---
name: agent-observability-dashboard-313bf40b6088
description: "Unified observability for OpenClaw agents \u2014 metrics, traces, and performance insights."
---

# 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

```bash
# 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:
```json
{
  "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

```bash
# 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


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/orosha-ai/agent-observability-dashboard/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/orosha-ai/agent-observability-dashboard/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Data Analyst, DevOps / SRE, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API, Backend
Tools: Influxdb, Sqlite
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 9ca775ac9ce6747703ca1880e98b93e2d28682f4f3ef8aeee1f5da3e65799c01