Understanding Circular Dependencies and How to Fix Them

What Is a Circular Dependency?

A circular dependency occurs when two or more modules depend on each other, directly or indirectly. Module A imports from Module B, and Module B imports from Module A. Or, in more complex cases, Module A depends on B, B depends on C, and C depends back on A.

In small codebases, circular dependencies may not cause immediate problems. The code might build, the tests might pass and everything appears fine. But as the codebase grows, these cycles become a source of subtle, frustrating issues that are disproportionately expensive to fix later.

How Circular Dependencies Happen

Circular dependencies rarely appear by design. Nobody sets out to create a cycle. They emerge gradually, often through reasonable decisions that each make sense in isolation.

A common pattern is mutual convenience. A user module needs to reference orders, and an order module needs to reference users. Rather than introducing a shared interface or restructuring the relationship, a developer imports directly from the other module. The cycle is born.

Another common cause is utility creep. A utility module starts small and focused. Over time, developers add functions that depend on domain-specific modules. Those domain modules already import from the utility module, and now the dependency runs both ways.

Feature growth also contributes. A notification module is created that depends on the user module. Later, someone adds a "notify on registration" feature that requires the user module to import from the notification module. Each change is small and logical. The cycle only becomes apparent when the chain is examined as a whole.

Why They Cause Problems

Build and runtime failures

Many module systems resolve imports in a specific order. When a cycle exists, one of the modules in the chain will be partially initialised when another module tries to use it. In Node.js, this can result in receiving an empty object or undefined where you expected a function. In bundlers like Webpack or Rollup, cycles can cause incorrect tree-shaking or runtime errors that are difficult to trace back to their root cause.

These failures are particularly insidious because they are intermittent. The order in which modules are loaded can vary between environments, between builds, or even between runs. A test suite might pass locally but fail in CI, with no obvious explanation.

Testing difficulty

Circular dependencies make isolated testing significantly harder. If Module A depends on Module B, and Module B depends on Module A, you cannot test either module in isolation without mocking the other. This creates brittle tests that are tightly coupled to implementation details and break whenever either module changes.

In practice, teams working with circular dependencies often resort to testing at a higher integration level, which is slower, less precise and harder to debug when failures occur. The feedback loop lengthens, and developer confidence in the test suite decreases.

Tight coupling

The most fundamental problem with circular dependencies is that they indicate tight coupling. When two modules depend on each other, they cannot evolve independently. A change to one module's interface forces a change in the other. Refactoring one requires understanding both. Deploying one requires deploying both.

This coupling spreads. Modules that depend on either side of the cycle become transitively coupled to both. Over time, what should be independent components become a tangled cluster where changing anything requires understanding everything.

How to Detect Them

Static analysis tools

Most modern ecosystems have tools that can detect circular dependencies automatically. ESLint has the import/no-cycle rule. Madge is a standalone tool that analyses JavaScript and TypeScript import graphs and reports cycles. For other languages, similar tools exist: JDepend for Java, pylint for Python, and cargo-geiger for Rust.

These tools work by building a directed graph of module imports and searching for cycles within it. They are fast, deterministic and can run as part of a CI pipeline to prevent new cycles from being introduced.

Dependency graphs

Visualising the dependency graph of a codebase can reveal cycles that tools miss or that span multiple layers. Tools like Madge can generate visual graphs. Even a manual exercise of drawing module relationships on a whiteboard can expose surprising connections.

The value of visualisation is that it shows not just the cycle itself but its context. You can see which modules are affected, how deep the coupling runs and where the most natural break point might be.

Import analysis in CI

The most effective detection strategy is automated enforcement. Add a CI check that fails when a circular dependency is introduced. This prevents new cycles from entering the codebase and creates a forcing function for developers to resolve the issue before merging.

Strategies for Breaking Cycles

Extract shared code into a third module

The most common fix for a circular dependency between Module A and Module B is to identify the shared concept that both modules need and extract it into a new Module C. Both A and B depend on C, but neither depends on the other.

For example, if a user module and an order module both need a UserId type, extract that type into a shared types module. If both modules need a validation function, extract it into a shared validation module. The key is that Module C must not depend on either A or B.

Use dependency inversion

Instead of Module A importing directly from Module B, define an interface in Module A that describes what it needs. Module B then implements that interface. The dependency direction is reversed: B depends on A's interface rather than A depending on B's implementation.

This is particularly effective when the cycle exists because a lower-level module needs to call back into a higher-level one. Rather than importing the higher-level module, the lower-level module accepts a callback or interface that the higher-level module provides at runtime.

Use events or message passing

When two modules need to communicate but should not depend on each other, an event system can break the cycle. Module A emits an event. Module B listens for it. Neither module imports the other. A lightweight event bus or pub/sub mechanism serves as the intermediary.

This approach is particularly well suited to notifications, logging and analytics, where the producing module should not know or care about the consuming module.

Restructure module boundaries

Sometimes a circular dependency reveals that the module boundaries are wrong. Two modules that depend on each other may actually be one cohesive concept that was split prematurely. Merging them into a single module, or reorganising the boundaries so that the dependency flows in one direction, can be the cleanest fix.

This requires stepping back and asking: do these modules represent genuinely independent concepts, or are they two halves of the same thing?

Prevention Is Cheaper Than Cure

Fixing circular dependencies in a mature codebase is expensive. The refactoring required can span many files, break existing tests and introduce regressions. It is far cheaper to prevent them in the first place.

A few practices help:

Enforce direction. Establish a clear dependency direction for your codebase. Higher-level modules depend on lower-level modules, never the reverse. Document this convention and enforce it with tooling.

Review imports. During code reviews, pay attention to import statements. A new import that reaches across module boundaries in an unexpected direction is a signal worth investigating.

Run automated checks. Add circular dependency detection to your CI pipeline. Make it a blocking check, not a warning. Warnings are ignored. Blocking checks are addressed.

Keep modules focused. Modules with a single, clear responsibility are less likely to develop cycles. When a module starts to grow broad, it begins importing from many places, and those places begin importing back.

Circular dependencies are not a catastrophic failure. They are a gradual degradation. Each one makes the codebase slightly harder to understand, slightly harder to test and slightly harder to change. The cumulative effect, over months and years, is a codebase that resists modification and punishes change.

Detecting and resolving them early is one of the most effective investments a team can make in long-term code health.