cli-apimon 0.1.1

CLI tool for API monitoring, analytics, and LLM-powered improvement insights

apimon

A CLI tool for API monitoring, analytics, and AI-powered improvement insights. Designed for both humans and AI agents.

Features

• Proxy-based Monitoring: Sits between clients and your API server to capture all traffic
• Rich Analytics: Hit counts, response times, error rates, percentiles (p50/p90/p95/p99)
• Pattern Detection: Automatically identifies slow routes, chatty APIs, caching opportunities, and security anomalies
• LLM-Powered Insights: Generate AI-driven analysis using OpenAI, Google Gemini, or Anthropic Claude
• Agent-Friendly: Full --json support and --ai mode for non-interactive automation
• Interactive TUI: Textual-based terminal UI for live monitoring

Installation

pip install apimon

For LLM integration (optional):

pip install apimon[llm]

Quick Start

1. Start the proxy server

apimon proxy --target-port 3000

2. Make requests through the proxy

curl http://localhost:8080/api/users

3. View analytics

# Human-friendly dashboard
apimon dashboard

# Or interactive TUI
apimon ui

4. Generate AI insights

export OPENAI_API_KEY="sk-..."
apimon insights --provider openai

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Commands Reference

Command	Description
apimon proxy	Start the proxy server
apimon ui	Interactive TUI dashboard
apimon ui --ai	Machine-readable JSON snapshot
apimon dashboard	Terminal analytics dashboard
apimon stats	Route statistics
apimon requests	Recent requests
apimon request <id>	Detailed view of a request
apimon suggestions	Rule-based improvement suggestions
apimon insights	LLM-powered AI analysis
apimon graph	ASCII graphs of activity
apimon export <file>	Export data to JSON file
apimon clear	Clear all stored data

---

AI Agent Integration

apimon is designed to be used by AI agents. Every command supports --json output, and apimon ui --ai provides a complete snapshot for automated analysis.

Non-Interactive Mode (--ai)

# Get full analytics snapshot as JSON (no TUI)
apimon ui --ai

# Include LLM analysis
apimon ui --ai --provider openai

# Pipe to jq for specific fields
apimon ui --ai | jq .analytics_summary
apimon ui --ai | jq .cache_candidates
apimon ui --ai | jq .unique_error_messages

JSON Output Fields

The --ai mode returns a comprehensive JSON object:

{
  "apimon_version": "0.1.0",
  "db_path": "apimon.db",
  "hours_analyzed": 24,
  "analytics_summary": {
    "total_requests": 8158,
    "error_rate": 48.1,
    "avg_response_time_ms": 26.15,
    "unique_routes": 7
  },
  "response_time_percentiles": {
    "p50": 1.16,
    "p90": 96.52,
    "p95": 123.33,
    "p99": 146.41
  },
  "route_stats": [...],
  "top_routes_by_traffic": [...],
  "route_percentiles": [...],
  "status_code_distribution": [...],
  "method_distribution": [...],
  "error_summary": [...],
  "unique_error_messages": [...],
  "slowest_routes": [...],
  "cache_candidates": [...],
  "hourly_summary": [...],
  "error_rate_trend": [...],
  "suggestions": [...],
  "llm_prompt": "...",
  "llm_provider": "openai",
  "llm_insights": "...",
  "llm_error": null
}

Key Fields for Agents

Field	Description
analytics_summary	Overall stats: total requests, error rate, avg response time
response_time_percentiles	Global p50, p90, p95, p99 latencies
top_routes_by_traffic	Routes ranked by hit count with traffic share %
route_percentiles	Per-route latency percentiles
unique_error_messages	Actual error response bodies grouped by route/status
cache_candidates	GET routes suitable for caching, with benefit scores
error_rate_trend	Hourly error rates to detect spikes
suggestions	Rule-based improvement suggestions
llm_prompt	The exact prompt sent to the LLM (for debugging/reuse)
llm_insights	LLM response (if --provider was specified)

JSON Mode for Individual Commands

# Route statistics
apimon stats --json
apimon stats --json | jq '.[] | select(.error_rate > 10)'

# Recent requests
apimon requests --json --limit 100
apimon requests --json --method POST | jq '.[] | select(.response_status >= 400)'

# Single request detail
apimon request 42 --json

# Suggestions
apimon suggestions --json
apimon suggestions --json | jq '.[] | select(.severity == "high")'

# LLM insights
apimon insights --json --provider openai
apimon insights --json --provider openai | jq -r .insights

# Graph data
apimon graph --json
apimon graph --json | jq .time_series

Example: Agent Workflow

# 1. Get full snapshot
DATA=$(apimon ui --ai --provider openai)

# 2. Check for critical issues
echo "$DATA" | jq '.suggestions[] | select(.severity == "high")'

# 3. Get caching recommendations
echo "$DATA" | jq '.cache_candidates'

# 4. Read LLM analysis
echo "$DATA" | jq -r '.llm_insights'

# 5. Get the prompt for custom LLM calls
echo "$DATA" | jq -r '.llm_prompt' > prompt.txt

---

LLM Providers

Environment Variables

Provider	Environment Variable
OpenAI	OPENAI_API_KEY
Gemini	GEMINI_API_KEY
Anthropic	ANTHROPIC_API_KEY

Usage

# OpenAI (default)
export OPENAI_API_KEY="sk-..."
apimon insights --provider openai
apimon ui --ai --provider openai

# Google Gemini
export GEMINI_API_KEY="..."
apimon insights --provider gemini

# Anthropic Claude
export ANTHROPIC_API_KEY="sk-ant-..."
apimon insights --provider anthropic

# Pass key directly (not recommended for scripts)
apimon insights --provider openai --api-key sk-...

LLM Prompt Contents

The LLM receives comprehensive data including:

• Overall analytics summary
• Response time percentiles (global and per-route)
• Traffic distribution by route
• Unique error messages with response bodies
• Caching candidates with benefit scores
• Error rate trends by hour
• Full route statistics

The prompt asks for:

1. Critical issues requiring immediate attention
2. Performance bottlenecks analysis
3. Caching strategy with TTL recommendations
4. Error pattern analysis
5. Architecture recommendations
6. Prioritized action items

---

Interactive TUI

apimon ui

On startup, you'll be prompted to configure an LLM provider (optional). Press Skip to use the TUI without LLM features.

Keyboard Shortcuts

Key	Action
1	Routes tab
2	Requests tab
3	Analytics tab
r	Refresh data
d	Toggle dark mode
q	Quit

The Analytics tab includes a "Get LLM Insights" button that calls the configured LLM and displays results inline.

---

Proxy Options

apimon proxy --target-host localhost --target-port 3000 --port 8080

Option	Default	Description
--target-host	localhost	Your API server host
--target-port	3000	Your API server port
--port	8080	Proxy listen port
--db-path	apimon.db	SQLite database path

---

Data Storage

All data is stored in a local SQLite database (apimon.db by default).

Route Pattern Normalization

The proxy automatically normalizes parameterized routes:

• /users/123 → /users/{id}
• /posts/abc-def-123 → /posts/{id}
• /api/v2/items → /api/{version}/items

This enables meaningful aggregation of statistics.

Clear Data

apimon clear --yes  # Non-interactive (for scripts/agents)
apimon clear        # Prompts for confirmation

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License

MIT