Agent Observability Dashboard 📊

Unified observability for OpenClaw agents — metrics, traces, and performance insights.

What It Does

OpenClaw agents need production-grade visibility. Multiple platforms exist (Langfuse, Langsmith, AgentOps) but no unified view.

Agent Observability Dashboard provides:

• Metrics tracking — Latency, success rate, token usage, error counts
• Trace visualization — Tool chains, decision flows, session timelines
• Cross-agent aggregation — Compare performance across multiple agents/sessions
• Exportable reports — JSON, CSV, markdown for human review
• Alert thresholds — Notify when metrics exceed limits

Problem It Solves

• No centralized view of OpenClaw agent performance
• Hard to debug across multiple tool calls
• No way to compare agents or track regressions
• Production monitoring is enterprise-grade; agents need the same

Usage

# Start dashboard server
python3 scripts/observability.py --dashboard

# Record metrics from a session
python3 scripts/observability.py --record --session agent:main --latency 1.5 --success true

# View session trace
python3 scripts/observability.py --trace --session agent:main:12345

# Get performance report
python3 scripts/observability.py --report --period 24h

# Export to CSV
python3 scripts/observability.py --export metrics.csv

# Set alert thresholds
python3 scripts/observability.py --alert --metric latency --threshold 5.0

Metrics Tracked

Category	Metric	Description
Performance	Latency	Tool call latency (ms)
	Throughput	Calls per second
Success	Success Rate	% of successful tool calls
	Error Count	Failed operations
Cost	Token Usage	Input + output tokens
	API Cost	Estimated cost in USD
Quality	Hallucinations	Detected false outputs
	Corrections Needed	User corrections

Trace Format

Each tool call is logged with:

• Timestamp
• Agent session ID
• Tool name + parameters
• Latency
• Success/failure
• Token usage
• Error details (if failed)

Example trace:

{
  "session_id": "agent:main:12345",
  "trace": [
    {
      "timestamp": "2026-01-31T14:00:00Z",
      "tool": "web_search",
      "params": {"query": "agent observability"},
      "latency_ms": 1234,
      "success": true,
      "tokens_used": 150
    },
    {
      "timestamp": "2026-01-31T14:00:02Z",
      "tool": "memory_write",
      "params": {"content": "..."},
      "latency_ms": 45,
      "success": true,
      "tokens_used": 0
    }
  ]
}

Architecture

┌─────────────────┐
│  Instrumentation│  ← Auto-capture from OpenClaw logs
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Metrics Store  │  ← SQLite/InfluxDB for time-series
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Analytics      │  ← Aggregations, trends, anomalies
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Dashboard UI  │  ← Web interface (Flask/FastAPI)
└─────────────────┘

Requirements

• Python 3.9+
• flask (for dashboard web UI)
• pandas (for analytics)
• influxdb-client (optional, for production storage)

Installation

# Clone repo
git clone https://github.com/orosha-ai/agent-observability-dashboard

# Install dependencies
pip install flask pandas influxdb-client

# Run dashboard
python3 scripts/observability.py --dashboard
# Open http://localhost:5000

Inspiration

• Dynatrace AI Observability App — Enterprise-grade unified observability
• Langfuse vs AgentOps benchmarks — Comparison of platforms
• Microsoft .NET tracing guide — Practical implementation patterns
• OpenLLMetry — OpenTelemetry integration for LLMs

Local-Only Promise

• Metrics stored locally (SQLite/InfluxDB)
• Dashboard runs locally
• No data sent to external services

Version History

• v0.1 — MVP: Metrics tracking, trace visualization, dashboard UI
• Roadmap: InfluxDB integration, anomaly detection, multi-agent comparison