Agnost AI vs Elva: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Agnost AI and Elva — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Agnost AI

Agnost Tech Inc

Freemium

Product analytics for conversational agents that surfaces silent failures, user frustration and policy violations across every conversation.

Silent Failure Detection: Reads each trace next to the conversation to catch cases where the run reported success but the user got nothing useful, including broken promises and confidently wrong answers.
Automatic Conversation Clustering: Turns thousands of chats into ranked recurring problems, ordered by user impact and ready to investigate rather than left as raw logs.
Frustration and Churn Signals: Pinpoints where users rage-prompt, get stuck or abandon the conversation, so churn drivers are visible before the user leaves.
Policy and Quality Violation Alerts: Flags hallucinations and quality, policy and compliance breaches with the exact conversation and trace behind each one.
Evidence-Backed Fix Recommendations: Hands over the highest-impact fixes with supporting evidence, a recommended change and the evals needed to ship it safely.
Two-Step Skill Install: Connects to an existing agent by installing an agent skill and running one prompt, with no rebuild of the agent and no separate implementation project.
Feature Request Mining: Surfaces what users repeatedly ask for across conversations, turning support volume into a prioritised roadmap signal.
Live Demo Without Signup: Ships a public interactive demo where you can click any insight and inspect the underlying conversations before creating an account.

Diagnosing Agent Churn: Finding the recurring conversation pattern that makes users abandon a support agent, with the specific chats as evidence.
Auditing Production Agents for Compliance: Reviewing conversations for policy violations and unsupported claims across real traffic rather than a hand-picked sample.
Prioritising Agent Improvements: Deciding which prompt or flow to fix next based on how many users hit each failure cluster instead of on anecdote.
Catching Regressions After a Prompt Change: Watching whether a newly shipped change increases silent failures or user frustration in live conversations.
Building Evals from Real Failures: Turning observed production failures into regression evals so the same bug does not ship twice.
Mining Conversations for Roadmap Input: Extracting repeated feature requests from support and sales chats to feed product planning.

Theneo

Freemium

Reads your repositories to discover every API, scores and governs them, then exposes them to developers and AI agents via hosted MCP servers.

Spec-Free API Discovery: Elva scans repository code directly to find endpoints and generates OpenAPI 3.1 as output, so no existing spec is needed to start.
Endpoint Scoring: Every collection is graded on design, developer experience, AI readiness, security and performance, with the weakest collection surfaced first.
AI Fix Pass: A one-click agent writes missing descriptions from code, types response schemas and documents auth, then rescores the collection.
API Contracts: Per-audience contracts pin the exact endpoints and fields a partner, internal team, public developer or MCP client receives, excluding PII and internal fields.
Breaking Change Enforcement: Each commit is diffed against published contracts, showing the schema diff, affected consumers and tools, and blocking publish by policy.
Hosted MCP Servers: Contracts generate MCP servers hosted behind Elva's gateway with OAuth2, scoped keys, per-tool authorization and exportable call logs.
MCP Playground and Agent Feedback: Test the server with a live model, then read the complaints agents file about confusing or failing tools, scored back into the catalog.
Multi-Target Publishing: One approved contract ships as OpenAPI spec, Theneo docs, MCP server, Postman collection and a typed TypeScript SDK in sync.

API Inventory Audit: Discover undocumented or forgotten endpoints across a large codebase and get a ranked list of what to fix first.
Agent Enablement: Expose an internal service to Claude, Cursor or ChatGPT as a governed MCP server instead of hand-writing tool wrappers.
Partner Integration Safety: Publish a restricted contract to an external partner and have Elva block commits that would break their integration.
PII Scoping: Keep customer emails and internal ops annotations out of a public or agent-facing surface while the same endpoints serve them internally.
Zombie Endpoint Retirement: Prove no active consumer references an endpoint before deleting it, using contract and call-log evidence.
Enterprise Security Review: Satisfy SOC 2, ISO 27001 and GDPR questions and wire agent access into an existing SSO and SCIM identity provider.
Documentation Drift Control: Keep docs, SDKs and Postman collections regenerated from code on every merge instead of maintained by hand.