Startup
Scorecard
The simulation platform for agent self-improvement
Scorecard is a platform for testing, evaluating and improving AI agents. It runs agents through large sets of realistic scenarios before release, and provides a metric library, prompt versioning and a playground. It also supports deploying agents and detecting real-world usage problems.
Neighbours in the directory
San Francisco, CA · Est. 2022
The de-facto standard toolkit for building LLM applications and agents.
LangChain makes the LangChain framework and LangGraph, the low-level orchestration standard for stateful, controllable agents, plus the LangSmith platform for tracing, evaluating and monitoring them in production.
~$75M raised
San Francisco, CA · Est. 2023
Enterprise-grade evals, data and AI gateway.
Braintrust turns prompts and agent traces into testable datasets and CI, plus an inference gateway — helping teams measure whether harness changes actually improve agent quality.
Berkeley, CA · Est. 2020
Observability and evaluation for AI — makers of OSS Phoenix.
Arize traces and evaluates LLM and agent systems in production; their open-source Phoenix tracer is a common instrumentation choice for agent harnesses that need to be debuggable.
Connections
Relations from the Harness Hub knowledge graph, as charted in the State of Harness reports.
Similar entries · 1
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Sources
Verified Oct 29, 2026