Claude Code Observability

Real-time monitoring for Claude Code — context window usage, active agents, tool calls, and token consumption. The observability layer your AI coding agent is missing.

Live Context Tool Monitoring Agent Tracking Open Source

The Observability Gap in AI Coding

AI coding agents like Claude Code are powerful — but they're black boxes. You type a prompt, wait, and hope the agent makes progress. When it fails, you have no idea why. When it hits context limits, you get a cryptic error. When it runs 47 tool calls, you can't see what's happening.

Claude HUD by jarrodwatts/claude-hud is the first open-source tool to crack this open — a real-time HUD for Claude Code that shows context usage, active tools, running agents, and todo progress.

"As AI agents become more autonomous, understanding what an agent is doing at any given moment is essential for debugging and optimization. Claude HUD displays 'running agents' and 'active tools,' giving the user a clear window into the AI's decision-making process and current actions." — AIToolly, March 22, 2026

Claude HUD — GitHub Trending Plugin

Context Window Tracking

Real-time display of context usage — see token consumption before hitting limits

Active Tools Monitor

See exactly which tools Claude Code is calling right now — Read, Edit, Bash, Glob...

Running Agents

Track multi-agent workflows — know which agent is doing what at every moment

Todo Progress

Visualize pending tasks and progress — align agent trajectory with your goals

"A Claude Code plugin that shows what's happening — context usage, active tools, running agents, and todo progress." — jarrodwatts/claude-hud on GitHub (1 week ago)

Observability for AI Coding Agents — Full Landscape

Tool	Type	Platform	Context Monitor	Open Source
Claude HUD	IDE Plugin	Claude Code	Yes	Yes
context_monitor.py	CLI Dashboard	OpenClaw	Yes	Yes
agent-memory dashboard	Terminal Dashboard	Any MCP agent	Yes	Yes
Langfuse	LLMOps Platform	OpenAI, LangChain	Partial	Yes
OpenTelemetry + Traceloop	Telemetry	Any LLM app	—	Yes

Why Context Observability Matters

Claude Code's /compact command forces context compaction — but you never know when it's about to happen or what will be lost. Tools like Claude HUD and context_monitor.py give you the visibility to act before data disappears.

As the AI agent ecosystem matures, observability is becoming a first-class requirement:

Resource management — Context windows are finite and expensive
Debugging — Agent failures are opaque without telemetry
Cost control — Token usage directly maps to API costs
Compliance — EU AI Act requires transparency in automated decisions
Multi-agent coordination — Know which agent is doing what

For OpenClaw Agents — Add Memory + Observability

OpenClaw agents (250K+ GitHub stars) can now add both memory and observability:

# 1. Give your agent memory (MCP server)
pip install agent-memory
python -m agent_memory.mcp_server

# 2. Monitor context health
python dashboard.py --json | jq .entries

# 3. context_monitor.py — predict overflow before it happens
python context_monitor.py --path ~/.openclaw/memory_state

Together, agent-memory and context_monitor.py provide:

Memory persistence — memories survive /compact and restarts
Context observability — know token usage in real time
TTL auto-expiration — stale data auto-clears
AES-256 encryption — sensitive memory is encrypted

Get Started

Claude HUD on GitHub agent-memory on GitHub