argus

README

🔭 Argus

AI-powered observability CLI for SREs.

Argus connects to your Signoz instances and uses AI to analyze logs, metrics, and traces with natural language queries. Supports Anthropic Claude, OpenAI GPT-4o, and Amazon Bedrock as AI providers.

"Why is latency high on the payments service?" — Just ask Argus.

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Features

• 🤖 Natural language queries — Ask questions about your infrastructure in plain English
• 📡 Multi-instance support — Manage multiple Signoz environments (production, staging, etc.)
• 📋 Real log/trace/metric queries — Direct integration with Signoz query_range API (v3 + v5)
• 🔧 Service discovery — List services with call counts and error rates
• 📊 Dashboard view — Combined overview of health, services, and recent errors
• ⚡ Streaming AI responses — Real-time analysis output as tokens arrive
• 🎨 Beautiful terminal UI — Severity-colored logs, formatted traces, metric tables
• 🔧 Simple configuration — YAML config, multiple profiles, easy setup

Installation

Homebrew (macOS & Linux)

brew install lbarahona/tap/argus

From source

go install github.com/lbarahona/argus/cmd/argus@latest

Binary releases

Download from GitHub Releases.

Build from source

git clone https://github.com/lbarahona/argus.git
cd argus
make build
# Binary at ./bin/argus

Quick Start

# 1. Initialize configuration
argus config init

# 2. Check instance health
argus status

# 3. List services
argus services

# 4. Query logs with AI analysis
argus logs auth-service --query "any errors in the last hour?"

# 5. View traces
argus traces frontend --duration 30

# 6. Quick dashboard
argus dashboard

# 7. Ask free-form questions
argus ask "why is latency high on the payments service?"

Commands

Command	Description
argus version	Print version information
argus config init	Interactive configuration setup
argus config add-instance	Add a new Signoz instance
argus instances	List configured instances
argus status	Health check all instances
argus services	List services with call counts and error rates
argus logs [service]	Query and analyze logs
argus traces [service]	Query distributed traces
argus metrics [metric]	Query metrics
argus incident create	Create a new incident
argus incident list	List active incidents
argus incident update	Update incident status
argus incident resolve	Resolve an incident
argus incident timeline	Show incident timeline
argus dashboard	Combined overview dashboard
argus ask [question]	Free-form AI analysis
argus report	Generate health report for shift handoffs
argus top	Ranked service view (like htop for services)
argus diff	Compare error rates between time windows
argus watch	Continuous monitoring with anomaly detection
argus alert	Declarative alert rules with cron-friendly output
argus explain	AI root cause analysis (correlates logs + traces)
argus deploy	Detect deployments from behavioral changes and analyze impact
argus slo	SLO tracking with error budgets and burn rates
argus budget check	Error budget burndown with burn rate analysis
argus guard	CI/CD deployment gate — SHIP/CAUTION/HOLD verdict

Logs

# Query logs for a service
argus logs my-service

# Filter by severity
argus logs my-service --severity ERROR

# With AI analysis
argus logs my-service --query "find authentication failures"

# Specify instance, duration, and limit
argus logs my-service -i staging -d 120 -l 50

Services

# List all services with error rates
argus services

# From a specific instance
argus services -i production

Traces

# Query traces for a service
argus traces frontend

# With duration and limit
argus traces api-gateway -d 30 -l 50

# With AI analysis
argus traces frontend --query "find slow requests over 1s"

Metrics

# Query a specific metric
argus metrics cpu_usage

# With AI analysis
argus metrics http_request_duration --query "any anomalies?"

Dashboard

# Quick overview of everything
argus dashboard

# Look back further for errors
argus dashboard -d 120

Ask

# Free-form questions — gathers context from Signoz automatically
argus ask "what services had the most errors today?"
argus ask "is there a correlation between high CPU and slow responses?"

Report

# Generate a health report (terminal format)
argus report

# Include AI-generated summary
argus report --ai

# Output as markdown (great for Slack/docs)
argus report -f markdown

# Cover last 4 hours
argus report -d 240 --ai

Top

# Show top services ranked by errors (like htop for services)
argus top

# Sort by error rate instead
argus top -s rate

# Sort by call volume
argus top -s calls

# Limit and custom duration
argus top -l 10 -d 120

Diff

# Compare last hour vs previous hour
argus diff

# Compare last 30 min vs previous 30 min
argus diff -d 30

# Shows which services are degrading, improving, or stable
argus diff -i production

Alert

# Create sample alert rules
argus alert init

# List configured rules
argus alert list

# Check all rules (colored output)
argus alert check

# JSON output for cron/automation
argus alert check --format json

# Exit codes: 0=ok, 1=warnings, 2=critical
argus alert check && echo "All clear" || echo "Alerts fired!"

Alert rules are defined in ~/.argus/alerts.yaml:

rules:
  - name: high-error-rate
    type: error_rate
    operator: gt
    warning: 5.0
    critical: 15.0
    duration: 5m
    labels:
      team: platform

  - name: api-errors
    service: api-service
    type: error_rate
    operator: gt
    warning: 2.0
    critical: 10.0

SLO

# Create sample SLO definitions
argus slo init

# List configured SLOs
argus slo list

# Evaluate all SLOs (colored output with budget bars)
argus slo check

# JSON output for dashboards/automation
argus slo check --format json

# Exit codes: 0=ok, 1=warning, 2=critical/exhausted
argus slo check && echo "Within budget" || echo "Budget alert!"

SLO definitions live in ~/.argus/slos.yaml:

slos:
  - name: "API Availability"
    service: ""        # empty = all services
    type: availability # availability or latency
    target: 99.9       # 99.9%
    window: 24h        # 1h, 6h, 24h, 7d, 30d
    labels:
      team: platform
      tier: "1"

  - name: "API Latency P99"
    type: latency
    target: 99.0       # 99% of requests under threshold
    threshold: 500     # milliseconds
    window: 24h

Output includes error budget bars, burn rates, and compliance status:

• ✅ OK — within budget
• ⚠️ Warning — >50% budget consumed
• 🔴 Critical — >80% budget consumed
• 💀 Exhausted — budget blown

Explain

# AI root cause analysis — correlates logs, traces, and metrics
argus explain api-service

# Analyze last 30 minutes
argus explain payment-service --duration 30

# Against a specific instance
argus explain auth-service -i production

Budget

# Error budget burndown analysis
argus budget check

# Check specific service
argus budget check --service api-gateway

# JSON output for automation
argus budget check --format json

# With AI recommendations
argus budget check --ai

# Different analysis windows
argus budget check --window 24h

Guard (CI/CD Deployment Gate)

# Should we deploy? Get a SHIP/CAUTION/HOLD verdict
argus guard

# Strict mode for critical services (lower thresholds)
argus guard --strict

# Check specific service before deploying it
argus guard --service api-gateway

# JSON output for CI/CD pipelines
argus guard --format json

# Custom thresholds
argus guard --max-error-rate 2.0 --max-p99 3000

# In CI/CD pipelines (exit code 0=ship, 1=caution, 2=hold)
argus guard --strict --format json || exit 1

# GitHub Actions example:
# - name: Pre-deploy safety check
#   run: argus guard --strict

Configuration

Config is stored at ~/.argus/config.yaml. Run argus config init for interactive setup.

Anthropic (default)

anthropic_key: sk-ant-...  # legacy root-level key still works
ai:
  provider: anthropic
  anthropic_key: sk-ant-...
  model: auto  # defaults to claude-sonnet-4-20250514
default_instance: production
instances:
  production:
    url: https://signoz.example.com
    api_key: your-signoz-api-key
    name: Production
    api_version: v3

OpenAI

ai:
  provider: openai
  openai_key: sk-...
  model: gpt-4o  # or gpt-4o-mini, etc.
default_instance: production
instances:
  production:
    url: https://signoz.example.com
    api_key: your-signoz-api-key
    name: Production

Amazon Bedrock

Bedrock uses bearer token auth (no AWS SDK, no SigV4). Get a bearer token via AWS SSO, Identity Center, or any mechanism that issues bearer tokens for Bedrock.

ai:
  provider: bedrock
  bedrock:
    endpoint: https://bedrock-runtime.us-east-1.amazonaws.com
    token: your-bearer-token
    model: anthropic.claude-3-5-sonnet-20241022-v2:0
default_instance: production
instances:
  production:
    url: https://signoz.example.com
    api_key: your-signoz-api-key
    name: Production

Environment Variables

All AI keys can be set via environment variables (they override config values):

Variable	Provider	Description
ANTHROPIC_API_KEY	Anthropic	API key for Claude
OPENAI_API_KEY	OpenAI	API key for GPT models
AWS_BEARER_TOKEN_BEDROCK	Bedrock	Bearer token for Bedrock

Model Selection

Set model: auto (or omit it) to use the best default model for each provider:

Provider	Default Model
Anthropic	claude-sonnet-4-20250514
OpenAI	gpt-4o
Bedrock	anthropic.claude-3-5-sonnet-20241022-v2:0

Or specify any model explicitly: model: claude-3-haiku-20240307

API Version

• v3 (default) — For self-hosted Signoz instances (/api/v3/query_range)
• v5 — For Signoz Cloud (/api/v5/query_range)

Backward Compatibility

The legacy anthropic_key at the root level of config.yaml still works. If set, it's used as fallback when ai.anthropic_key is empty.

Requirements

• A Signoz instance (self-hosted or cloud)
• An AI API key from one of: Anthropic, OpenAI, or Amazon Bedrock

Contributing

Contributions are welcome! Please open an issue or submit a PR.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT © Lester Barahona