Nuanced
Nuanced: structured call graphs for more accurate LLM code
LLMs are powerful, but they guess. They hallucinate function calls, miss dependencies, and struggle with cross-file reasoning. Nuanced fixes that.
Nuanced is a local code analysis tool that builds structured call graphs—giving both developers and LLMs a precise understanding of how code actually behaves. By modeling real control flow and function relationships, it helps reduce hallucinations, surface broken dependencies, and improve AI-generated code reviews, test cases, and refactors.
Unlike language servers (LSPs), which index symbols statically, Nuanced captures execution paths—showing what calls what, and under what conditions. That deeper structure makes AI outputs more reliable and grounded.
Learn why we chose call graphs over LSPs: Why we chose call graphs over LSPs
Whether you’re building an AI pair programmer, analyzing a PR, or just trying to get better test coverage, Nuanced gives your tools the structure they’re missing.
Key Capabilities
- Call Graph Generation: Index entire codebases and generate cross-file call graphs that reflect real control flow.
- Function Enrichment: Get a structured view of a specific function’s callees, file location, and behavior metadata.
- Change Impact Analysis: Identify what functions and files would be affected by modifying a given function.
- Built for AI coding: Nuanced outputs structured JSON designed for direct use in prompts for test generation, code review, summarization, and refactoring.
- Local Execution: All analysis happens offline. Your code never leaves your machine.
- CLI and Programmatic Access: Use Nuanced from the terminal or embed it into a larger dev or AI workflow.
Language Support
- Nuanced Python: Fully supported via the Nuanced CLI and library. Available in both free and paid tiers.
- Nuanced TypeScript: Now available via a private NPM package in the paid tier.
More language support is coming soon. We’re actively expanding to meet the needs of multi-language codebases and AI workflows.
Nuanced
Nuanced: structured call graphs for more accurate LLM code
LLMs are powerful, but they guess. They hallucinate function calls, miss dependencies, and struggle with cross-file reasoning. Nuanced fixes that.
Nuanced is a local code analysis tool that builds structured call graphs—giving both developers and LLMs a precise understanding of how code actually behaves. By modeling real control flow and function relationships, it helps reduce hallucinations, surface broken dependencies, and improve AI-generated code reviews, test cases, and refactors.
Unlike language servers (LSPs), which index symbols statically, Nuanced captures execution paths—showing what calls what, and under what conditions. That deeper structure makes AI outputs more reliable and grounded.
Learn why we chose call graphs over LSPs: Why we chose call graphs over LSPs
Whether you’re building an AI pair programmer, analyzing a PR, or just trying to get better test coverage, Nuanced gives your tools the structure they’re missing.
Key Capabilities
- Call Graph Generation: Index entire codebases and generate cross-file call graphs that reflect real control flow.
- Function Enrichment: Get a structured view of a specific function’s callees, file location, and behavior metadata.
- Change Impact Analysis: Identify what functions and files would be affected by modifying a given function.
- Built for AI coding: Nuanced outputs structured JSON designed for direct use in prompts for test generation, code review, summarization, and refactoring.
- Local Execution: All analysis happens offline. Your code never leaves your machine.
- CLI and Programmatic Access: Use Nuanced from the terminal or embed it into a larger dev or AI workflow.
Language Support
- Nuanced Python: Fully supported via the Nuanced CLI and library. Available in both free and paid tiers.
- Nuanced TypeScript: Now available via a private NPM package in the paid tier.
More language support is coming soon. We’re actively expanding to meet the needs of multi-language codebases and AI workflows.