What metrics does llmprobe measure?

It measures time to first token, total request latency, generation throughput in tokens per second, output tokens, and request errors.

Can llmprobe be used in CI?

Yes. The probe command supports fail-on settings that return exit code 1 for errors, degraded endpoints, or both.

Can it run continuously?

Yes. The watch command probes configured endpoints at a specified interval and can output JSONL or expose Prometheus and OpenTelemetry metrics.

Llmprobe

monitoringllmsynthetic-monitoringprometheusopentelemetry

Monitor LLM endpoints with synthetic probes for TTFT, latency, throughput, errors, and deployment regressions.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾

No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for Jwrede/llmprobe, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.

Overview

llmprobe is a CLI and MCP server for probing hosted LLM APIs and OpenAI-compatible inference servers. Use it for one-off health checks, continuous monitoring, or CI gates based on endpoint thresholds. It exports results through JSON, Prometheus, and OpenTelemetry.

Use cases

Measure TTFT and total latency across configured models

Detect provider degradation with recurring synthetic probes

Block deployments when probe thresholds fail

Compare throughput and error rates across endpoints

Export monitoring data to Prometheus or OpenTelemetry

Key features

Measures TTFT, total latency, tokens per second, output tokens, and errors

Supports OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible endpoints

Runs one-off probes or continuous monitoring intervals

Provides table, JSON, JSONL, and terminal dashboard output

Generates Markdown reports with p50, p95, and p99 percentiles

Exports Prometheus and OpenTelemetry metrics

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Llmprobe.

Extracted Tool Capabilities

Measures TTFT, total latency, tokens per second, output tokens, and errors

Supports OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible endpoints

Runs one-off probes or continuous monitoring intervals

Provides table, JSON, JSONL, and terminal dashboard output

Generates Markdown reports with p50, p95, and p99 percentiles

Exports Prometheus and OpenTelemetry metrics

Documentation Overview

llmprobe

Synthetic monitoring and CI smoke tests for LLM inference endpoints. Measure TTFT, latency, throughput, and errors. Single binary, zero SDKs.

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

Public benchmark

llm-bench uses llmprobe to run a continuous public benchmark of major LLM APIs. It publishes a live dashboard at bench.jonathanwrede.de and raw JSONL data in 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 (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:

Terminal

claude plugin install Jwrede/llmprobe

Or register the MCP server directly:

Terminal

claude mcp add --transport stdio llmprobe -- llmprobe mcp

llmprobe runs locally and only contacts LLM endpoints you configure. See 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:

Code

$ 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.

server.ts

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

Code

$ 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:

Code

| 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:

Code

Failed endpoints (1/4):
  openai/gpt-4o  DEGRADED  TTFT=280ms  Latency=950ms  Tok/s=32.1

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

bash

llmprobe mcp

This starts the MCP server over stdio.

Registering with Claude Code

Terminal

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Llmprobe

It supports OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible servers such as vLLM, Ollama, OpenRouter, Groq, Together AI, Fireworks, DeepSeek, and Mistral.

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Technical Specs & Signals

CategoryMonitoring

PricingFree

43Quality signal: Fair · 43/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

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Documentation & tools20/30

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Llmprobe

monitoringllmsynthetic-monitoringprometheusopentelemetry

Monitor LLM endpoints with synthetic probes for TTFT, latency, throughput, errors, and deployment regressions.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON ▾

Overview

Use cases

Measure TTFT and total latency across configured models

Detect provider degradation with recurring synthetic probes

Block deployments when probe thresholds fail

Compare throughput and error rates across endpoints

Export monitoring data to Prometheus or OpenTelemetry

Key features

Measures TTFT, total latency, tokens per second, output tokens, and errors

Supports OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible endpoints

Runs one-off probes or continuous monitoring intervals

Provides table, JSON, JSONL, and terminal dashboard output

Generates Markdown reports with p50, p95, and p99 percentiles

Exports Prometheus and OpenTelemetry metrics

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Llmprobe.

Extracted Tool Capabilities

Measures TTFT, total latency, tokens per second, output tokens, and errors

Supports OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible endpoints

Runs one-off probes or continuous monitoring intervals

Provides table, JSON, JSONL, and terminal dashboard output

Generates Markdown reports with p50, p95, and p99 percentiles

Exports Prometheus and OpenTelemetry metrics

Documentation Overview

llmprobe

Synthetic monitoring and CI smoke tests for LLM inference endpoints. Measure TTFT, latency, throughput, and errors. Single binary, zero SDKs.

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

Public benchmark

llm-bench uses llmprobe to run a continuous public benchmark of major LLM APIs. It publishes a live dashboard at bench.jonathanwrede.de and raw JSONL data in 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 (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:

Terminal

claude plugin install Jwrede/llmprobe

Or register the MCP server directly:

Terminal

claude mcp add --transport stdio llmprobe -- llmprobe mcp

llmprobe runs locally and only contacts LLM endpoints you configure. See 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:

Code

$ 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.

server.ts

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

Code

$ 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:

Code

| 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:

Code

Failed endpoints (1/4):
  openai/gpt-4o  DEGRADED  TTFT=280ms  Latency=950ms  Tok/s=32.1

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

bash

llmprobe mcp

This starts the MCP server over stdio.

Registering with Claude Code

Terminal

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Llmprobe

It supports OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, and OpenAI-compatible servers such as vLLM, Ollama, OpenRouter, Groq, Together AI, Fireworks, DeepSeek, and Mistral.

AllMCPs Directory Badge

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Technical Specs & Signals

CategoryMonitoring

PricingFree

43Quality signal: Fair · 43/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools20/30

Adoption & activity2/15

Community engagement0/10

★ Featured

AllMCPs Server

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More in 📊 Monitoring →Best MCP servers for Monitoring & Observability →Alternatives to Llmprobe →Install in Claude Desktop Install in Cursor Install in VS Code