llmprobe

Probe LLM API endpoints. Measure TTFT, latency, throughput. Single binary, zero SDKs.

CI Go License: MIT llmprobe MCP server

llmprobe is a CLI tool that probes LLM API endpoints and measures the metrics that matter for production reliability: 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.

Quick start

Download a prebuilt binary from the latest release (Linux, macOS, Windows; amd64 and arm64).

Or install from source:

go install github.com/Jwrede/llmprobe@latest

Create a probes.yml (or copy the included example):

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.

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.

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)

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 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.

CI integration

Use llmprobe probe as a pre-deploy gate:

# .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.

MCP server

llmprobe MCP server

llmprobe includes a built-in Model Context Protocol server, allowing Claude Code and other MCP hosts to check LLM API health directly from an agent workflow.

Running the server

llmprobe mcp

This starts the MCP server over stdio.

Registering with Claude Code

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 (openai, anthropic, google, azure, bedrock), model (the model identifier), and api_key_env (env var holding the API key).
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

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
    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
        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

  - 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

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:

providers:
  # Groq
  - name: openai
    api_key: ${GROQ_API_KEY}
    base_url: https://api.groq.com/openai
    models:
      - name: llama-3.3-70b-versatile

  # DeepSeek
  - name: openai
    api_key: ${DEEPSEEK_API_KEY}
    base_url: https://api.deepseek.com
    models:
      - name: deepseek-chat

  # Together AI
  - name: openai
    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
    api_key: unused
    base_url: http://localhost:11434/v1
    models:
      - name: llama3.2

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.

Live benchmark

llm-bench uses llmprobe to run a continuous public benchmark of major LLM APIs. Results are published as an open JSONL dataset and a live terminal dashboard at bench.jonathanwrede.de.

Roadmap

• Baseline tracking: store rolling percentiles, alert when current probe exceeds Nx baseline
• OpenTelemetry metric export for integration with Grafana/Datadog
• Prometheus /metrics endpoint
• Structured output validation: verify JSON mode responses parse correctly

License

MIT