Devin
Unclaimed verified 8 oct 2026The AI Software Engineer
TL;DR
Devin by Cognition is an autonomous AI software engineer that independently plans, writes, tests, and ships pull requests within its own sandboxed cloud environment. Built for mid-to-large engineering teams, it tackles tech debt, large-scale migrations, and repetitive backlog tasks. Its primary differentiator is its fully autonomous, closed-loop execution loop operating with a dedicated shell, browser, and editor.
What Users Actually Pay
No user-reported pricing yet.
Our Take
Devin marks a distinct paradigm shift away from inline autocomplete assistants toward asynchronous task delegation. Rather than sitting inside a developer's local IDE predicting keystrokes, Devin acts as a synthetic developer executing in a cloud virtual machine. It can pull repositories, read stack traces, write test harnesses, browse external documentation, and iteratively debug its own code before opening a GitHub pull request. The platform's primary strength lies in structured, bounded engineering workflows such as framework migrations, test suite backfills, and well-specified bug fixes. With parallel agent fleets and Devin Review, teams can offload high-volume backlog chores. Repository knowledge indexing allows it to retain team conventions and architectural playbooks over time. However, Devin is not designed for open-ended product architecture or ambiguous feature design where requirements are fluid. In addition, its compute-based consumption model (ACUs) means that stalled or infinite debugging loops can drive up operational costs without yielding a functional patch. Devin is best suited for organizations with mature CI/CD practices and well-defined testing pipelines looking to expand engineering bandwidth on routine tasks and multi-repo refactoring.
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The first autonomous AI software engineer
Pros
- + Autonomous sandboxed execution with access to a browser, terminal, and code editor to run and verify tests before opening PRs
- + Native integrations with GitHub, Slack, Linear, and Jira for automated ticket triage and PR creation
- + Parallel agent orchestration enabling multiple tasks and multi-repo migrations to execute simultaneously
- + Knowledge base indexing that learns repo-specific architecture, patterns, and team playbooks
Cons
- - ACU compute consumption can be unpredictable and costly on complex or stuck tasks
- - Struggles with ambiguous, open-ended architectural specifications lacking clear test signals
- - Slower turnaround times compared to real-time interactive pair programming in local IDEs
- - Contractual opt-out for model training on private codebases is restricted to paid and enterprise tiers
Sentiment Analysis
Sentiment has improved since last capture. Sentiment has shifted substantially positive (+0.64 vs baseline 0.00). While early 2024 demos faced skepticism, the release of stable parallel agents, Model Context Protocol (MCP) support, sandboxed VM test execution, and enterprise customer outcomes have proven its practical utility for bounded engineering backlogs.
Sentiment Over Time
By Source
240 mentions
Sample quotes (2)
- "IMO it's the best tool for AI generating features if you know what you're looking for, have patterns to follow, etc. I suspect the context they build about your project helps a ton."
- "Devin Review is free and available for PRs... I just tried this on a production PR and I liked it. It found some things that Claude review missed."
Agent Readiness
73/100Devin offers tier-1 agent readiness with a comprehensive v3 REST API, an official hosted Model Context Protocol (MCP) server, and deep integrations with GitHub, Slack, and Linear. Its sandboxed cloud runtime, OpenAPI specifications, and support for automated parallel agent dispatch make it ready for programmatic workflows and external agent orchestration.
Last checked Oct 2, 2026
MCP Integrations
3 servers3 tools237 total usesNavigate and understand GitHub repository documentation effortlessly by retrieving wiki structures and contents. Get direct answers to specific questions about project wikis to save time searching through manual pages. Streamline the onboarding process by quickly grasping the layout and details of any GitHub project.
3 tools
read_wiki_structureGet a list of documentation topics for a GitHub repositoryread_wiki_contentsView documentation about a GitHub repositoryask_questionAsk any question about a GitHub repository
Local supply-chain CVE scanner via OSV/NVD. Scans deps and IDE extensions. No upload.
Needs a self-provisionable API key
All public upAPI operations as MCP tools: web scraping, search, screenshots, PDF, OCR and more.
Last checked Sep 13, 2026
[ features ]
Geostrategic Position
Information on which part of the world this product / vendor belongs to, i.e. the country of their headquarters primarily, but also their hosting options etc.
Find which geostrategic world region the headquarter is located in. Relevant for compliance questions (e.g., CLOUD Act) or risk of cut-off in case of conflicts. For example, some EU companies are worried about the US and would definitely not host their customer with Chinese or Russian companies.
The hosting provider that is used to host this product, if any.
The available hosting locations, if you can choose
Compliance & Security
Security certifications, compliance features, and access control capabilities.
SOC 2 Type I or Type II certification.
ISO 27001 information security certification.
Built-in tools for GDPR compliance (data export, deletion, consent).
Complete audit log of all data changes.
Granular permissions based on user roles.
Single Sign-On integration support.
Security, Privacy & Support
Features ensuring data protection, quality assurance, and user assistance.
Compliance standards for data security.
Automatic deletion of user photos after processing.
Availability of real-time customer support.
Offers free remakes for unsatisfactory results.
Pricing & Free Tier
Free tier limits and overall pricing structure.
Maximum Monthly Active Users allowed on the free tier.
Key usage metrics that incur costs.
Autonomy & Workflow
Level of autonomous execution and human oversight mechanisms across the SDLC
Can complete multi-step engineering tasks with minimal human intervention
Provides explicit approval gates, oversight dashboards, and workflow governance
Supports running multiple agents simultaneously for concurrent tasks
Automatically learns and retains knowledge from existing codebases over time
SDLC Coverage
Which stages of the software delivery lifecycle the product can handle
Supports ingesting requirements from Jira, Figma, or natural language and generating plans
Generates or edits production code across multiple files
Creates tests, runs test suites, and performs visual or regression QA
Handles deployments, incident triage, runtime monitoring, and observability
Performs pull request reviews and generates documentation
Integrations & Ecosystem
Connections to developer tools, IDEs, and collaboration platforms
Supported code editors and development environments
Native integration with GitHub, GitLab, or other version control systems
Connections to issue tracking and project management tools
Integrations with communication and alerting platforms
Compare With
Reviews
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