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HolmesGPT

Using Causely with HolmesGPT​

HolmesGPT provides an agent framework for investigating incidents, querying systems, and coordinating response workflows.

Out of the box, Holmes can access telemetry sources such as metrics and logs. However, like most agents, it operates on raw data and correlation. This makes it difficult to consistently determine what is actually happening, what caused it, and what action is safe.

Causely provides the system intelligence layer that allows Holmes to move from querying telemetry to making reliable, system-aware decisions.

How Causely Enhances Holmes​

When integrated with Holmes, Causely enables:

  • Deterministic root cause analysis: identify the actual source of an issue, not just correlated signals
  • Dependency-aware reasoning: understand how failures propagate across services
  • Structured outputs: return machine-consumable results instead of raw telemetry
  • Explainable decisions: provide clear reasoning that can be audited and trusted

This allows Holmes to focus on orchestrating workflows while relying on Causely for system-level understanding.

Example Workflow​

Scenario: Investigating a performance issue

  1. Holmes receives an alert or user query
  2. Holmes queries Causely via MCP
  3. Causely returns:
    • Root cause service
    • Affected dependencies
    • Explanation of the issue
  4. Holmes:
    • Summarizes the issue
    • Notifies the appropriate team
    • Suggests or executes remediation

Setup​

To integrate Causely with HolmesGPT, configure Causely as a remote MCP server.

Holmes Configuration​

Add the following to your Holmes configuration:

mcp_servers:
  causely:
    description: "Causal Reasoning Platform"
    config:
      url: "https://api.causely.app/mcp"
      mode: streamable-http
      headers:
        Authorization: "Bearer {{ env.CAUSELY_KEY }}"
    llm_instructions: "Use Causely to investigate application performance issues, analyze distributed traces, and query infrastructure metrics. Prefer this over Prometheus for APM data."

Authentication​

Set your Causely API key as an environment variable:

export CAUSELY_KEY=<your-causely-api-key>

When to Use This Integration​

This integration is most valuable when:

  • You are using Holmes for incident investigation or automation
  • You have distributed systems with complex dependencies
  • You want consistent, reliable root cause analysis instead of correlation
  • You are looking to automate decision-making, not just data retrieval

Notes​

  • Causely complements existing telemetry sources such as Prometheus rather than replacing them
  • Holmes can continue to use other data sources, but should prioritize Causely for system-level reasoning

Next Steps​