Agent

A webhook invoked when the agent verb ends. The payload includes a completion_reason field indicating why it terminated.

Controls whether and how the user can interrupt the assistant while it is speaking.

Allow the user to interrupt the assistant while it is speaking.

Seconds of detected speech required before confirming an interruption. Prevents brief noises from cutting off the assistant.

If true, once the user interrupts, the assistant does not resume speaking the interrupted response.

Enable speculative LLM prompting before end-of-turn is confirmed. When using Krisp turn detection, set this to true to speculatively prompt the LLM before Krisp confirms the turn has ended. If the transcript matches when the turn ends, buffered tokens are released immediately — reducing response latency. Deepgram Flux performs early generation automatically regardless of this setting.

A webhook invoked for agent events. Receives event types: user_transcript, agent_response, user_interruption, turn_end, and history_summarized. See eventHook Events below.

Whether the LLM should generate an initial greeting before the user speaks. Set to false if you want the agent to wait silently for the user to speak first.

An optional unique identifier for this verb instance.

LLM configuration. This is the only required property. See Bring Your Own LLM for the full list of supported vendors and per-vendor setup.

LLM vendor id. One of: openai, anthropic, google, vertex-gemini, vertex-openai, bedrock, deepseek, azure-openai, groq, huggingface. See Bring Your Own LLM for setup of each.

Model name. Format is vendor-specific (e.g., gpt-4o-mini, claude-haiku-4-5-20251001, gemini-2.5-flash, llama-3.3-70b-versatile, meta-llama/Llama-3.3-70B-Instruct). For Azure OpenAI, this is your deployment name, not the model id.

Only needed when the account has more than one LLM credential for the same vendor — give each a distinct label in the portal at create time and reference it explicitly here. Most accounts have a single credential per vendor and should leave this unset.

Inline authentication credentials. Format depends on the vendor — see the per-vendor pages under Bring Your Own LLM. If not provided, jambonz looks up credentials by vendor (and label, if the rare multi-credential case applies) from the LLM Services configured in the portal.

LLM options including system prompt, tools, and generation parameters.

Initial conversation messages. Typically includes a system message with instructions: [{ role: 'system', content: '...' }].

System prompt for the LLM. Alternative to providing a system message in messages.

Tool/function definitions available to the LLM. Uses OpenAI function-calling format. See Tool Calling below.

Maximum number of tokens in the LLM response.

External MCP servers that provide tools to the LLM. The agent verb connects to each server at startup, discovers available tools, and makes them callable by the LLM. Each entry requires a url property and optionally auth and roots.

Enable server-side noise isolation to reduce background noise. As a string, pass "krisp" or "rnnoise" for default settings. As an object: { mode: "krisp", level: 80, direction: "read" }. direction can be "read" (filter caller audio, default) or "write" (filter outbound audio). Krisp requires an API key on self-hosted systems.

Seconds to wait after the assistant finishes speaking before prompting the user to respond. When triggered, the LLM is prompted with a system cue to check if the user is still there. Set to 0 to disable.

Speech-to-text configuration. See recognizer for available properties. Key properties: vendor, language, hints, and vendor-specific options like deepgramOptions.

A webhook invoked when the LLM requests a tool/function call. The payload includes tool_call_id, name, and arguments (already parsed as an object). See Tool Calling below.

Text-to-speech configuration. See synthesizer for available properties. Key properties: vendor, voice, language, and vendor-specific options.

Turn detection strategy. Controls when the agent verb decides the user has finished speaking.

As a string:

• "stt" — Uses the STT vendor’s native end-of-utterance signal. For most vendors this is silence-based. Vendors with smarter built-in turn detection (deepgramflux, assemblyai, speechmatics) always use their native detection regardless of this setting.
• "krisp" — Uses the Krisp acoustic end-of-turn model, which analyzes speech patterns rather than just silence.

As an object (Krisp only):

• mode — "krisp" (required)
• threshold — Confidence threshold 0.0–1.0. Lower values trigger earlier turn transitions. Default: 0.5.
• model — Optional Krisp model name override.