Invariant Tools

Why this matters

AI agents write code confidently but frequently drift from the stated goal — adding unrelated changes, deleting important functions, or introducing subtle dependency shifts. Static analysis catches syntax errors but can't evaluate intent. Invariant bridges the gap: it uses LLM inference to understand what the code does semantically, then uses deterministic symbolic reasoning to verify whether that matches what was intended. Add a few lines to your agent's system prompt and every code change gets verified before it's presented as complete.

Capabilities

• ✓invariant.code_lens — Extract structural and semantic facts from source code: functions, calls, dependencies, intent, side effects, and patterns
• ✓invariant.code_query — Run predefined or custom queries over lensed facts: orphans, test gaps, dependency cycles, intent mismatches, security concerns, hotspots
• ✓invariant.diff_analyzer — Compare code before and after changes against a stated goal. Returns alignment score, unexpected changes, concerns, and actionable suggestions

Agent feedback loop

The most powerful use of Invariant is as an automatic verification step in an agent's coding loop. After making code changes, the agent calls invariant.diff_analyzer with its stated goal. If the alignment score is low or unexpected changes are flagged, the agent revises before presenting its work. This creates a neuro-symbolic feedback loop: fuzzy reasoning writes the code, deterministic reasoning verifies it, and fuzzy reasoning interprets the feedback to self-correct.