Progress

If your team runs its workflow in Linear, you already have the raw data to answer questions like "why did this initiative slip" or "is the team overloaded," but Linear's native views weren't built to answer them. This roundup covers tools that pull Linear (and usually GitHub) data into dashboards or decision-ready signals, selected for real Linear integrations, transparent-enough pricing, and relevance to startup engineering and product teams rather than large enterprise IT shops.

Quick comparison

Progress: best for startup engineering leaders who want interpreted risk and morale signals; pricing is quote-based; the only tool here that scores deployment risk and team morale rather than just charting activity.
Linear Insights (native): best for small teams wanting a first read on delivery trends; included with paid Linear plans; the only option that requires zero setup or integration.
Swarmia: best for teams fluent in DORA metrics; quote-based tiered pricing; the only tool with automatic working-agreement enforcement on PR size and review time.
LinearB: best for multi-team orgs benchmarking delivery; quote-based tiered pricing; the only tool that pairs analytics with gitStream workflow automation.
Jellyfish: best for leaders reporting engineering spend to finance; quote-based; the only tool built around investment allocation and cost-of-delay reporting.
Hatica: best for mixed Linear/Jira/Slack stacks; quote-based tiered pricing; the only tool with dedicated burnout and context-switching metrics.
Axify: best for teams wanting simple, retro-ready flow metrics; quote-based tiered pricing; the only tool explicitly designed for live sprint retro review.
Screenful: best for stakeholder-facing chart sharing; tiered pricing starting at a monthly rate; the only tool centered on scheduled, automated report emails.

1. Progress

Progress is an AI-native engineering intelligence platform built for startup engineering leaders and dev managers who need to know what's actually happening across their codebase and team, not just what a chart implies. It ingests Linear and GitHub activity and analyzes it continuously, surfacing pre-computed signals rather than leaving interpretation to the reader.

What separates Progress from the rest of this list is the shift from visualization to interpretation. Most Linear analytics tools hand you cycle time charts or DORA dashboards and expect you to draw conclusions. Progress does that analysis upfront, flagging stalled work, scoring deployment risk from merge volume and code churn, and reading team momentum and morale before those problems show up in a sprint retro.

Pre-computed signals flag stalled work and emerging risk before they become missed deadlines
Deployment risk and change-pressure scoring based on merge volume and code churn tells you when a release window is getting dangerous
Initiative and work-stream health tracking connects Linear projects to actual delivery reality
Team momentum and morale/wellness reads give you a human-layer signal most engineering tools skip entirely
An MCP server and Claude API integration let you ask plain-language questions and get executive summaries on demand

Setup involves connecting Linear and GitHub; day-to-day use is typically owned by an engineering manager or VP of Engineering who wants a standing signal rather than a report they have to build. Because Progress is newer to this category than platforms like LinearB or Jellyfish, it has less of a multi-year track record with very large, multi-team enterprise orgs. Pricing is quote-based as of 2026; check the website for current tiers.

Best for: Startup engineering leaders who want Linear and GitHub activity synthesized into clear risk and morale signals, not more dashboards to interpret.

2. Linear Insights (native)

Before adding any third-party tool, most teams should know what Linear already gives them. Linear Insights is the built-in analytics layer showing cycle time, throughput, and completion trends directly inside the workspace teams already use every day.

Its main advantage is that it requires nothing extra. There's no integration to configure, no second login, and no data-sharing agreement to negotiate. If your team is small and just wants to know whether cycles are speeding up or slowing down, this is the fastest way to find out.

Cycle time and throughput charts built from issues your team is already tracking
Team and project completion trend views for spotting slippage early
No setup, no separate tool to learn, no data pipeline to maintain
Included as part of paid Linear plans rather than billed separately

The limitation is scope. Linear Insights has no view into GitHub or code-level activity, so it can tell you an initiative is behind schedule but not why: whether it's blocked by review latency, a risky merge, or overloaded engineers. Reporting also isn't customizable beyond what Linear ships. Any engineering manager can check it without training, but it won't answer deeper diagnostic questions.

Pricing is included with paid Linear plans as of 2026; see the website for current plan pricing.

Best for: Small teams that want a quick read on delivery trends before investing in a dedicated analytics tool.

3. Swarmia

Swarmia is an engineering effectiveness platform that combines Linear issue data with Git activity to track DORA metrics, deployment frequency and lead time for changes, and to enforce team working agreements. It's aimed at engineering managers who already think in terms of these standard metrics and want a tool that operationalizes them rather than just displaying them.

What it does well

Swarmia's standout feature is its working agreements: configurable rules around PR size, review turnaround, and WIP limits that get flagged automatically when a team drifts outside them. That turns metrics into a living process check rather than a monthly report nobody reads.

DORA metrics tracking, including deployment frequency and lead time for changes
Working agreements that auto-flag broken thresholds like oversized PRs or slow reviews
Direct Linear and GitHub/GitLab integration for combined issue and code visibility
Team and individual contribution views for spotting imbalanced workload

Where it falls short

Swarmia is metrics-first by design, which means your team still has to interpret what the numbers mean for a given sprint or initiative. It won't tell you a release is risky; it will show you the churn and lead-time data and expect you to connect the dots. Setup requires connecting Linear, GitHub or GitLab, and Slack, and it's typically owned by an engineering manager. Pricing is tiered and quote-based as of 2026; see the website for current rates.

Best for: Teams already fluent in DORA metrics who want configurable enforcement of engineering norms.

4. LinearB

Its limitation up front: LinearB can feel like more tool than a five-person startup needs. It's an established engineering analytics platform built for organizations running several teams that want to benchmark delivery performance against each other, and its metric taxonomy and setup reflect that scale.

Where LinearB earns its place on this list is gitStream, a workflow automation layer that goes beyond reporting into action, automatically labeling and routing pull requests based on rules your team defines. No other tool here extends analytics into that kind of automated PR handling.

Cycle time breakdown by stage so you can see exactly where work stalls
Cross-team benchmarking dashboards for comparing squads on the same metrics
gitStream automation for PR labeling and routing based on custom rules
Native integrations with Linear, GitHub, and GitLab

Setup typically involves an engineering manager or platform team connecting Linear and your Git provider, then configuring gitStream rules over time. Day-to-day ownership usually sits with an EM or director overseeing multiple teams. A single small team evaluating LinearB may find the benchmarking dashboards more than they need, and the onboarding lift heavier than a lighter tool like Axify. Pricing is tiered and quote-based as of 2026; see the website for current rates.

Best for: Multi-team organizations benchmarking delivery performance across several squads.

5. Jellyfish

Jellyfish is an engineering intelligence platform built for a different buyer than most of this list: the engineering leader who needs to justify headcount and spend to finance and the board, not just track sprint velocity. It maps Linear and GitHub activity against roadmap and business priorities to report on how engineering investment is actually allocated.

No other tool here is built specifically for that conversation. Jellyfish's investment allocation reporting breaks down time spent on new features versus maintenance versus tech debt, and pairs it with headcount and cost-of-delay analysis, giving non-engineering stakeholders a way to understand engineering spend in terms they already use.

Investment allocation reporting across features, maintenance, and tech debt
Roadmap and Linear initiative alignment tracking to show whether work maps to strategy
Headcount and cost-of-delay analysis for budget conversations
Executive-facing reports designed for finance and board audiences

This depth comes at a cost. Jellyfish is enterprise-oriented in both pricing and onboarding, and a five- or ten-person engineering team is unlikely to need, or be able to justify, its full feature set. Implementation typically involves a sales conversation and a longer setup process than the other tools on this list, and it integrates with Linear, GitHub, Jira, and HR and finance systems. Pricing is quote-based as of 2026 and generally requires talking to sales.

Best for: Engineering leaders who need to report investment allocation and cost data to non-engineering stakeholders.

6. Hatica

Hatica connects Linear, GitHub, Jira, and Slack to track both delivery metrics and developer experience signals, positioning it for teams whose stack isn't purely Linear and GitHub. If your organization still has pockets of Jira or leans heavily on Slack for coordination, Hatica covers more of that surface than the other tools here.

Its differentiator is developer experience tracking: metrics like context-switching and after-hours activity that point toward burnout risk before it shows up as attrition or slipping delivery. Combined with goal-setting tied to Linear cycle progress, it gives managers a wellness lens most flow-metrics tools skip.

Developer experience metrics covering context-switching and after-hours work patterns
Goal-setting features tied directly to Linear cycle progress
Integration across Linear, GitHub, Jira, and Slack for a mixed-tool team
Sprint and cycle-level reporting for standard delivery tracking

The tradeoff for that breadth is maintenance. Covering four different tools means more dashboards and configuration options than a smaller team may want to keep current, and someone, usually an engineering manager or ops-minded team lead, needs to own that upkeep. Pricing is tiered and quote-based as of 2026; see the website for current rates.

Best for: Mixed-stack teams that need developer experience tracking alongside delivery metrics.

7. Axify

Axify is a lightweight flow-metrics tool built for teams that want cycle time, throughput, and WIP visibility without adopting a full engineering-intelligence platform. It pulls from Linear and Git history and keeps its dashboard set intentionally simple.

What sets Axify apart here is its focus on live use in sprint retros. Reports are designed to be pulled up and discussed with the team in the room, rather than generated for a manager to read alone later. That makes it less of a top-down reporting tool and more of a shared team artifact.

Core flow metrics, cycle time, throughput, and WIP, without extra clutter
Retro-ready reports formatted for live discussion during sprint review
Direct Linear and Git integration for pulling issue and commit data together
A simplified dashboard set that avoids the configuration overhead of larger platforms

The narrower feature set is also the limitation: Axify doesn't do risk scoring, investment reporting, or developer experience tracking, so teams that need those will outgrow it quickly. Setup is straightforward and typically handled by whoever runs the team's retros, often a team lead or EM. Pricing is tiered and quote-based as of 2026; see the website for current rates.

Best for: Teams that want a simple, retro-friendly flow-metrics layer without heavy configuration.

8. Screenful

Screenful is a dashboard and reporting tool built around a direct Linear connector, generating burndown, cumulative flow, and cycle time charts without custom setup. It's aimed at teams whose main need is clean, shareable visuals rather than deep diagnostic analysis.

Its standout feature is automated scheduled reporting: Screenful can email stakeholders recurring summaries built from Linear data automatically, removing the manual export-and-send routine that eats time before every stakeholder update.

A direct Linear connector that works without custom configuration
Burndown and cumulative flow charts for standard sprint reporting
Scheduled report emails that keep stakeholders updated with no manual work
Shareable dashboard links for anyone who needs read-only access

Screenful is a pure visualization layer. It has no code-level analysis, no risk scoring, and no developer experience tracking, so teams that need to diagnose why delivery is slipping will need to pair it with something else or look elsewhere on this list. It also integrates with Jira and GitHub alongside Linear, and setup is light enough that a single team lead or product manager can run it without engineering support. Pricing is tiered, starting at a monthly rate as of 2026; see the website for current plans.

Best for: Teams that mainly need clean, shareable Linear charts for stakeholder updates rather than deep diagnostics.

Matching the tool to your team's stage

If you're just getting your workflow settled in Linear, start with the native Insights views: they're free with your plan and enough to spot obvious trends. Once you need to know why an initiative slipped or whether a release is riskier than usual, Progress is built for that gap, turning Linear and GitHub activity into risk, momentum, and morale signals instead of another set of charts to puzzle over. Teams that already run on DORA metrics and want enforcement, not just measurement, should look at Swarmia or LinearB, with LinearB the better fit once you're benchmarking multiple squads. If your real audience is finance or the board, Jellyfish's investment allocation reporting is worth the heavier setup. Mixed-stack teams juggling Jira and Slack alongside Linear should look at Hatica for its developer experience layer, while teams that just want lightweight, visual flow metrics for retros or stakeholder updates will find Axify or Screenful sufficient.

Most teams end up combining a lightweight visualization tool with something that does the interpretation for them, since dashboards alone tend to get built once and ignored. Learn more about our services to see how Progress turns your existing Linear and GitHub activity into signals your team can act on.

8 Best Linear Integration Analytics Tools for Engineering Teams in 2026