output-validation

The new package transforms a user’s plain‑text search phrase into a structured, BM25‑ranked list of PostgreSQL search results. It takes the query text as input, forwards it to an LLM that rewrites the

A new package that processes user-provided text input to generate structured summaries of technical challenges, such as the difficulty of implementing resumable LLM streaming. It uses a system prompt

A unified suite of MCP suppressors that prevent hallucinations, enforce grounding, and stabilise agent behaviour.

A practical Python toolkit for evaluating, monitoring, and governing LLM outputs in production

Parallel multi-critic LLM output arbitration using LangGraph. Multiple specialized critics evaluate responses in parallel; central adjudicator synthesizes verdicts with confidence scores. Pydantic contracts ensure type-safe judge outputs.

Deterministic regex scanner for dangerous payloads in LLM responses — Python port of the npm package, byte-for-byte parity. OWASP LLM02.

Listener-side output re-validation decorator against schema/predicate.

Enhancing a Customer Support Agent with production-grade safety layers including prompt injection defense, PII masking, structured output validation, and red-team testing.

Verify LLM output against your source documents. Catch hallucinations in RAG pipelines and agentic workflows before they reach users.

A linter for content written by AI agents. Most linters check what the text says. This one checks how it got there whether the agent that wrote a note labelled itself, whether a claim that generalizes was routed to review instead of asserted as fact, and whether the review queue is actually moving.