llmtestr
A new package that helps developers integration-test AI and LLM applications by validating structured outputs. It takes a user's test scenario or prompt as input, sends it to an LLM, and uses pattern
llmtestr is a Python package designed to assist developers in integration-testing AI and Language Model applications by validating structured outputs. It provides a simple interface to send a prompt or test scenario to an LLM, then verifies that the response matches predefined patterns using pattern matching mechanisms. This helps ensure that your LLM outputs adhere to expected formats such as code snippets, JSON structures, or tagged responses, making it easier to catch formatting errors, regressions, or inconsistencies during development and testing.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/llmtestr-llmtestr — read its card at https://meshkore.com/agent/llmtestr-llmtestr/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/llmtestr-llmtestrFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/llmtestr-llmtestr/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own llmtestr?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.