Project Graph

confident-ai/deepeval

A relationship view of alternatives, deployments, compatible protocols, dependencies, use cases, and categories.

Graph Summary

confident-ai/deepeval graph connects 3 alternatives, 8 related projects, 1 inferred dependencies, 3 deployment targets, and 3 use cases.

Nodes52
Edges417
Projects39
Dependencies34

Project Context

deepeval

Maintainerconfident-ai
LicenseApache-2.0
LanguagePython
Recent activityActive in the last week

Deployment Targets

library_onlylocalcloud

Dependencies

LLM provider

Knowledge Graph

52 nodes / 417 edges

confident-ai/deep… focus llm eval category library_only deployment local deployment cloud deployment evaluate LLM … use case benchmark pro… use case track model q… use case LLM provider dependency promptfoo project opik project phoenix project giskard-oss project lighteval project langfuse project

Migration Paths

What to verify before switching

JSON

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

confident-ai/deepeval -> promptfoo/promptfooCompatibility: medium / estimated cost: medium.Shared: category:llm_eval, deployment:library_only, deployment:local, deployment:cloud, use_case:evaluate LLM outputs, use_case:benchmark prompts and agents, use_case:track model quality, dependency:LLM provider.Gaps: Language changes from Python to TypeScript. License changes from Apache-2.0 to MIT.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.
confident-ai/deepeval -> comet-ml/opikCompatibility: high / estimated cost: low.Shared: category:llm_eval, deployment:library_only, deployment:local, deployment:cloud, use_case:evaluate LLM outputs, use_case:benchmark prompts and agents, use_case:track model quality, dependency:LLM provider.Gaps: No indexed gap; still run the validation steps.Validate: 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.
confident-ai/deepeval -> Arize-ai/phoenixCompatibility: high / estimated cost: medium.Shared: category:llm_eval, deployment:library_only, 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 Apache-2.0 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

evaluate LLM outputsbenchmark prompts and agentstrack model quality

Categories

llm eval

Alternatives

promptfoo/promptfoocomet-ml/opikArize-ai/phoenixhuggingface/lightevallangfuse/langfusemodelscope/evalscope

Related Projects

promptfoo/promptfooGiskard-AI/giskard-osshuggingface/lightevalmodelscope/evalscopelangfuse/langfusetruera/trulens