CrewAI vs LangGraph: Which One to Choose

Evidence shows: AGENTS.md provides only contribution guidelines, with no mention of permission management, user confirmation, data flow transparency, or sensitive data handling. For dependency security, pyproject.toml and .pre-commit-config.yaml show dependency overrides and pip-audit usage, but with a large ignore list and no mitigation details. External effects, rollback, and source attribution are not addressed in AGENTS.md. Deductions: lack of evidence for least privilege, user confirmation, data flow transparency, sensitive data handling, external effects, and rollback; dependency security deducted due to ignore list and lack of mitigation details; source attribution only partially evidenced via author info.

Evidence shows no documentation of permission model, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust criteria are unmet, hence score 0.

Evidence shows: AGENTS.md guidelines are consistent with repository structure, but contain typos (e.g., 'behaivor'). Dependency availability: pyproject.toml specifies Python version range and dependency overrides, but no availability guarantees. Failure messages: AGENTS.md provides no error handling or failure message guidance. Deductions: self-consistency deducted for typos; dependency availability deducted for lack of guarantees; failure messages deducted for absence.

Self-consistency: README describes low-level orchestration framework, consistent with codebase structure, but not deeply verified. Dependency availability: pip install and dependency management provided, but no lock file or availability guarantee. Failure messages: No documentation of error handling or failure messages.

Evidence shows: AGENTS.md targets contributors, providing documentation translation and code contribution guidelines, but does not explicitly define target audience or use cases. Capability boundaries are not clearly stated. Trigger precision: guidelines do not define trigger conditions. Environment fit: Python version and installation instructions provided, but not covering all environments. Deductions: audience and scenarios deducted for lack of explicit definition; capability boundaries and trigger precision deducted for absence; environment fit deducted for incomplete coverage.

Audience and scenarios: Clearly targets developers, provides multiple use cases. Capability boundaries: Describes functional scope but no explicit limitations. Trigger precision: No trigger mechanism or precise conditions. Environment fit: Supports Python and JS, but no detailed environment requirements.

Evidence shows: AGENTS.md provides information architecture (e.g., docs structure), installation notes in README, naming stability not explicit, examples and FAQ in README, known limitations not mentioned, license is MIT, versioning via commitizen and changelog config, maintenance responsibility not explicit. Deductions: naming stability, known limitations, and maintenance responsibility deducted for absence; versioning deducted for config presence but not explicit.

Information architecture: Clear documentation structure with navigation. Install notes: Provides pip install command. Naming stability: No naming conventions or stability guarantees. Examples and FAQ: Provides examples and links, but no FAQ. Known limitations: Not explicitly listed. License: MIT license, clear. Versioning and changelog: Version badge present, but no changelog. Maintenance responsibility: Maintained by LangChain Inc, but unverified.

Evidence shows: Output usability: README provides examples and output file instructions, but no explicit output format. Marginal value: framework offers multi-agent orchestration, but no comparison with alternatives. Cost-benefit: no performance or resource usage data. Deductions: output usability deducted for lack of explicit format; marginal value deducted for lack of comparison; cost-benefit deducted for lack of data.

Output usability: API reference and docs provided, but actual output not verified. Marginal value: As a framework, provides unique features, but not quantified. Cost-benefit: No performance or cost data.

Evidence shows: Claim traceability: README claims lack specific evidence. Cross-source corroboration: no external validation provided. Fact-inference separation: not explicitly distinguished. Deductions: all sub-criteria deducted for lack of evidence.

Claim traceability: Claims in README have doc links, but not verified. Cross-source corroboration: Multiple doc sources, but not independently verified. Fact-inference separation: No clear separation of facts and inferences.