Get the current trust score / detail for a previous LLM evaluation created via trustmodel_evaluate. Takes an integer evaluation_id and returns the evaluation detail including scores.

Open a new agent-trace capture session. Returns a trace_id that must be passed to every trustmodel_trace_step and the final trustmodel_trace_finalize. Capture steps (thoughts, tool calls, tool results, responses) as your agent executes; finalize when done to upload + auto-create an evaluation run.

Record a single step in an active trace session. Call once per reasoning step, tool call, tool result, or user-facing response. Step numbers are auto-assigned (1-based). Requires a trace_id from trustmodel_trace_start.

Close an active trace session, serialize the captured steps, upload to TrustModel cloud storage, and auto-create an agentic evaluation run. Returns both a file_path (for record-keeping / retry) and an evaluation_run_id that can be polled with trustmodel_score_agent.

Score AI output locally across the 10 TrustModel dimensions (safety, fairness, accuracy, privacy, transparency, robustness, accountability, explainability, compliance, reliability) and roll it into a 0-100 TrustScore. NO API key required — runs on this machine with a transparent heuristic judge. Returns trust_score, grade, per-dimension scores, and violations. Local scores are uncalibrated; use trustmodel_evaluate (cloud, needs a free TRUSTMODEL_API_KEY) for a calibrated, audit-ready score.

Check text against a governance policy pack and decide allow/block, locally. NO API key required. policy is a built-in pack id — eu-ai-act (default), nist-ai-rmf, nyc-ll144, or owasp-llm. Returns allowed/blocked, the policy id, and the list of rule violations (pattern and dimension rules). Use before letting AI output reach a user or another tool.