microsoft/superbenchmark Graph

Graph Summary

microsoft/superbenchmark graph connects 3 alternatives, 8 related projects, 1 inferred dependencies, 3 deployment targets, and 3 use cases.

Nodes50

Edges415

Projects37

Dependencies35

Open project knowledge Find alternatives Explore related projects Explain score Compare shortlist

Alternatives3 linked signals Related8 linked signals Dependencies1 linked signals Deployments3 linked signals Use Cases3 linked signals Categories28 linked signals Migrations3 linked signals

Project Context

superbenchmark

Maintainermicrosoft

LicenseMIT

LanguagePython

Recent activityActive in the last week

Deployment Targets

Dependencies

Knowledge Graph

50 nodes / 415 edges

microsoft/superbenchmark ↔ samugit83/redamonUse samugit83/redamon when the user needs a llm eval project with docker, local, cloud deployment options.

microsoft/superbenchmark ↔ hugalafutro/model-hotelUse hugalafutro/model-hotel when the user needs a llm eval project with docker, local, cloud deployment options.

microsoft/superbenchmark ↔ dataelement/bishengUse dataelement/bisheng when the user needs a llm eval project with docker, local, cloud deployment options.

microsoft/superbenchmark ↔ promptfoo/promptfooUse promptfoo/promptfoo when the user needs a llm eval project with docker, library-only, local deployment options.

microsoft/superbenchmark ↔ modelscope/evalscopeUse modelscope/evalscope when the user needs a llm eval project with docker, library-only, local deployment options.

microsoft/superbenchmark ↔ EricLBuehler/mistral.rsUse EricLBuehler/mistral.rs when the user needs a llm eval project with docker, kubernetes, library-only deployment options.

Alternatives Network

Replacement candidates

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microsoft/superbenchmark ↔ comet-ml/opikUse comet-ml/opik when the user needs a llm eval project with docker, kubernetes, library-only deployment options.

microsoft/superbenchmark ↔ confident-ai/deepevalUse confident-ai/deepeval when the user needs a llm eval project with library-only, local, cloud deployment options.

microsoft/superbenchmark ↔ Arize-ai/phoenixUse Arize-ai/phoenix when the user needs a llm eval project with docker, vercel, serverless deployment options.

microsoft/superbenchmark ↔ modelscope/evalscopeUse modelscope/evalscope when the user needs a llm eval project with docker, library-only, local deployment options.

microsoft/superbenchmark ↔ NVIDIA/garakUse NVIDIA/garak when the user needs a llm eval project with library-only, local, cloud deployment options.

microsoft/superbenchmark ↔ ml-explore/mlx-lmUse ml-explore/mlx-lm when the user needs a llm eval project with library-only, local, cloud deployment options.

Migration Paths

What to verify before switching

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These paths are evidence-backed heuristics, not drop-in compatibility claims.

microsoft/superbenchmark -> comet-ml/opikCompatibility: high / estimated cost: medium.Shared: category:llm_eval, deployment:docker, deployment:local, deployment:cloud, use_case:evaluate LLM outputs, use_case:benchmark prompts and agents, use_case:track model quality, dependency:LLM provider.Gaps: License changes from MIT to Apache-2.0.Validate: Review license obligations before migrating production code. Compare API, configuration, license, and dependency requirements. Run the target project's minimal example or test suite. Verify deployment, persistence, and tool-execution behavior in the requested runtime.

microsoft/superbenchmark -> confident-ai/deepevalCompatibility: high / estimated cost: medium.Shared: category:llm_eval, deployment:local, deployment:cloud, use_case:evaluate LLM outputs, use_case:benchmark prompts and agents, use_case:track model quality, dependency:LLM provider, language:Python.Gaps: Deployment targets not listed by target: docker. License changes from MIT to Apache-2.0.Validate: Review license obligations before migrating production code. Compare API, configuration, license, and dependency requirements. Run the target project's minimal example or test suite. Verify deployment, persistence, and tool-execution behavior in the requested runtime.

microsoft/superbenchmark -> Arize-ai/phoenixCompatibility: high / estimated cost: medium.Shared: category:llm_eval, deployment:docker, deployment:local, use_case:evaluate LLM outputs, use_case:benchmark prompts and agents, use_case:track model quality, dependency:LLM provider, language:Python.Gaps: Deployment targets not listed by target: cloud. License changes from MIT to NOASSERTION.Validate: Review license obligations before migrating production code. Compare API, configuration, license, and dependency requirements. Run the target project's minimal example or test suite. Verify deployment, persistence, and tool-execution behavior in the requested runtime.

Use Cases