AUTOMATION
Set Up Agent Observability with Torrix MCP Server
Monitor agent behavior in production using lightweight SQLite-based observability with MCP integration
Updated: 5/14/2026
Difficulty
easy
Time
15-30m
Use Case
Enable AI agents to query their own execution logs and performance metrics in real-time, supporting self-monitoring and debugging workflows.
Popularity
0 views
About this automation
Deploy Torrix as a single Docker container to capture LLM call traces, token usage, latency, and costs. Expose logs via MCP server so AI assistants can query observability data directly, enabling agents to understand their own behavior and performance.
How to implement
1
Download docker-compose.yml from Torrix GitHub
2
Run 'docker compose up' (no Postgres/Redis required)
3
Configure HTTP proxy or Python/Node SDK to log LLM calls
4
Enable MCP server in Torrix config
5
Connect AI assistant to Torrix MCP server for log querying
6
Set cost forecasting and budget caps for agent spending