Description

n8n-nodes-verica

n8n community node for LLM observability: it sends your AI Agent / LLM
executions to Verica as traces, with inputs, outputs,
tool calls, token usage and cost.

Install

• Self-hosted n8n: Settings → Community Nodes → Install n8n-nodes-verica.
• n8n Cloud: available once the node is verified; meanwhile use the

importable recipe from Verica’s Connect dialog (Traces → Connect → n8n).

Usage

1. Create a Verica API credential: an ingest token (Verica → Settings →
API tokens, with the ingest scope). The endpoint defaults to the Verica
cloud.
2. Drop Verica Trace after your AI Agent (or any LLM step), or pick its
Send a trace action straight from the canvas. The node has one resource,
Trace, with one operation, Send — an ingest token reaches exactly one
endpoint. The defaults
read $json.output, $json.chatInput, $json.intermediateSteps and
$json.sessionId; set Model so the trace can be priced. It works after
the AI Agent or “Message a model” out of the box: object outputs are
flattened to text automatically.
3. Enable Return intermediate steps on the AI Agent so tool calls land in
the trace (Verica’s tool_check grader can then assert on them).

Sessions. Executions sharing a sessionId (the Chat Trigger provides one)
reassemble into a Verica session, one turn per execution. Turns are stored as
deltas matched on byte-identical text, so if you map Input to something
richer than the new message (for example a rebuilt history), resend it exactly
as sent before: any per-turn mutation (like appending “Respond in JSON” to the
last message only) makes every turn degrade to a full copy of the
conversation.

With “Message a model” (OpenAI Responses API), token usage is picked up
from the response automatically: adding the Input Tokens / **Output
Tokens options captures the response’s usage, including the Reasoning
Tokens (a breakdown of output tokens) and Cached Tokens** (a breakdown of
input tokens, priced at the cache rate) when the response reports them. The
response does not echo your prompt: map Input to the node’s parameter,
e.g. {{ $('Message a model').params.responses.values[0].content }} (hover
the Prompt field to confirm the parameter path in your n8n version).

Tool calls in the trace need a node that emits them. The AI Agent does
(enable Return intermediate steps); “Message a model” with attached tools
runs its tool loop internally and returns only the final answer, so its
executed calls are not capturable downstream. For tool-using workflows, use
the AI Agent.

With an AI Agent, map Model to the chat-model sub-node’s parameter,
e.g. {{ $('OpenAI Chat Model').params.model.value || $('OpenAI Chat Model').params.model }}.
Token usage is NOT capturable in agent workflows: n8n does not propagate the
chat model’s tokenUsage to the agent output or to downstream expressions
(open issue n8n#26302); the
trace lands without tokens/cost but stays fully evaluable.

The node is fail-open: an export error never breaks your workflow; the item
passes through with a vericaError annotation instead.

License

MIT