GroundLens MCP server

<!-- mcp-name: io.github.groundlens-dev/groundlens -->

Execution verification runtime for AI systems and agents

What it is · Quick start · Architecture · How it works · Engine · Runtime · Records · MCP server · Determinism · Examples · Docs · FAQ · Roadmap

What GroundLens is

GroundLens is an execution verification runtime for AI systems and agents. It turns observable AI execution into deterministic, policy-governed evidence that can be independently verified.

GroundLens provides a vendor-neutral runtime and evidence protocol for observing AI executions, evaluating claims, tool calls, actions and outcomes against composable verifiers and policies, and producing signed, reproducible evidence records.

The unit is the execution: an ordered sequence of steps an AI system or agent takes, from a model call and a retrieval to a tool call, an action with side effects and a human approval. GroundLens records each step, checks it, decides, and seals the run into a signed record anyone can verify offline. Verifying a single answer is the smallest case, a run with one claim.

• an answer, and the claims inside it → PASS, REVIEW or FAIL
• a tool call or an action → ALLOW, REVIEW or DENY

Where a guardrail blocks or scores an output in the moment and leaves nothing behind, GroundLens leaves signed, hash-chained evidence a third party can check without trusting you. It runs locally, needs no access to your weights, prompts or architecture, and is built for teams shipping AI answers and agents into regulated or high-stakes workflows who need proof, not a score.

Quick start

pip install groundlens

The package installs the engine, the groundlens and glv commands, and needs no other dependency. Numbers and rules are checked out of the box; the lexical verifier needs one optional download, shown at the end.

Verify an answer

Check a model's answer against the sources it was given, under a policy, and get a record you can keep.

from groundlens import verify

question = "What is the invoice total?"
source   = "The total amount due is 10,000 dollars, payable within 30 days of receipt."
answer   = "The invoice total is 1,000 dollars, due in 30 days."

Shortened. The full README is on GitHub.