--- title: "Introducing Morphiq Bench: Agent Adoption Benchmarking for Developer Tools" authors: "Morphiq Labs" date: "May 20, 2026" last_updated: "2026-05-20" category: "Product" slug: "morphiq-affinity-bench" --- # Introducing Morphiq Bench: Agent Adoption Benchmarking for Developer Tools Authors: Morphiq Labs Last updated: 2026-05-20 Developer tools are increasingly discovered, evaluated, and integrated by coding agents. As this shift accelerates, developer-tool companies need a clearer way to understand how agents experience their products across real engineering workflows. Today, we are introducing Morphiq Bench, an agent adoption benchmark for developer tools. At the center of Morphiq Bench is the Agent Affinity Index, a scoring system designed to measure how well coding agents discover, compare, and use developer tools inside codebases. The core metric is the Affinity Score: a measure of how strongly agents associate a provider with the use cases, implementation patterns, and developer segments where that provider is expected to appear. ## Why Transparency Matters A single top-level score is not enough. We repeatedly heard from customers that transparency is essential to trusting any agent adoption benchmark. Teams do not only want to know where they rank. They want to understand why they rank there, which segments of developers are influencing the score, which use cases are driving agent selection, and where the strongest or weakest signals are coming from. That is why Morphiq Embed is built to expose the layers behind the score. The benchmark moves from fine-grained use-case affinity to broader provider-level scoring. This gives teams a clearer picture of how agents behave across specific product capabilities, customer segments, and developer workflows before those signals roll up into an overall score. ## Representativeness Starts With Scope Representativeness depends on scope. A provider's score should reflect the actual developer segments and use cases that matter for its market. For example, in a domain such as Auth, the benchmark should not only measure broad category visibility. It should also evaluate how agents respond to specific use cases within that category. That may include foundational implementation flows, team or organization-level patterns, advanced security capabilities, enterprise workflows, or other segments that represent how developers actually adopt the product. The more accurately those segments are captured, the more representative the score becomes. Morphiq Bench looks at the public package and repository landscape that agents can observe, compare, and reason over. Over time, this helps us understand where agent selection patterns align with real developer adoption, where the signal is saturated, and where the market is still underrepresented. ## Fine-Grained Affinity Creates Better Signals The most representative benchmark is not just the top-level provider score. It comes from analyzing fine-grained affinities across specific use cases. A broad score can show market-level visibility, but segment-level affinity explains the underlying behavior. It shows where agents consistently recognize a provider, where the signal is weaker, and where competing options are more likely to be selected. This matters because coding agents do not make decisions in the abstract. They reason from a task, a codebase, a framework, a developer segment, and a likely implementation path. Morphiq Bench is designed to measure affinity in that same context. ## From Segment Signals to Overall Affinity Morphiq Bench combines segment-level measurements into broader affinity views. The top-level score provides a useful benchmark for market comparison, but it is only meaningful when supported by transparent underlying data. Teams should be able to inspect which use cases contributed to the score, how different agents behaved, which developer segments influenced the result, and how representative those measured segments are for the provider's intended market. This structure makes the benchmark more actionable. Instead of optimizing for a vague overall ranking, teams can identify the specific segments where agent understanding, documentation, examples, integrations, or ecosystem presence need to improve. ## Building Trust in Agent Adoption Morphiq Bench is designed to help developer-tool companies understand agent adoption with more clarity and trust. The goal is not simply to optimize what agents can reason over. It is to map the market, understand product scope, and measure how reliably agents discover and use tools across the most probable developer workflows. As agents become a primary interface for software development, the companies that win will be the ones agents can reliably discover, compare, and integrate. Morphiq Bench makes that visibility measurable and actionable. **Affinity Score** is a metric for measuring how well coding agents discover, compare, and integrate or use developer tools in a codebase.