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

{
#29066 is harder because it crosses persistence, Pydantic modeling, and the request authorization path, with broader regression risk; #35214 is a relatively localized provider-translation fix with narrower testing scope.
}
https://github.com/berriai/litellm/issues/29066 3:1 https://github.com/berriai/litellm/issues/35214

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

{
The left requires coordinated defensive handling across multiple response-processing layers, with streaming, logging, translation, and regression-test risks. The right is primarily an external benchmark-validation and configuration-review discussion with little or no implementation scope.
}
https://github.com/berriai/litellm/issues/34754 5:1 https://github.com/berriai/litellm/issues/34441

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

{
Bayesian optimization is a broad cross-cutting feature requiring objective definition, configuration search orchestration, persistence, provider/model integration, cost and latency evaluation, safety controls, and substantial testing. The logging fix is comparatively localized to payload sizing, truncation, or batching around an existing integration.
}
https://github.com/berriai/litellm/issues/30448 8:1 https://github.com/berriai/litellm/issues/26450

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

{
The left task requires coordinated changes across synchronous and asynchronous streaming state machines, careful event-order preservation, and regression coverage for boundary conditions. The right task is comparatively localized, with straightforward exception-propagation and focused tests.
}
https://github.com/berriai/litellm/issues/25214 3:1 https://github.com/berriai/litellm/issues/34299

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

{
The right issue is harder because it requires provider capability metadata changes, compatibility validation, and regression coverage across Databricks request handling. The left issue is comparatively narrow, focused on recognizing or forwarding a single parameter.
}
https://github.com/berriai/litellm/issues/34143 3:1 https://github.com/berriai/litellm/issues/32656

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

{
32574 is harder because it involves coordinating retry/fallback state, shared logging lifecycle, callback semantics, and regression coverage across multiple execution paths; 29397 is comparatively localized to persisted-state reconstruction and validation.
}
https://github.com/berriai/litellm/issues/32574 3:1 https://github.com/berriai/litellm/issues/29397

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

{
Provider/model-specific throttling spans configuration, request scheduling, shared rate-limit state, concurrency, retries, and multiple provider/model routing paths. Correcting failed-request accounting is comparatively narrower, mainly requiring consistent quota updates across proxy success and error flows.
}
https://github.com/berriai/litellm/issues/32620 3:1 https://github.com/berriai/litellm/issues/21312

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

{
The right issue is harder because it requires coordinated schema, migration, data-integrity, and database-behavior changes with production-scale cleanup and compatibility risk. The left issue is narrower, primarily involving exception semantics across existing async error-handling paths and targeted regression tests.
}
https://github.com/berriai/litellm/issues/34232 5:4 https://github.com/berriai/litellm/issues/22100

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

{
The right issue is harder because it spans multiple request pipelines, persistence behavior, configuration propagation, and regression coverage, with added privacy and data-integrity risks. The left issue is more localized to model-list aggregation and access-control metadata handling.
}
https://github.com/berriai/litellm/issues/34747 5:3 https://github.com/berriai/litellm/issues/31438

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

{
The right issue is harder because it requires tracing a regression across message normalization, provider-specific identifier constraints, multi-turn state preservation, and compatibility tests, with risk of affecting existing Anthropic integrations. The left issue is comparatively localized: expose provider capability metadata through a small, stable API with focused tests.
}
https://github.com/berriai/litellm/issues/32214 5:1 https://github.com/berriai/litellm/issues/26724