redline

Name: redline
Author: gowtham0992

by gowtham0992version 0.3.0i★ –iRuns on your computer

Registry nameio.github.gowtham0992/redline

Install

Overview

Description from the maker (original English)

Run local prompt regression checks from prompt-response logs in AI coding assistants.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "redline": {
      "command": "uvx",
      "args": [
        "redline-ai"
      ]
    }
  }
}

Run this in your terminal

claude mcp add redline -- uvx redline-ai

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "redline": {
      "command": "uvx",
      "args": [
        "redline-ai"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "redline": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "redline-ai"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account gowtham0992, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32602 servers in this category
Published to the registry more recently than 15% of the other 32602 servers in this category
Basic checks equal to or better than 100% of the other 32602 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is redline safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Rystic

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

redline

Catch prompt regressions before they ship.

Automatic eval suites from the prompt logs you already have.

redline turns real prompt-response logs into local regression tests. It selects representative cases, replays your changed prompt, and shows the behavioral diff before a bad prompt reaches users.

Website · Docs · MCP · MCP Registry · Security · License

Start Here

Install from PyPI:

python -m pip install redline-ai

Run the guided local app with the public proof loaded:

redline app --demo

This generates the public demo reports, opens the local product app, and shows the full import -> suite -> eval -> review workflow. The demo catches ten synthetic regressions without API keys, private logs, a cloud account, or an LLM judge.

Prefer terminal output first:

redline demo --public --compact

The demo writes JSON, Markdown, and self-contained HTML reports under .redline/demo.

Ask redline what to do next:

redline status --reports-dir .redline/demo/reports

status reads local config, suites, reports, history, and audit evidence, then prints the next command instead of leaving you to infer the workflow.

Open the guided local product app on existing reports:

redline app --reports-dir .redline/demo/reports

The app is a local, copy-command workflow: import logs, generate suites, run evals, review regressions, record history, and export CI/MCP setup without the browser executing shell commands.

On headless CI or remote shells, skip browser opening and use the printed HTML path:

redline app --reports-dir .redline/demo/reports --no-open --out .redline/app.html

First-run troubleshooting

redline: command not found: run python -m pip install redline-ai, then confirm python -m pip show redline-ai.
App did not open: use --no-open --out .redline/app.html and open or upload that file from your environment.
Suite not found: run redline suite logs/baseline.jsonl --out redline-suite.json.
Validation failed: run redline validate redline-suite.json --strict and fix the first reported error.
GitHub Action cannot find a suite: commit redline-suite.json or point the action suite input at your prompt manifest.

Full guide: docs/troubleshooting.md.

Product Proof

redline has two proof paths: a fast first-run demo and a larger public-data dogfood run.

Proof	Command or data	Result
First-run demo	`redline demo --public --compact`	10 synthetic regressions caught locally with no API keys.
Internet dogfood	100 prompt-response rows sampled from Databricks Dolly 15k	51 regressions, 27 changed cases, 22 neutral controls, and 0 dashboard warnings.
Release gate	tests, lint, type check, action smoke, and release build	Package, CI, report, dashboard, and MCP paths are validated before publish.

These screenshots are local artifacts from the 100-row internet dogfood run.

Dashboard	HTML report

What Is redline?

redline is an open-source, local-first eval tool for AI teams. It uses logs you already have: prompts, outputs, support tickets, traces, model responses, and production JSONL exports.

Instead of asking you to hand-write evals first, redline generates the first suite from real behavior. You can then run that suite every time a prompt, model, or runner changes.

No cloud account is required. No manual test writing is required. No LLM judge is required for the core regression signal. The package has zero runtime dependencies, which keeps installs fast and the default supply-chain surface small.

How It Works

redline gives you three primitives that cover the prompt-regression loop:

For a first pass on two local logs, use one command:

redline quick-check logs/baseline.jsonl logs/candidate.jsonl --open

It generates a temporary suite, writes JSON/Markdown/HTML reports plus a guided local app under .redline/quick-check, opens the focused HTML report, and prints the concrete behavioral diff. Use --open-app when you want the guided review workflow to open instead of only the focused report.

1. Logs

Start with prompt-response data you already have. Import JSONL, convert exports from tools like Langfuse or Helicone, capture OpenAI/Anthropic SDK calls, or add bounded FastAPI/ASGI middleware.

redline import downloaded.jsonl --detect

redline import downloaded.jsonl --auto-map --preview 3 redline import downloaded.jsonl --auto-map --out logs/baseline.jsonl redline import downloaded.jsonl --input-field instruction --output-field response --preview 3 redline import downloaded.jsonl --input-field instruction --output-field response --out logs/baseline.jsonl redline import langfuse-export.jsonl --preset langfuse --out logs/baseline.jsonl redline suite logs/baseline.jsonl --out redline-suite.json redline cases redline-suite.json

Use --detect when you do not know the field names. Use --preview when the export is new to you; it shows mapped, redacted sample rows without writing a baseline file.

Suite generation prints a readiness score and improvement suggestions. That score measures suite health, not model quality or candidate safety.

2. Suite

redline groups behavior into deterministic signatures and selects representative cases first. You can add pinned edge cases and explicit requirements when a scenario must never be missed.

redline cases redline-suite.json

redline suite add redline-suite.json --prompt "..." --response "..."

3. Eval

Replay a changed prompt or compare candidate outputs. redline names the behavior that broke: missing JSON keys, URLs, numbers, tables, code blocks, refusals, empty answers, or requirement failures.

redline eval --prompt prompts/v2.txt

redline diff redline-suite.json logs/candidate.jsonl

Product Promise

In under five minutes, on a real prompt log, redline should catch one regression you did not want to ship.

That promise is intentionally narrow. redline is not a hosted eval platform, a generic score, or a replacement for human judgment. It is the local safety loop between "I changed the prompt" and "this is safe enough to merge."

Real Workflow

Build a suite from baseline logs:

redline suite logs/baseline.jsonl --out redline-suite.json

Evaluate a changed prompt file through your configured runner:

redline eval --prompt prompts/v2.txt

Or compare candidate outputs you already generated:

redline diff redline-suite.json logs/candidate.jsonl

When redline finds a blocking change, it exits non-zero for CI and prints the reason:

REGRESSION case_004

candidate missing JSON keys: owner, required_action
candidate missing URL: https://example.com/policies/refunds

Confidence: HIGH | fix blocking cases before shipping

What redline Catches

Signal	Example regression
JSON validity and keys	Candidate stops returning valid JSON or drops `owner`.
Tables, lists, and code blocks	Markdown table becomes prose; code fence disappears.
Numbers, URLs, and entities	Refund window, ticket ID, policy URL, or owner is missing.
Empty outputs and refusals	Candidate newly refuses a safe task or returns nothing.
Content drift	Same-shape response changes substantially.
Explicit requirements	Pinned cases require or forbid exact strings.

redline is deterministic and local-first by default. Optional judge commands are available for ambiguous changed cases, but redline does not call a cloud model unless you explicitly configure that command.

That is the point. redline is designed to be the fast merge-blocking gate for regressions that break production systems: invalid JSON, missing required fields, lost tables, empty answers, dropped URLs, changed refusal behavior, and explicit requirement failures. LLM judges are useful for semantic review, but they are slower, cost money, and can be flaky in CI. redline keeps the default gate deterministic, reproducible, and cheap, then lets you add judges only where the structural signal is not enough.

Methodology details live in docs/methodology.md.

Suite generation does not run statistical or embedding clustering by default. It groups logs by deterministic behavior signatures, such as prompt intent, response shape, length bucket, and JSON schema. It picks one representative per group first, then adds high-variance edges and evenly spread prompt-diverse samples from large groups when the case budget allows.

Trust Boundary

A green redline run means no configured high-signal structural blockers were found. It does not prove factual correctness, tone, hallucination safety, policy compliance, or subtle reasoning quality.

That boundary is visible in CLI output and reports because over-trusting eval tools is dangerous. Each reported case includes a confidence and signal (structural, shallow_semantic, requirement, judge, or human_judgment) so reviewers can see why redline is making the call. Use requirements or an optional judge for semantic risks that structural checks cannot prove.

Product Surface

redline is built around the full prompt-regression loop:

redline watch: collect prompt-response observations from logs, Python functions, OpenAI/Anthropic-compatible SDK calls, or ASGI apps, with best-effort common secrets and PII redacted before write by default.
redline import: normalize exported team logs into redline JSONL, with the same best-effort redaction enabled by default. Use --no-redact only for reviewed local-only logs.
RedlineMiddleware: capture bounded JSON FastAPI or ASGI request/response pairs locally, with optional skip diagnostics.
redline redact --check: scan logs for common secrets and PII, then write a scrubbed copy when needed. Redaction is best-effort pattern matching, not a privacy boundary; review sensitive logs before sharing.
redline cluster: inspect deterministic behavior-signature groups

Only the beginning is shown; read the rest in the repo

redline

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Rystic

README from the maker

Start Here

Product Proof

What Is redline?

How It Works

1. Logs

2. Suite

3. Eval

Product Promise

Real Workflow

What redline Catches

Trust Boundary

Product Surface