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

{
The right issue is harder because it requires tracing and safely correcting an existing proxy interception flow, preserving tool-call semantics across provider translations, and adding regression coverage for multiple request paths. The left issue is comparatively bounded: an OpenAI-compatible provider mainly needs provider registration, configuration, model metadata, and integration tests.
}
https://github.com/berriai/litellm/issues/31902 5:3 https://github.com/berriai/litellm/issues/27860

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

{
#32068 requires cross-layer work across proxy streaming, cache serialization/deserialization, replay semantics, and regression coverage, while #32900 is comparatively localized to wizard/provider configuration and validation.
}
https://github.com/berriai/litellm/issues/32068 4:1 https://github.com/berriai/litellm/issues/32900

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

{
The right issue is harder because it spans dashboard form behavior, new proxy/API integration, remote capability discovery, fallback handling, and user-visible error states. The left issue is primarily provider routing and embedding support/error-path coverage with narrower backend and test changes.
}
https://github.com/berriai/litellm/issues/30512 3:2 https://github.com/berriai/litellm/issues/31894

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

{
The right issue is harder because it requires safely changing persistence-layer behavior and session reconstruction under large-scale workloads, with greater compatibility, performance, and regression risk. The left issue is a narrower request-path integration fix with a more localized validation surface.
}
https://github.com/berriai/litellm/issues/33666 5:1 https://github.com/berriai/litellm/issues/35599

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

{
The right issue is harder because it requires careful handling of cross-request data propagation in an authentication workflow, validation across multiple configuration fields, and regression coverage. The left issue appears to be a localized logging cleanup with limited behavioral risk.
}
https://github.com/berriai/litellm/issues/20495 5:1 https://github.com/berriai/litellm/issues/23879

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

{
The left issue is harder because it likely spans provider integration, model metadata, capability detection, and proxy validation across several variants. The right appears more localized to diagnosing and correcting one provider's streaming behavior.
}
https://github.com/berriai/litellm/issues/29675 3:2 https://github.com/berriai/litellm/issues/29995

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

{
The right-hand task is harder because it requires tracing request-header propagation across MCP generation, OpenAPI handling, and backend invocation, while preserving existing authentication behavior and adding targeted regression coverage. The left-hand task is comparatively bounded dependency remediation, though it still carries security and compatibility validation risk.
}
https://github.com/berriai/litellm/issues/33344 3:2 https://github.com/berriai/litellm/issues/30833

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

{
The right issue is harder because it requires correcting deployment identity propagation across several video request paths, handling fallback behavior, and adding regression coverage for multi-deployment routing. The left issue is comparatively localized catalog/configuration work with limited behavioral risk.
}
https://github.com/berriai/litellm/issues/33740 5:1 https://github.com/berriai/litellm/issues/32650

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

{
Fixing the telemetry issue requires tracing exporter behavior, normalizing nullable structured data without breaking semantic conventions, and adding regression coverage across streaming and provider variants. The model-price request is a narrowly scoped dual-file metadata update with limited implementation risk.
}
https://github.com/berriai/litellm/issues/32996 5:1 https://github.com/berriai/litellm/issues/30430

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

{
The right issue requires debugging and changing streaming transformation behavior, handling stateful edge cases, and adding regression coverage across multiple integration paths. The left issue is a localized configuration-data update with comparatively low implementation risk.
}
https://github.com/berriai/litellm/issues/33678 5:1 https://github.com/berriai/litellm/issues/31692