![llmprobe](demo/tui-thumbnail.png?v=2) # llmprobe > Synthetic monitoring and CI smoke tests for LLM inference endpoints. Measure TTFT, latency, throughput, and errors. Single binary, zero SDKs. [![CI](https://github.com/Jwrede/llmprobe/actions/workflows/ci.yml/badge.svg)](https://github.com/Jwrede/llmprobe/actions/workflows/ci.yml) [![Go](https://img.shields.io/badge/go-1.25+-00ADD8?logo=go)](https://go.dev) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) [![llmprobe MCP server](https://glama.ai/mcp/servers/Jwrede/llmprobe/badges/score.svg)](https://glama.ai/mcp/servers/Jwrede/llmprobe) llmprobe is a CLI tool for LLM serving reliability. It probes hosted APIs or OpenAI-compatible inference servers, then reports the metrics that matter for production user experience: time to first token (TTFT), total latency, generation throughput (tokens/sec), and error rates. Use it as a one-off health check, a continuous monitor, or a CI gate that blocks deploys when your LLM provider is degraded. ![demo](demo/llmprobe-demo.gif) ## Public benchmark [llm-bench](https://github.com/Jwrede/llm-bench) uses llmprobe to run a continuous public benchmark of major LLM APIs. It publishes a live dashboard at [bench.jonathanwrede.de](https://bench.jonathanwrede.de) and raw JSONL data in [Jwrede/llm-bench-data](https://github.com/Jwrede/llm-bench-data). This is the intended use case: repeated synthetic probes that make LLM latency, TTFT regressions, throughput drops, and provider degradation visible before users report them. ## Install **Download a prebuilt binary** from the [latest release](https://github.com/Jwrede/llmprobe/releases/latest) (Linux, macOS, Windows; amd64 and arm64). Or install from source: ```bash go install github.com/Jwrede/llmprobe@latest llmprobe version ``` ### Claude Code plugin Install as a Claude Code plugin for `/llmprobe` skill and MCP tools: ```bash claude plugin install Jwrede/llmprobe ``` Or register the MCP server directly: ```bash claude mcp add --transport stdio llmprobe -- llmprobe mcp ``` llmprobe runs locally and only contacts LLM endpoints you configure. See [PRIVACY.md](PRIVACY.md) for details. ## Quick start llmprobe works with OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible endpoints such as vLLM, Ollama, OpenRouter, Groq, Together AI, Fireworks, DeepSeek, and Mistral. Create a `probes.yml` (or copy the included example): ```yaml providers: - name: openai api_key: ${OPENAI_API_KEY} models: - name: gpt-4o thresholds: max_ttft: 2s - name: gpt-4o-mini thresholds: max_ttft: 500ms - name: anthropic api_key: ${ANTHROPIC_API_KEY} models: - name: claude-sonnet-4-20250514 thresholds: max_ttft: 1s ``` Run a probe: ``` $ llmprobe probe Provider Model Status TTFT Latency Tok/s Tokens Error -------- ----- ------ ---- ------- ----- ------ ----- openai gpt-4o healthy 312ms 2100ms 68.4 42 openai gpt-4o-mini healthy 98ms 814ms 112.3 56 anthropic claude-sonnet-4-20250514 healthy 420ms 2831ms 52.1 38 azure gpt-4o healthy 289ms 1950ms 71.2 44 bedrock anthropic.claude-3-5... degraded 1820ms 4510ms 28.1 38 4 healthy, 1 degraded, 0 errors ``` ## What it measures | Metric | What it means | |--------|--------------| | **TTFT** | Time from request send to first content token. This is what users feel as "lag" before the response starts streaming. | | **Latency** | Total time from request to stream close. | | **Tok/s** | Generation throughput: tokens produced per second after the first token. Calculated as `token_count / (latency - ttft)`. | | **Tokens** | Total output tokens. Prefers provider usage metadata when available, falls back to SSE event counting. | | **Status** | `healthy` if all thresholds pass, `degraded` if any threshold is exceeded, `error` if the request failed. | ## Commands ### `llmprobe probe` One-off health check. Probes all configured endpoints and prints results. ```bash llmprobe probe # table output llmprobe probe -f json # JSON output llmprobe probe --fail-on degraded # exit 1 if any endpoint is degraded llmprobe probe -c custom-config.yml # custom config path ``` Exit codes for CI: | `--fail-on` | Exit 0 | Exit 1 | |-------------|--------|--------| | `error` (default) | healthy or degraded | any error | | `degraded` | healthy only | degraded or error | | `none` | always | never | ### `llmprobe watch` Continuous monitoring. Probes all endpoints on an interval and prints a summary line per iteration. ```bash llmprobe watch # default 60s interval llmprobe watch --interval 30s # custom interval llmprobe watch --tui # live terminal dashboard with TTFT chart llmprobe watch --tui --load data.jsonl # load historical data into the dashboard llmprobe watch -f json # JSONL output (one line per result) llmprobe watch --prometheus :9090 # expose Prometheus metrics llmprobe watch --otel localhost:4317 # export OpenTelemetry metrics via OTLP/gRPC ``` The `--tui` flag launches a live terminal dashboard with a TTFT chart, color legend, and statistics table. Use `--load` to import historical JSONL data (from `llmprobe watch -f json > data.jsonl`). ![llmprobe](demo/tui-screenshot.png?v=2) ``` $ llmprobe watch --interval 30s Watching 4 endpoints every 30s (Ctrl+C to stop) [14:01:02] All 4 endpoints healthy. [14:01:32] All 4 endpoints healthy. [14:02:02] 3 healthy, 1 degraded, 0 errors. DEGRADED: openai/gpt-4o (TTFT 1820ms) [14:02:32] All 4 endpoints healthy. ``` ### `llmprobe report` Generate a Markdown summary from JSONL probe data with p50/p95/p99 percentiles for TTFT, latency, and throughput per endpoint. ```bash llmprobe report data.jsonl ``` Output: ``` | Provider | Model | Probes | Errors | TTFT p50 | TTFT p95 | ... | Tok/s p50 | ... |----------|-------|--------|--------|----------|----------|-----|-----------|---- | openai | gpt-4o | 100 | 2 | 115ms | 188ms | ... | 46.9 | ... ``` ### `llmprobe baseline` Create a baseline file from historical JSONL data for regression detection. ```bash llmprobe baseline data.jsonl -o baseline.json ``` Reference the baseline in your config to use multiplier-based thresholds: ```yaml baseline: baseline.json providers: - name: openai api_key: ${OPENAI_API_KEY} models: - name: gpt-4o thresholds: max_ttft_multiplier: 2.0 # fail if TTFT > 2x baseline p50 max_latency_multiplier: 2.5 # fail if latency > 2.5x baseline p50 ``` This lets you detect regressions relative to your own historical data rather than setting absolute thresholds. ### `llmprobe version` Print the installed binary version. ```bash llmprobe version ``` ## CI integration Use `llmprobe probe` as a pre-deploy gate: ```yaml # .github/workflows/deploy.yml - name: Check LLM providers env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} run: | go install github.com/Jwrede/llmprobe@latest llmprobe probe --fail-on degraded ``` This blocks the deploy if any LLM provider is experiencing degraded performance right now. When a probe fails, the output shows only the failing endpoints: ``` Failed endpoints (1/4): openai/gpt-4o DEGRADED TTFT=280ms Latency=950ms Tok/s=32.1 ``` ## MCP server [![llmprobe MCP server](https://glama.ai/mcp/servers/Jwrede/llmprobe/badges/card.svg)](https://glama.ai/mcp/servers/Jwrede/llmprobe) llmprobe includes a built-in [Model Context Protocol](https://modelcontextprotocol.io/) server, allowing Claude Code and other MCP hosts to check LLM API health directly from an agent workflow. ### Running the server ```bash llmprobe mcp ``` This starts the MCP server over stdio. ### Registering with Claude Code ```bash claude mcp add --transport stdio llmprobe -- llmprobe mcp ``` Once registered, Claude Code can call llmprobe tools during any conversation. ### Available tools | Tool | Description | |------|-------------| | `probe_all` | Probe all configured endpoints from `probes.yml`. Returns TTFT, latency, throughput, and health status for every model. Accepts an optional `config` parameter for a custom config path. | | `probe_model` | Probe a single model without a config file. Requires `provider`, `model`, and `api_key_env`. Supports optional `base_url` for OpenAI-compatible endpoints and optional `label` for display. | | `list_providers` | List all providers and models in the config file with their thresholds. Use this to discover available models before probing. | | `get_config` | Return the full parsed configuration including defaults, providers, models, and thresholds. | **Example use case:** An agent calls `list_providers` to see what models are configured, then `probe_all` to verify they are healthy before deploying changes. ## Configuration ```yaml defaults: prompt: "Hello" # probe prompt max_tokens: 20 # max output tokens timeout: 30s # per-probe timeout concurrency: 5 # max parallel probes providers: - name: openai # openai, anthropic, google, azure, bedrock label: openai-prod # optional display name; useful for multiple OpenAI-compatible endpoints api_key: ${OPENAI_API_KEY} # env var expansion base_url: https://custom.api # optional, override endpoint models: - name: gpt-4o prompt: "Say hello." # override default prompt max_tokens: 10 # override default max_tokens response_format: json # optional; OpenAI-compatible JSON mode validate_json: true # optional; mark degraded if returned content is not valid JSON thresholds: max_ttft: 2s # alert if TTFT exceeds this max_latency: 10s # alert if total latency exceeds this min_tokens_per_sec: 20 # alert if throughput drops below this max_ttft_multiplier: 2.0 # optional; compare against baseline p50 max_latency_multiplier: 2.5 - name: azure api_key: ${AZURE_OPENAI_API_KEY} base_url: https://your-resource.openai.azure.com api_version: "2024-10-21" # optional, defaults to 2024-10-21 models: - name: gpt-4o # deployment name - name: bedrock access_key: ${AWS_ACCESS_KEY_ID} secret_key: ${AWS_SECRET_ACCESS_KEY} region: us-east-1 models: - name: anthropic.claude-3-5-sonnet-20241022-v2:0 ``` Reasoning models (for example gpt-oss, o-series, DeepSeek R1) spend output tokens on hidden reasoning before the visible answer. With the default `max_tokens: 20` the budget can run out before any visible text arrives. llmprobe counts only visible content tokens for OpenAI-compatible endpoints, so such a probe reports `degraded` with a hint to raise `max_tokens`. For reasoning models, set `max_tokens` to about 256 per model. API keys and AWS credentials support `${ENV_VAR}` syntax. Only credential fields are expanded, so env var references in prompts or model names are left as-is. ### OpenAI-compatible providers Many providers (Groq, Together AI, Fireworks, DeepSeek, Mistral, OpenRouter, Ollama, vLLM) expose an OpenAI-compatible API. These work out of the box by setting `base_url`. Use the `label` field to distinguish multiple OpenAI-compatible blocks: ```yaml providers: # Groq - name: openai label: groq api_key: ${GROQ_API_KEY} base_url: https://api.groq.com/openai models: - name: llama-3.3-70b-versatile # DeepSeek - name: openai label: deepseek api_key: ${DEEPSEEK_API_KEY} base_url: https://api.deepseek.com models: - name: deepseek-chat # Together AI - name: openai label: together api_key: ${TOGETHER_API_KEY} base_url: https://api.together.xyz models: - name: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo # Local Ollama - name: openai label: ollama api_key: unused base_url: http://localhost:11434 models: - name: llama3.2 ``` See [examples/](examples/) for ready-to-use configs for vLLM, SGLang, and Ollama. ### JSON response validation For OpenAI-compatible endpoints, set `response_format: json` to request JSON mode and `validate_json: true` to mark the probe as `degraded` if the streamed content is not valid JSON. ```yaml providers: - name: openai label: vllm-json api_key: unused base_url: http://localhost:8000 models: - name: meta-llama/Llama-3.1-8B-Instruct prompt: 'Return {"ok": true} as JSON.' response_format: json validate_json: true ``` ## Prometheus metrics Run with `--prometheus` to expose metrics for scraping: ```bash llmprobe watch --interval 30s --prometheus :9090 ``` Available metrics at `/metrics`: | Metric | Type | Labels | |--------|------|--------| | `llmprobe_ttft_seconds` | gauge | provider, model | | `llmprobe_latency_seconds` | gauge | provider, model | | `llmprobe_tokens_per_second` | gauge | provider, model | | `llmprobe_token_count` | gauge | provider, model | | `llmprobe_status` | gauge | provider, model | | `llmprobe_probes_total` | counter | provider, model | | `llmprobe_errors_total` | counter | provider, model | | `llmprobe_ttft_seconds_hist` | histogram | provider, model | | `llmprobe_latency_seconds_hist` | histogram | provider, model | | `llmprobe_tokens_per_second_hist` | histogram | provider, model | The `llmprobe_status` gauge encodes health as: 1 = healthy, 0.5 = degraded, 0 = error. Use this for alerting in Grafana or Alertmanager. ## OpenTelemetry metrics Run with `--otel` to export probe metrics to an OTLP/gRPC collector. ```bash llmprobe watch --interval 30s --otel localhost:4317 ``` Exported metric names: | Metric | Description | |--------|-------------| | `llmprobe.ttft.seconds` | Time to first token in seconds | | `llmprobe.latency.seconds` | Total request latency in seconds | | `llmprobe.tokens_per_second` | Generation throughput | | `llmprobe.token_count` | Output token count from the last probe | | `llmprobe.status` | 1 = healthy, 0.5 = degraded, 0 = error | | `llmprobe.probes.total` | Total probes executed | | `llmprobe.errors.total` | Total probe errors | All metrics include `provider` and `model` attributes. ## Architecture ``` probes.yml -> Config loader (YAML + env var expansion) -> Probe engine (concurrent goroutines per provider/model) -> Provider clients (raw HTTP + SSE parsing, no SDKs) -> Results (TTFT, latency, tokens/sec, status) -> Output (table, JSON, JSONL) ``` Each provider client is a thin HTTP wrapper that sends a streaming request and parses the response. No LLM SDKs are imported. The SSE parser handles both data-only events (OpenAI, Google) and named events (Anthropic). The Bedrock client implements SigV4 signing and AWS binary event stream parsing from scratch. TTFT is measured from the moment the HTTP request is sent to the first event that contains actual content text (not role assignments or metadata). ## Providers | Provider | Endpoint | Auth | Streaming format | |----------|----------|------|-----------------| | OpenAI | `/v1/chat/completions` | `Authorization: Bearer` | SSE, `[DONE]` sentinel | | Anthropic | `/v1/messages` | `x-api-key` header | named-event SSE | | Google | `/v1beta/models/{model}:streamGenerateContent?alt=sse` | `key` query param | SSE | | Azure OpenAI | `/openai/deployments/{model}/chat/completions` | `api-key` header | SSE, `[DONE]` sentinel | | AWS Bedrock | `/model/{model}/converse-stream` | SigV4 | AWS binary event stream | | OpenAI-compat | `/v1/chat/completions` (custom base_url) | `Authorization: Bearer` | SSE | OpenAI-compatible covers: Groq, Together AI, Fireworks, DeepSeek, Mistral, OpenRouter, Ollama, vLLM, and any endpoint that speaks the OpenAI chat completions API. ## Roadmap - More provider-specific examples for self-hosted OpenAI-compatible endpoints - More report formats for long-running monitoring windows - Optional runbook templates for common LLM endpoint failures ## License MIT