Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
The left issue requires diagnosing and safely changing asynchronous shutdown behavior across worker lifecycle boundaries, with database-driver error handling, concurrency, regression testing, and deployment-specific validation. The right issue is primarily a contained metadata update with straightforward schema and verification work.
}
https://github.com/berriai/litellm/issues/26619 8:1 https://github.com/berriai/litellm/issues/28306

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
The right issue is harder because it requires tracing the telemetry integration, choosing a compatible attribute-serialization strategy, and validating behavior across instrumentation paths and SDK constraints. The left issue is primarily a bounded metadata update with focused validation.
}
https://github.com/berriai/litellm/issues/24057 4:1 https://github.com/berriai/litellm/issues/24202

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
Issue 26507 is harder because it requires careful identifier-normalization logic across tool matching paths, compatibility handling, and regression coverage, while 22753 is more localized alert-state and deduplication behavior.
}
https://github.com/berriai/litellm/issues/26507 3:1 https://github.com/berriai/litellm/issues/22753

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
The right-side work is harder because it requires defensive handling across a streaming translation path, careful preservation of protocol behavior, and broader regression coverage. The left-side work appears more localized to model capability registration and validation.
}
https://github.com/berriai/litellm/issues/30761 3:1 https://github.com/berriai/litellm/issues/29322

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
The UI change has broader frontend mapping and fallback implications across model naming conventions and provider configurations, while the proxy change is comparatively localized to request-context capture in an error path. The UI work is therefore moderately harder, though neither requires major architectural changes.
}
https://github.com/berriai/litellm/issues/35095 3:2 https://github.com/berriai/litellm/issues/33150

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
31118 is harder because it requires a cross-cutting security fix in shared request construction, preserving provider-specific behavior while adding authorization-consistency checks and broad regression coverage. 27926 is comparatively localized to proxy metrics authentication and configuration handling.
}
https://github.com/berriai/litellm/issues/31118 5:1 https://github.com/berriai/litellm/issues/27926

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
The right issue is substantially harder because it requires designing and integrating a general optimization system, including statistical modeling, experimentation, metric collection, safety controls, and a production-facing workflow. The left issue is comparatively bounded: adding validated configuration, propagating it through budget-reset logic, and covering timezone, persistence, and compatibility cases.
}
https://github.com/berriai/litellm/issues/30448 8:1 https://github.com/berriai/litellm/issues/33161

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
The left issue is harder because it likely spans Vertex-specific endpoint routing, model translation, authentication, and compatibility testing across deployment modes. The right issue is more contained: tracing one identity value through an existing MCP lifecycle with focused regression coverage.
}
https://github.com/berriai/litellm/issues/22965 3:2 https://github.com/berriai/litellm/issues/31609

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
Concurrent state mutation requires careful synchronization, compatibility review, and reliable race-condition regression tests across multiple execution paths, making it substantially riskier than exposing or clarifying a single configuration option in the UI.
}
https://github.com/berriai/litellm/issues/34719 4:1 https://github.com/berriai/litellm/issues/35064

Sorting LiteLLM GitHub issues by difficulty to implement. Higher = harder.

{
Lakera requires cross-cutting guardrail configuration handling, hook-path integration, and regression coverage across message types and execution modes; the fallback endpoint fix is comparatively localized to routing semantics with focused API tests.
}
https://github.com/berriai/litellm/issues/34396 3:1 https://github.com/berriai/litellm/issues/32770