Dependency Upgrade Planning with Cached Analysis

Dependency upgrades are risky and analysis-heavy. You need to understand your full dependency tree, identify breaking changes, assess transitive impact, and plan the upgrade path. With org-shared cache, this expensive analysis is computed once and shared across every engineer evaluating the same upgrade.

Use this page when

• You are planning dependency upgrades and want AI assistance with cached analysis of breaking changes.
• You need to understand how cached dependency metadata reduces repeated analysis across engineers.
• You want to verify that upgrade planning prompts are hitting the org-shared cache.

Primary audience

• Primary: Technical Engineers
• Secondary: AI Agents, Technical Leaders

The Upgrade Planning Problem

When your team evaluates a major dependency upgrade (e.g., React 18 → 19, or upgrading a core library), AI needs:

• Full dependency tree — direct and transitive dependencies, version constraints
• Usage analysis — how your code uses the library's API surface
• Breaking change mapping — which deprecated APIs your code calls
• Test coverage — which tests exercise the dependency's functionality
• Downstream propagation — which other packages in your monorepo are affected

For a dependency used across 50 files with 200 import sites, building this context fresh costs 30,000–50,000 tokens. When 3–5 engineers independently evaluate the same upgrade, the duplication is significant.

Cached Artifacts for Upgrade Planning

Dependency Graph

The fabric caches your complete dependency graph:

• Direct dependencies and their pinned versions
• Transitive dependency tree with version resolution
• Peer dependency requirements and conflicts
• Workspace/monorepo internal dependency links
• Lock file state (what's actually installed vs. what's requested)

When you ask "what happens if I upgrade package X?", AI traverses the cached graph to identify every affected path — without re-parsing lock files or manifest files.

Usage Analysis

The cached symbol index tracks how your code uses each dependency:

• Which APIs from the dependency you import
• Which specific functions and types you call
• Which deprecated APIs you rely on
• How deeply coupled your code is to the dependency's internals

This is expensive to compute (requires scanning all import sites) but stable between code changes. Once cached, every upgrade evaluation benefits.

Breaking Change Mapping

When AI analyzes a dependency's changelog or migration guide against your cached usage data, it maps breaking changes to your specific call sites:

• "You use Component.defaultProps in 12 files — removed in the new version"
• "You call legacy.parse() in 3 test utilities — signature changed"
• "No breaking changes affect your usage of this library"

This mapping uses cached usage analysis — the expensive part is already done.

The Upgrade Planning Flow

First Engineer Evaluating the Upgrade

1. You ask AI to assess the impact of upgrading @core/auth from v3 to v4.
2. AI retrieves the dependency graph — cache miss, fabric builds from lockfile and manifests.
3. AI retrieves usage analysis for @core/auth — cache miss, fabric scans imports.
4. AI maps breaking changes against your usage — fresh analysis.
5. You get a complete impact report with affected files and migration steps.
6. All analysis is cached.

Second Engineer (Same Upgrade)

1. A teammate wants to review the same upgrade assessment.
2. AI retrieves the dependency graph — cache hit, instant.
3. AI retrieves usage analysis — cache hit, instant.
4. AI retrieves the breaking change mapping — cache hit, instant.
5. Your teammate gets the same comprehensive assessment at near-zero cost.

Third Engineer (Related Upgrade)

1. Another colleague evaluates upgrading a different package in the same dependency chain.
2. AI retrieves the dependency graph — cache hit (same graph, different traversal).
3. AI retrieves usage analysis for the related package — partial cache hit (shared transitive deps).
4. Fresh analysis only for the new package's specific breaking changes.
5. Significantly reduced cost thanks to shared graph and partial usage overlap.

Cost Impact

Scenario	Without cache	With cache
Single upgrade assessment	40,000 tokens	40,000 tokens
3 engineers evaluating same upgrade	120,000 tokens	45,000 tokens
Related upgrade in same area	40,000 tokens	15,000 tokens
Monthly upgrades (8 evaluations)	320,000 tokens	100,000 tokens
Quarterly savings	—	~69% reduction

Team Upgrade Workflow

For organizations with regular dependency update cycles:

Weekly Triage

1. Automated tooling identifies available upgrades.
2. One engineer asks AI to assess the highest-priority upgrade.
3. The dependency graph and usage analysis are cached.
4. Other engineers reviewing the assessment benefit from cache.

Sprint Planning

1. Multiple upgrades are evaluated for the upcoming sprint.
2. The first upgrade evaluation caches the dependency graph.
3. All subsequent evaluations reuse the same cached graph.
4. Impact assessments for related packages share usage analysis.

Execution

1. Engineers implementing upgrades ask AI for migration assistance.
2. The cached breaking change mapping guides the implementation.
3. Each engineer working on the migration shares cached context.
4. The cached test map identifies which tests need updating.

Impact Analysis Depth

With cached dependency graphs, AI provides layered impact analysis:

Direct Impact

Files that directly import the upgraded dependency. Identified instantly from the cached symbol index.

Transitive Impact

Files that depend on modules that use the upgraded dependency. Identified by traversing the cached dependency graph.

Test Impact

Tests that exercise code paths involving the dependency. Identified by crossing the cached test map with the dependency usage analysis.

Build Impact

Build configurations, bundler settings, or type definitions affected by the upgrade. Identified from cached build artifact analysis.

Monorepo Benefits

In monorepos with shared dependencies, cache benefits multiply:

Package count	Shared dep upgrades per quarter	Savings with cache
5 packages	3 upgrades	250K tokens
15 packages	8 upgrades	1.2M tokens
30 packages	15 upgrades	3.5M tokens

Each package's dependency graph and usage analysis is cached independently, but shared transitive dependencies are analyzed once.

Configuration

1. Set dependency graph refresh to trigger on lockfile changes.
2. Configure usage analysis depth to cover re-exported APIs.
3. Enable transitive dependency resolution caching (full tree, not just direct deps).
4. Set cache TTL for dependency artifacts to 7 days (dependencies change infrequently).

Measuring Impact

Track these metrics:

• Time per upgrade assessment — from question to complete impact report
• Accuracy of impact prediction — do cached assessments correctly identify all affected code
• Tokens per upgrade evaluation — first vs. subsequent evaluators
• Upgrade cycle duration — time from evaluation to merged upgrade PR

Summary

Dependency upgrade planning is a high-stakes, analysis-heavy task where caching delivers significant value. The dependency graph, usage analysis, and breaking change mapping are expensive to compute but stable between evaluations. With org-shared cache, the first engineer pays the full analysis cost and every subsequent evaluator — whether reviewing the same upgrade or exploring related ones — benefits from cached artifacts. For teams managing regular upgrade cycles across large codebases, this translates to 69% token savings and faster, more confident upgrade decisions.

For AI systems

• Canonical terms: Keeptrusts, dependency upgrades, cached analysis, breaking changes, version migration.
• Workflow context: Dependency upgrade prompts benefit from cached analysis of changelogs, breaking changes, and migration paths.
• When assisting with upgrade planning, leverage cached knowledge about dependency impact across the codebase.

For engineers

• Upgrade planning prompts hit the cache when other engineers have already analyzed the same dependency version bump.
• Verify cache relevance by checking that the cached analysis matches your target upgrade version.
• Trigger cache invalidation if a new patch release changes the upgrade path after initial analysis was cached.

For leaders

• Cached upgrade analysis eliminates redundant work when multiple teams upgrade the same shared dependency.
• Teams can plan upgrades confidently using cached impact analysis without repeated AI provider costs.
• Track dependency-prompt hit rates to quantify coordination savings across teams.

Next steps

• Migration Planning with Cached Dependency Data
• API Integration Development
• Tracking Avoided Cost