# Coval

> Coval is the deployment-readiness platform for voice and chat AI agents: simulate conversations before launch, monitor real production calls, and improve reliability with metrics and human review. Supports inbound/outbound voice, standard chat, chat WebSocket, chat A2A, voice-to-voice (OpenAI Realtime, Gemini Live), SMS, WebSocket, Pipecat, and LiveKit agents.

## Sofia

- [Sofia](https://docs.coval.ai/sofia/overview): Use Sofia in Coval or through the Coval connector to investigate results and improve evaluation coverage
- [Using Sofia](https://docs.coval.ai/sofia/using-sofia): Open Sofia, ask grounded questions, and turn evaluation findings into action

## Simulate

- [Welcome](https://docs.coval.ai/welcome): What Coval does and who it's built for
- [Simulations](https://docs.coval.ai/concepts/simulations/overview): What a simulation is and what you need to set one up
- [Reports](https://docs.coval.ai/concepts/simulations/reports): Combine multiple runs into one view to compare across agents, personas, and other dimensions
- [Agents](https://docs.coval.ai/concepts/agents/overview): Connect your voice and chat agents to Coval to launch simulated conversations
- [Agent Mutations](https://docs.coval.ai/concepts/agents/mutations): Test agent configuration variants side-by-side in a single evaluation
- [Personas](https://docs.coval.ai/concepts/personas/overview): Define who the simulated user is and how they sound
- [Test Sets](https://docs.coval.ai/concepts/test-sets/overview): Define the scenarios you want to evaluate your agent against
- [Create a Test Set](https://docs.coval.ai/concepts/test-sets/create): Generate test cases with AI, build them manually, or upload a CSV/Excel file
- [Input Types](https://docs.coval.ai/concepts/test-sets/input-types): Scenario, transcript, or script — how tightly the simulated user follows your input
- [Expected Behavior & Attributes](https://docs.coval.ai/concepts/test-sets/evaluation): Define how the agent should respond and attach per-test-case data for evaluation
- [Templates](https://docs.coval.ai/concepts/templates/overview): Save an evaluation configuration so you can re-launch or schedule it
- [Runs & Schedules](https://docs.coval.ai/concepts/runs/overview): Tag your evaluations and run them on a recurring schedule
- [Attributes](https://docs.coval.ai/concepts/attributes/overview): Use dynamic attributes from agents, test cases, and simulations in metric prompts and scenarios
- [Versioning](https://docs.coval.ai/concepts/versioning/overview): How Coval keeps a version history for metrics, test sets, personas, and agents
- [Benchmarks](https://docs.coval.ai/concepts/benchmarks/overview): Compare speech-to-text and text-to-speech providers on your own audio and text

## Agent Connection Types

- [Inbound Voice](https://docs.coval.ai/concepts/agents/connections/inbound-voice): Configure agents that receive incoming phone calls and simulate inbound calls
- [Outbound Voice](https://docs.coval.ai/concepts/agents/connections/outbound-voice): Configure agents that initiate phone calls to users
- [Standard Chat](https://docs.coval.ai/concepts/agents/connections/chat): Connect a chat agent over a standard HTTP endpoint and simulate text conversations
- [Chat WebSocket](https://docs.coval.ai/concepts/agents/connections/chat-websocket): Connect to text chat agents over a persistent WebSocket connection
- [Chat A2A (JSON-RPC)](https://docs.coval.ai/concepts/agents/connections/chat-a2a): Connect to text chat agents that speak the A2A v2 JSON-RPC protocol
- [OpenAI Realtime](https://docs.coval.ai/concepts/agents/connections/openai-realtime): Evaluate an OpenAI Realtime voice-to-voice agent without hosting your own endpoint
- [Gemini Live](https://docs.coval.ai/concepts/agents/connections/gemini-live): Evaluate a Google Gemini Live voice-to-voice agent without hosting your own endpoint
- [SMS Simulations](https://docs.coval.ai/guides/simulations/sms): Simulate SMS conversations with your agent
- [WebSocket (Voice)](https://docs.coval.ai/concepts/agents/connections/websocket): Connect a real-time voice agent over WebSocket using configurable audio messages
- [Pipecat Cloud](https://docs.coval.ai/concepts/agents/connections/pipecat): Connect your Pipecat Cloud agent to run voice AI simulations
- [LiveKit](https://docs.coval.ai/concepts/agents/connections/livekit): Connect to LiveKit for advanced audio/video agents

## Evaluate

- [Metrics](https://docs.coval.ai/concepts/metrics/overview): Turn a conversation into a measurable signal — what metrics are and where to go next
- [Add your first metric](https://docs.coval.ai/concepts/metrics/quickstart): Create a metric and attach it to a run, end to end
- [Choose a metric](https://docs.coval.ai/concepts/metrics/choose-a-metric): Find the right metric by what you want to measure
- [Write judge prompts](https://docs.coval.ai/concepts/metrics/writing-judge-prompts): Prompt structure, examples, and techniques for reliable LLM-judge metrics
- [Configure metrics](https://docs.coval.ai/concepts/metrics/configuring-metrics): Template variables, transcript scope, and trace context for custom metrics
- [Metric Chaining](https://docs.coval.ai/concepts/metrics/metric-chaining): Combine metrics into custom logic flows for more efficient, accurate evaluations
- [Deterministic Metrics](https://docs.coval.ai/concepts/metrics/types/deterministic): Rule-based metrics — regex, field matching, and pattern checks
- [Statistical Metrics](https://docs.coval.ai/concepts/metrics/types/statistical): Timing, signal, and acoustic measurements computed directly from a call
- [ML Model Metrics](https://docs.coval.ai/concepts/metrics/types/ml-model): Purpose-built ML models — sentiment, voice consistency, and transcription accuracy
- [LLM Judge Metrics](https://docs.coval.ai/concepts/metrics/types/llm-judge): Use an LLM to evaluate a transcript or recording against your own question
- [Trace Metrics](https://docs.coval.ai/concepts/metrics/types/trace): Metrics derived from OpenTelemetry spans — latency, token usage, tool calls, transcription accuracy

## Observe

- [Live Conversations](https://docs.coval.ai/concepts/conversations/overview): Evaluate real production calls in Coval — the production counterpart to simulations
- [Uploading Conversations](https://docs.coval.ai/concepts/conversations/uploading): Transcript and audio formats for uploading conversations for evaluation
- [Dashboards](https://docs.coval.ai/concepts/dashboard/overview): Track agent metrics over time across simulations and live conversations
- [Attach Audio After a Call](https://docs.coval.ai/concepts/conversations/async-audio-attach): Submit transcripts at call end and attach audio when the recording URL finalizes
- [Add Metadata After a Call](https://docs.coval.ai/concepts/conversations/add-metadata-after-submit): Add values produced by post-call analysis to an existing conversation and filter on them
- [Traces](https://docs.coval.ai/concepts/simulations/traces/overview): Send your agent's traces to Coval to evaluate what it did, not just what it said
- [Tracing Wizard](https://docs.coval.ai/concepts/simulations/traces/wizard): Automatically add Coval OTel tracing to your Python voice agent with one command
- [Tracing Skills](https://docs.coval.ai/concepts/simulations/traces/tracing-skills): Instrument, validate, optimize, and debug agent traces with an AI-assisted workflow
- [OpenTelemetry Traces](https://docs.coval.ai/concepts/simulations/traces/opentelemetry): Send traces from your agent using the OpenTelemetry SDK
- [Import Traces from a Platform](https://docs.coval.ai/concepts/simulations/traces/imports): Import traces automatically from Langfuse, Arize Phoenix, or LangSmith
- [Twilio ConversationRelay + OTel Traces](https://docs.coval.ai/guides/simulations/twilio-conversationrelay): Add OTel tracing to a Twilio Programmable Voice agent and correlate spans with simulations
- [Viewing Traces](https://docs.coval.ai/concepts/simulations/traces/viewing): Inspect a call's spans and find where runs fail with Transition Hotspots
- [Trace Search](https://docs.coval.ai/concepts/conversations/trace-search): Search all traced calls with natural language queries, structured filters, and Transition Hotspots

## Review

- [Human Review Projects](https://docs.coval.ai/concepts/metrics/human-review/human-review): Organize human labeling of conversations to validate metrics and triage production calls
- [How to Review a Conversation](https://docs.coval.ai/concepts/metrics/human-review/how-to-review): For reviewers — open assignments, label each metric, apply triage labels
- [Human Review via API](https://docs.coval.ai/guides/human-review-api): Create review projects and submit ground-truth annotations programmatically
- [Improving Metrics with Human Review](https://docs.coval.ai/guides/improving-metrics-with-human-review): Use human labels to measure how much you can trust each metric

## Cookbooks

- [Handling User Authentication](https://docs.coval.ai/guides/handling-authentication): Make your simulated caller authenticate as a real user before the agent helps them
- [Testing IVR](https://docs.coval.ai/guides/testing-ivr): Check whether your voice agent routes callers correctly through your phone menu
- [Testing Across Audio Qualities](https://docs.coval.ai/guides/testing-across-audio-qualities): Compare performance across voices, styles, volume, interruptions, and background noise
- [Testing Across Accents](https://docs.coval.ai/guides/testing-across-accents): Compare how a voice agent performs with callers across different accents
- [Adversarial & Red-Team Testing](https://docs.coval.ai/guides/adversarial-red-team-testing): Test whether an agent stays safe and in-policy against prompt injection, jailbreaks, and social engineering
- [Evaluating Tool Calls](https://docs.coval.ai/use-cases/evaluating-tool-calls): Use OpenTelemetry traces to validate what your agent's tools actually did

## Reference

- [API Keys](https://docs.coval.ai/guides/api-keys): Create and manage API keys to authenticate with the Coval API
- [Keyboard Navigation](https://docs.coval.ai/guides/keyboard-navigation): Use Coval efficiently with the keyboard across lists, detail views, and the command palette
- [GitHub Actions](https://docs.coval.ai/getting-started/github-actions-tutorial): Launch Coval evaluation runs from GitHub Actions

## CLI

- [CLI](https://docs.coval.ai/cli/overview): Command-line interface for the Coval AI evaluation platform
- [Installation & Configuration](https://docs.coval.ai/cli/installation): Install the Coval CLI and configure authentication
- [Agent Mode](https://docs.coval.ai/cli/agent-mode): Run the CLI from autonomous agents with structured I/O, discovery, and next actions
- [Agents](https://docs.coval.ai/cli/agents): Manage AI agent configurations
- [Runs](https://docs.coval.ai/cli/runs): Launch and manage evaluation runs
- [Simulations](https://docs.coval.ai/cli/simulations): View simulation results and download audio
- [Conversations](https://docs.coval.ai/cli/conversations): Inspect monitored conversations, audio, and metric results
- [Test Sets](https://docs.coval.ai/cli/test-sets): Manage test set collections
- [Test Cases](https://docs.coval.ai/cli/test-cases): Manage individual test cases
- [Personas](https://docs.coval.ai/cli/personas): Manage simulated personas
- [Metrics](https://docs.coval.ai/cli/metrics): Manage evaluation metrics
- [Mutations](https://docs.coval.ai/cli/mutations): Test agent variations with config overrides
- [API Keys](https://docs.coval.ai/cli/api-keys): Manage API keys for programmatic access
- [Human Review](https://docs.coval.ai/cli/human-review): Manage human review projects and annotations
- [Run Templates](https://docs.coval.ai/cli/run-templates): Create reusable evaluation configurations
- [Scheduled Runs](https://docs.coval.ai/cli/scheduled-runs): Schedule recurring evaluation runs
- [Dashboards](https://docs.coval.ai/cli/dashboards): Create and manage dashboards and widgets

## SDKs

- [SDKs](https://docs.coval.ai/sdks/overview): Typed TypeScript and Python clients generated from the public OpenAPI specs

## AI Agents

- [Evaluations for Agents](https://docs.coval.ai/agents/overview): Give AI coding agents the tools and knowledge to evaluate AI quality via Skills, MCP, CLI, or API
- [Guided Onboarding](https://docs.coval.ai/agents/onboarding): Run /onboard to set up a complete evaluation interactively
- [Agent Skills](https://docs.coval.ai/agents/skills): Install evaluation expertise into your AI coding agent with one command
- [Context7](https://docs.coval.ai/agents/context7): Access up-to-date Coval documentation directly inside AI coding agents via Context7

## Coval Connector

- [Coval Connector](https://docs.coval.ai/mcp/overview): Use Coval and Sofia from ChatGPT, Claude, Codex, and other MCP-compatible clients
- [Installation](https://docs.coval.ai/mcp/installation): Install the Coval connector in ChatGPT, Claude, Codex, or another MCP client
- [Tools Reference](https://docs.coval.ai/mcp/tools): Complete reference for all MCP server tools
- [Connector Guide](https://docs.coval.ai/mcp/beginners-guide): A practical guide to using Coval and Sofia from an AI assistant
- [Troubleshooting](https://docs.coval.ai/mcp/troubleshooting): Common installation, connection, OAuth, and tool issues for the Coval connector

## API Reference

- [API Reference](https://docs.coval.ai/api-reference/v1/introduction): Authentication, base URL, pagination, filtering, and error codes
- [OpenAPI Specs](https://api.coval.dev/v1/openapi): Machine-readable API specifications

## Optional

- [Changelog](https://docs.coval.ai/changelog): Product updates and release notes