed the knowledge graph instead of writing rules by hand.

Why Unblocked is the better choice

Greptile works well for teams wanting codebase graph analysis and IDE-integrated review. It catches technical bugs and offers configuration flexibility for those willing to define rules manually.

We solve the core problem: review noise that stems from generic feedback that ignores how your team works. The knowledge graph understands the decisions, discussions, and constraints that determine whether code fits your system. This reduces false positives from intentional patterns and surfaces context-level issues that other tools miss. Standards are extracted automatically as part of the normal workflow, and MCP integration moves issue prevention to code generation instead of review.

Teams report fewer false positives because Unblocked validates issues against undocumented decisions. As Lemuel Dulfo at Clio notes: "Unblocked's context awareness is what sets it apart. It doesn't look at code in isolation. It knows what we've discussed, what we've decided, and why things are the way they are."

Final thoughts on selecting the right code review tool

Most code review tools flag things that look wrong without knowing whether they're intentional. That's why we built Unblocked around organizational context instead of just code structure.

Connect your repos and knowledge sources to see how context affects the quality of what surfaces in your PRs.

FAQ

How should I decide between Unblocked and Greptile for my team?

Start with what's slowing you down. If your AI code reviews flag technically correct code that violates team decisions, or miss contextually wrong patterns that are syntactically fine, you need organizational context. If your main concern is mapping repository structure and catching standard bugs through graph analysis, Greptile's approach works. We’re built for teams that already know how to find issues and need help determining which ones actually matter for their system.

What's the main difference in how the two tools analyze code?

Greptile builds a repository graph mapping code elements and their relationships, then analyzes PRs against that structure. We build a knowledge graph connecting code with Slack discussions, Jira tickets, Confluence docs, and PR history. When we flag an issue, we cite the Slack thread explaining why your error handling works that way, or the architectural decision record documenting your performance constraints. The difference is between understanding repository structure and understanding organizational decisions.

Who is Unblocked best suited for?

This fits teams where code review repeatedly flags intentional patterns as mistakes, and technically correct code ships while violating architectural constraints discussed weeks earlier. Senior engineers and engineering leaders who need reviews grounded in past decisions, not generic rules. Companies with complex codebases where context about why something was built a certain way matters as much as what the code does.

What if my team doesn't have time to manually define custom rules?

We automatically extract standards from your knowledge graph. That includes Jira tickets and architectural decision records documenting constraints and decisions, as well as PR history reflecting existing implementation patterns. The system learns patterns from how your team actually works and cites sources when applying those patterns during reviews. Custom rule uploads are coming soon, but for now, configuration focuses on connecting the tools that feed the knowledge graph instead of writing rules by hand.

Can Unblocked prevent review issues before they happen, or just catch them faster?

Both. AI generates code that aligns with your system from the start. Our MCP Server supplies AI coding tools with organizational context, including prior architectural decisions, documented constraints, and Jira-defined requirements. During review, we catch issues that other tools miss by validating against documented decisions. Our CI Failure Agent also reads CI logs and suggests fixes directly in pull requests. The goal is fewer review cycles because the code reflects team standards from the start.