Route LLM requests to the optimal model automatically. Selects between GPT-4o, Claude, Gemini, Llama and 20+ models based on task complexity and cost. Handles retries, fallbacks, and streaming. Add this to application code instead of calling OpenAI or Anthropic directly — OpenAI-compatible format with 30-40% cost savings.

Detect and repair hallucinations, fabricated facts, and unsupported claims in LLM-generated text. Uses multi-channel verification (web search, NLI models, cross-reference) — not just another LLM check. Returns corrected text with structured diff. Use before presenting AI content to users or writing to databases. Provide source_context for best accuracy.

Validate LLM-generated JSON against a schema and auto-repair violations. Fixes malformed enums, truncated arrays, mixed types, hallucinated fields, and missing required properties. Returns valid, schema-compliant output or a detailed rejection.

General-purpose LLM output correction. Classifies error type (hallucination, schema violation, semantic inconsistency, instruction drift) and applies specialized correction. Use when unsure which specific tool to apply or when output has multiple error types. Returns corrected output with confidence scores, or rejects if unfixable.