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@ura-dev/agentrace
Advanced tools
Structured observability for AI agents. See what your agent does, why it decides, and where it fails.
MCP server + CLI viewer. Zero dependencies. Works with Claude Code, Cursor, or any MCP client.
# CLI only (global)
npm install -g @ura-dev/agentrace
# As a library in your project
npm install @ura-dev/agentrace
After install, the agentrace CLI and MCP server are available globally.
# View recent traces
agentrace list
# View a specific trace
agentrace view <trace-id>
# Watch a trace in real-time
agentrace watch <trace-id>
# Show statistics
agentrace stats
Add to your AI tool config:
{
"mcpServers": {
"agentrace": { "command": "agentrace-mcp" }
}
}
Your AI agent now has access to structured tracing tools.
| Tool | Description |
|---|---|
trace_start | Start a new trace session |
trace_step | Log a step (action, tool used, input/output) |
trace_decision | Log a decision point (options, chosen, reasoning) |
trace_error | Log an error or unexpected state |
trace_end | End a trace session |
trace_list | List recent traces |
trace_view | View a complete trace |
agentrace list # Recent traces
agentrace view <trace-id> # Full trace with events
agentrace watch <trace-id> # Real-time tail
agentrace stats # Trace statistics
When an AI agent starts a complex task, it calls trace_start. As it works, it logs each step (trace_step), records decision points (trace_decision), and captures errors (trace_error). When done, it calls trace_end.
Traces are stored as JSON files in ~/.agentrace/traces/. You can view them with the CLI, or read them directly.
const { createTrace, addStep, addDecision, endTrace } = require('@ura-dev/agentrace');
const { id } = createTrace({ name: 'deploy pipeline', agent: 'my-agent' });
addStep(id, { action: 'build', tool: 'npm', output: 'success' });
addDecision(id, { question: 'Deploy target?', chosen: 'staging', reasoning: 'Friday deploy' });
endTrace(id, { status: 'completed', summary: 'Deployed to staging' });
| Env var | Description |
|---|---|
AGENTRACE_DIR | Override storage directory (default: ~/.agentrace) |
MIT — ura
FAQs
Structured observability for AI agents — MCP server + CLI viewer
The npm package @ura-dev/agentrace receives a total of 10 weekly downloads. As such, @ura-dev/agentrace popularity was classified as not popular.
We found that @ura-dev/agentrace demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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