openclaw-context-meter

Automatic context window usage footer for OpenClaw Telegram bots.

After every bot response, sends a small status message showing how much of the model's context window is used:

📊 45k / 200k (22%)

When compaction is detected (tokens drop significantly), shows the before/after:

📊 30k / 200k (15%) — сжат с 150k

Features

• Zero-cost: uses agent_end + message_sent hooks only, no extra API calls
• No subprocesses: model context windows are hardcoded (no execSync OOM risk)
• Smart filtering: skips tool_use turns, only sends footer after final text response
• Debounced: waits 1.5s after last message to avoid footer mid-stream
• Multi-agent: works with multiple agents and Telegram accounts
• Compaction detection: detects token drops and shows before/after stats

Install

From npm (recommended)

cd ~/.openclaw/extensions
npm pack openclaw-context-meter
tar xzf openclaw-context-meter-*.tgz
mv package context-meter
rm openclaw-context-meter-*.tgz

Manual

mkdir -p ~/.openclaw/extensions/context-meter
cp index.ts openclaw.plugin.json ~/.openclaw/extensions/context-meter/

Enable in config

Add to ~/.openclaw/openclaw.json:

{
  "plugins": {
    "allow": ["context-meter"],
    "entries": {
      "context-meter": { "enabled": true }
    }
  }
}

Restart gateway:

systemctl --user restart openclaw-gateway

Requirements

• OpenClaw >= 2026.3.22 (for plugin manifest support and compaction notifications)
• Telegram channel enabled

Supported models (40+)

| Provider | Models | Context Window | |----------|--------|---------------| | OpenAI Codex | gpt-5.4 / pro / mini / nano, gpt-5.3-codex | 272k | | OpenAI | gpt-5.2, gpt-5.1, gpt-5-mini / nano | 400k | | Anthropic | claude-opus-4-6 | 1M | | Anthropic | claude-sonnet-4-6 / 4-5, claude-haiku-4-5 | 200k | | Google | gemini-3-pro / flash | 1M | | Qwen | qwen3.5-plus, qwen3-coder-plus | 1M | | Qwen | qwen3-coder-next, coder-model | 262k | | MiniMax | M2.5 / M2.7 / M2.1 | 200k | | Z.AI | glm-5, glm-5-turbo, glm-4.7 | 205k | | xAI | grok | 131k | | Mistral | mistral-large | 262k | | Moonshot | kimi-k2.5, kimi-code | 262k | | Xiaomi | mimo-v2-pro | 262k |

Unknown models default to 200k. To add a model, edit MODEL_CONTEXT_WINDOWS in index.ts.

How it works

1. agent_end hook fires after each bot response — plugin checks if it was a text response (not tool_use) and finds the Telegram chat ID from the session
2. message_sent hook fires for each Telegram message delivery — plugin debounces with 1.5s timer
3. After the last message is delivered, reads the session JSONL file tail to get current token count
4. Calculates percentage of model's context window and sends the footer via Telegram Bot API

v2.0 vs v1.0

v1.0 used execSync("openclaw models list --json") to dynamically discover model context windows. This spawned a full OpenClaw process (~2GB RAM) on every plugin load, causing OOM on servers with limited memory.

v2.0 hardcodes model context windows — zero memory overhead, zero subprocesses.

License

MIT