Deeptrace
Deeptrace is an AI SRE (site reliability engineering) agent that automatically investigates and root-causes production alerts by reasoning across logs, traces, metrics, and code. It triages and prioritizes alerts, produces evidence-backed root cause analyses in a couple of minutes, answers natural-language questions about production from Slack or the web app, and can auto-generate remediation such as pull requests and GitHub Actions. Deeptrace connects to the existing toolchain — Datadog, Grafana, New Relic, Sentry, Honeycomb, Coralogix, AWS CloudWatch, PagerDuty, GitHub, Linear, and Slack — to cut mean time to resolution. It exposes a REST API to trigger investigations, poll results, and drive AI chat programmatically. Founded 2025 and backed by Felicis, Matrix, and Y Combinator.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Deeptrace the way a machine reads it — 5 machine-readable artifacts across 2 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Deeptrace scores 50.4/100 (developing), with a separate agent-readiness read of 51/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Deeptrace
Each block below is one kind of artifact we hold for Deeptrace. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 2
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Deeptrace Chat API
AI-powered conversational interface over your production systems.
Deeptrace Investigations API
Trigger and retrieve asynchronous root-cause investigations.
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
deeptrace-mcp.yml
MCP SERVERSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Deeptrace — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 4
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 3
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Access & Security 2
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/deeptrace · machine-readable index on apis.io