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Supported Technologies

Causely integrates with your existing infrastructure, observability stack, and operational tools. This page provides a complete reference of all supported technologies across three areas:

Installation: Deploy Causely agents in Kubernetes, Docker, or other environments
Telemetry Sources: Connect metrics, traces, logs, and infrastructure data from platforms you already use
Workflows: Route insights to Slack, Microsoft Teams, Grafana, and other tools your team relies on
Coding Agents: Connect to Causely from your IDE or CLI using the Model Context Protocol

Use the search and filters below to find what you need.

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Category

Signal Types

61 technologies found

Enable cross-service traceability for enterprise and cloud applications.

Application Instrumentation

Sourcegraph's CLI coding agent with native MCP support.

Atlassian Rovo DEV CLI

Command line interface for Atlassian Rovo DEV.

AWS Managed Services

Integrate CloudWatch metrics and events to infer causes in managed AWS workloads.

Infrastructure Scraper

Infrastructure Entities

Service Discovery

Azure Managed Services

Surface reliability issues in Azure services with native telemetry inputs.

Infrastructure Scraper

Infrastructure Entities

Service Discovery

Capture detailed performance traces from high-performance systems to improve causal inference precision.

Application Instrumentation

Causely Grafana Plugin

Visualize causes and their service impact directly in your Grafana dashboards — across Grafana Cloud or your self-hosted deployment.

Causely Slack App

Ask questions and investigate reliability issues directly in Slack using Ask Causely.

Use API and synthetic check failures as trusted inputs for causal analysis.

Symptom Activation

Anthropic's agentic coding assistant for the terminal.

Anthropic's desktop app with native MCP support.

Install Causely using the command-line interface for quick setup and configuration.

Use CockroachDB metrics and alerts as key signals for detecting and diagnosing service-level issues.

Confluent Cloud

Detect message queue congestion and downstream impact from Kafka telemetry.

Infrastructure Entities

Use Cortex metrics and alerts as key signals for detecting and diagnosing service-level issues.

AI-first code editor built for pair programming with AI.

Use Datadog traces and monitors as trusted signals for causal analysis.

Service Communication

Deploy Causely on standalone Docker hosts for services running outside Kubernetes.

Infrastructure Scraper

Infrastructure Entities

Service Discovery

Use Dynatrace traces and metrics to strengthen causal inference and reliability insights.

Service Communication

Capture application-level dependencies and performance signals without modifying code.

Service Communication

Integrate Elasticsearch metrics and logs to detect and resolve performance issues.

Infrastructure Entities

Map message-passing and concurrent behavior to detect issues in distributed environments.

Application Instrumentation

Deploy Causely using FluxCD for GitOps-based deployment and management.

GCP Managed Services

Integrate GCP service metrics to infer causes in managed workloads.

Infrastructure Scraper

Infrastructure Entities

Service Discovery

Add low-overhead tracing to cloud native applications for faster dependency discovery.

Application Instrumentation

Use Grafana to bring in metrics, logs, and traces as rich inputs for causal analysis.

Service Communication

Grafana Alertmanager

Send causal insights to Grafana Alertmanager without additional configuration.

Combine high-fidelity eBPF data with Causely's analytics.

Service Communication

Deploy Causely on Kubernetes using Helm charts for easy management and configuration.

Leverage incident.io alerts as trusted inputs for causal analysis.

Symptom Activation

Integrate Instana traces and metrics to enhance causal analysis and deepen reliability insights.

Service Communication

Map service mesh communication patterns for precise dependency-aware causal inference.

Connect JVM traces and metrics for proactive detection of performance regressions.

Application Instrumentation

Instrument JavaScript-based services to capture backend traces and uncover issues in distributed architectures.

Application Instrumentation

Use Kafka metrics and alerts as key signals for detecting and diagnosing service-level issues.

AWS coding assistant with MCP integration.

Deploy Causely on Kubernetes clusters for real-time causal analytics.

Infrastructure Scraper

Infrastructure Entities

Service Discovery

Microsoft Teams

Post causal insights into Microsoft Teams to keep dev and ops teams aligned.

Use Mimir metrics and alerts as key signals for detecting and diagnosing service-level issues.

Use MongoDB metrics and alerts as key signals for detecting and diagnosing service-level issues.

Identify database query latency or connection errors as causes in distributed issues.

Infrastructure Entities

Align SLO breaches with Causely's causal insights to prioritize reliability work.

Install Causely on HashiCorp Nomad clusters for distributed workload management.

Infrastructure Scraper

Infrastructure Entities

Service Discovery

Automatically instrument services with OpenTelemetry and feed traces into Causely's engine.

Service Communication

Enable automatic discovery of service dependencies across both synchronous and asynchronous communications.

Service Communication

Include PHP service traces to expose web transaction bottlenecks in causal analysis.

Application Instrumentation

Link PostgreSQL performance and query issues to affected services in real time.

Infrastructure Entities

Use Prometheus metrics and alerts as key signals for detecting and diagnosing service-level issues.

Prometheus Alertmanager

Use Alertmanager alerts as trusted inputs for causal analysis.

Symptom Activation

Link Python app traces and metrics to detect slow APIs or resource contention.

Application Instrumentation

Use RabbitMQ metrics and alerts as key signals for detecting and diagnosing service-level issues.

Use Redis metrics and alerts as key signals for detecting and diagnosing service-level issues.

Enable full-trace visibility across Ruby web and background jobs for causal inference.

Application Instrumentation

Feed low-level telemetry into Causely to uncover performance and memory-related causes.

Application Instrumentation

Send causal insights directly to your team Slack channels, so critical issues are seen and acted on immediately.

Surface warehouse congestion or query slowdowns as upstream causes in analytics pipelines.

Infrastructure Entities

Deliver real-time causal insights into Splunk On-Call with service-level impact and ownership metadata.

Connect Swift app traces to isolate problems across distributed components.

Application Instrumentation

Visual Studio Code

Popular code editor with MCP extension support.

AI-powered IDE with native MCP support.

High-performance editor with built-in MCP support.