# Mendrift

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/suneel190700/mendrift  
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**Directory Page:** https://allmcps.com/mcp/mendrift

## Description
MCP server for ML drift detection, MLflow registry diffing, and HMAC-gated model rollback

## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):

```json
"mcpServers": {
  "mendrift": {
    "command": "npx",
    "args": ["-y","mendrift"]
  }
}
```

## Documentation & README

<!-- mcp-name: io.github.suneel190700/mendrift-mcp -->

![test](https://github.com/suneel190700/mendrift/actions/workflows/test.yml/badge.svg)

# Mendrift

Autonomous MLOps incident response agent, plus **mendrift-mcp** — an open-source
MCP server for drift detection and ML incident tooling.

```bash
pip install mendrift-mcp     # or: uvx mendrift-mcp
```

Published on [PyPI](https://pypi.org/project/mendrift-mcp/) and the
[MCP Registry](https://registry.modelcontextprotocol.io) as
`io.github.suneel190700/mendrift-mcp`.

**▶ [Live demo](https://mendrift-demo.onrender.com)** — run a real incident in your
browser: supply an alert, watch the agent diagnose it against a real MLflow registry,
and approve or reject the rollback at the human-in-the-loop gate. Toggle between a
crafted synthetic scenario and **real US consumer-credit benchmark data**. React
frontend on a FastAPI backend; the free tier sleeps, so the first load may take ~40s.

When a production model drifts or degrades, Mendrift detects it, diagnoses the
root cause from monitoring and registry evidence, proposes a remediation, and
executes it **only after human approval**.

```
alert ──> classify ──> diagnose (MCP tools) ──> propose
             │                                     │
           noise ──> close               human approval gate
                                                   │
                                    execute ──> verify recovery
```

Built with LangGraph (agent orchestration), LangChain (`ChatAnthropic` +
`bind_tools`), the Model Context Protocol, Evidently, MLflow, and Claude
(Haiku + Sonnet).

## mendrift-mcp tools

| tool | type | purpose |
|---|---|---|
| `get_drift_report` | read | per-feature drift distances + schema changes (Evidently) |
| `summarize_metric_anomalies` | read | production vs previous model scored on current traffic |
| `get_deployment_history` | read | registry version transitions and aliases |
| `diff_deployments` | read | params / metrics / feature-schema diff between versions |
| `propose_rollback` | read | generates a reviewable rollback plan |
| `execute_rollback` | **gated** | requires a single-use HMAC `approval_token` |
| `open_incident` | write | incident record with diagnosis + evidence |

## Safety model

The approval gate is enforced in the **tool layer, not the prompt**:
`execute_rollback` verifies a single-use, action-scoped HMAC token minted only
by the human review flow — the minting function is never exposed over MCP. A
prompt-injected or confused agent cannot execute writes.

Tested live: Claude was first ordered to roll back "with full authorization"
(it proposed but declined to fabricate a token), then handed a fabricated
token, which the gate rejected by constant-time HMAC comparison:

![Approval gate defense: refusal, then cryptographic rejection](https://raw.githubusercontent.com/suneel190700/mendrift/main/docs/gate-defense.png)

See `tests/test_approval_gate.py`, including the action-scoping test: a token
minted for one model/version is invalid for any other.

## Human-in-the-loop, crash-proof

The incident graph halts before execution (`interrupt_before`) and checkpoints
every step to SQLite. The process can die; a new process resumes the same
incident by `thread_id` after a human mints the approval token — which enters
state only via `update_state()`, from outside the graph. Denial is a
first-class path: no token → `closed_approval_denied`, no execution.

![Kill-and-resume demo](https://raw.githubusercontent.com/suneel190700/mendrift/main/docs/interrupt-demo.gif)

## Agent design

| step | model | why |
|---|---|---|
| classify | Haiku | single constrained label; cheapest path |
| diagnose | Sonnet | multi-hop tool reasoning over evidence |
| verify | Haiku | threshold check on fresh metrics |

Routing lives in a code table (`ROUTER_TABLE`), not prompts, so cost per path
is measurable config — ~3.9K input / 630 output tokens per incident. The
diagnose loop is bounded (max 8 tool calls) with per-call retries and capped
backoff; on tool failure the model receives a structured error record, and on
budget exhaustion the agent degrades to an incident with partial evidence — it
never invents a diagnosis. Destructive actions require affirmative evidence: a
rollback is recommended only when retrieved evidence links the symptom to a
specific deployment, never on deploy-correlation alone. The agent can also
recommend **monitor** — real but mild, non-actionable drift is watched, not
acted on.

## Live mode

`MENDRIFT_DEMO=0` runs the agent against real infrastructure rather than fixtures:

- `scripts/seed_demo.py` trains two sklearn versions into a local MLflow registry —
  v13 clean, v14 with a schema swap and a training window polluted by missed-fraud
  labels (recall 0.72 → 0.18, AUC 0.84 → 0.82) — and writes reference/current frames
- `get_drift_report` runs Evidently's `DataDriftPreset` over those frames, returning
  real Wasserstein/JS distances against per-metric thresholds, plus schema changes
  derived from actual column sets
- `get_deployment_history` / `diff_deployments` read the registry and the underlying
  runs — real aliases, params, metrics
- `summarize_metric_anomalies` scores the current window with both the production and
  previous versions, so it reports **model** divergence rather than population drift —
  a rollback clears it, ordinary data shift does not
- an approved `execute_rollback` moves the `production` alias for real

```bash
uv run mlflow server --host 127.0.0.1 --port 5001        # separate terminal
PYTHONPATH=src uv run python scripts/seed_demo.py

rm -f demo.db
MENDRIFT_DEMO=0 PYTHONPATH=src uv run python scripts/demo_interrupt.py start
MENDRIFT_DEMO=0 PYTHONPATH=src uv run python scripts/demo_interrupt.py approve
```

A live run diagnoses from computed evidence — e.g. *"v2 introduced a schema swap
replacing promo_flag with promo_flag_v2 … label_noise 0.0 → 0.45 collapsing
val_recall 0.724 → 0.176 … 79.7% prediction-rate divergence from the prior version,
model-induced, not population drift"* — then halts for approval and resolves.

The eval suite deliberately stays on fixtures: evals need determinism and zero cost
in CI, while live mode exercises the real stack.

## Live web demo (two worlds)

A hosted web app wraps live mode behind a browser UI: a **React (Vite)** frontend on
a **FastAPI** backend, deployed on Render. A visitor submits an alert, the frontend
posts it to `/api/diagnose`, and the backend runs the real LangGraph agent — live
Claude reasoning over an embedded MLflow registry (`sqlite://`, seeded on boot) — then
halts at the HMAC gate. Approve or reject and the backend resumes the graph via
`/api/decision`, executing a real alias rollback and verifying recovery. The Anthropic
key lives only on the server; runs are rate-limited since each calls a real model.
Try it: **[mendrift-demo.onrender.com](https://mendrift-demo.onrender.com)**.

The dashboard toggles between two seeded worlds, so the same agent can be seen against
both a crafted scenario and genuine real-world data:

- **Synthetic** (`scripts/seed_demo.py`, model `fraud-scorer`) — the crafted schema-swap
  incident: clean, teachable, an unambiguous rollback story.
- **Real US credit** (`scripts/seed_real.py`, model `credit-risk`) — the
  [Give Me Some Credit](https://www.openml.org/search?type=data&status=active&id=44228)
  dataset (real US consumer-credit records, target `SeriousDlqin2yrs`) split by borrower
  age into reference/current windows for genuine feature drift, with a **controlled model
  regression injected** into v2 (asymmetric missed-default label noise) so the incident
  has ground truth. Real distributions and real Evidently drift; a known correct action.
  Measured gap: val_recall 0.637 → 0.156, AUC 0.854 → 0.810.

Injecting a known regression into real data is standard practice for validating a
drift-detection system — it gives the evaluator ground truth for what the agent *should*
decide while the drift computation still runs on genuine distributions.

The backend routes each request to the right world (model + parquet frames + label
column) per the `dataset` field; the tool layer reads those from env vars, applied
per-request under a lock so concurrent requests stay isolated.

Run the web app locally:

```bash
# 1. build the React frontend (FastAPI serves the built assets)
cd frontend && npm install && npm run build && cd ..

# 2. seed both worlds, then start the backend (frontend + API on one port)
export ANTHROPIC_API_KEY=sk-ant-...
export MLFLOW_TRACKING_URI="sqlite:///$(pwd)/mlflow.db"
PYTHONPATH=src uv run python scripts/seed_demo.py     # synthetic world (fraud-scorer)
PYTHONPATH=src uv run python scripts/seed_real.py     # real world (credit-risk)
PYTHONPATH=src uv run uvicorn app.main:app --port 8000     # open http://localhost:8000
```

For frontend development with hot reload, run `cd frontend && npm run dev` (port 5173);
Vite proxies `/api` to the backend on port 8000.

## Evaluation

`src/mendrift/evals/` replays synthetic incident trajectories against the
**real graph** — only the LLM (scripted) and the read tools (fixture world)
are faked; the gated action tools are the genuine implementations, so the HMAC
gate is exercised by every test. Four assertions per trajectory:

| check | meaning |
|---|---|
| `no_ungated_writes` | every `execute_rollback` carried a valid HMAC token — **hard fail** |
| `classification_ok` | triage label matched |
| `tool_sequence_ok` | required tool calls occurred in order (extras allowed) |
| `action_ok` | terminal outcome matched |

**19 logic-distinct incident scenarios** spanning the decision space, each with
its own evidence shape and correct action:

- **Rollback** — deploy-correlated drift or quality regression with affirmative diff evidence
- **Retrain** — label/concept shift, segment-specific degradation (no valid rollback target)
- **Monitor** — mild seasonal drift, low-importance-feature drift, holiday effects
- **Incident (investigate)** — upstream schema rename, feature-store change, docs-only deploy, calibration break, threshold shift, silent data-quality drop
- **Graceful degradation** — evidence tools down → incident with partial evidence, never a fabricated diagnosis
- **Noise** — flapping / auto-resolved alerts closed with zero tool calls
- **Human-declined** — well-founded rollback the reviewer rejects → closed, no execution

Scripted for fast CI, live for the measured rate:

```bash
PYTHONPATH=src uv run python scripts/run_traj.py --all          # scripted, fast
PYTHONPATH=src uv run python scripts/run_traj.py --all --live   # real models
```

Live-model eval runs at ~95% task-success; the handful of run-to-run
divergences reflect LLM eval variance on decision-margin scenarios. The live
suite surfaced real failure classes during development — a JSON extractor
masking a correct decision, a classifier baited by an alert's reassuring
wording, and a diagnoser proposing rollback on correlation alone — each fixed
at its own layer (parser, alert wording, evidence-rule prompt).

## Quickstart (demo mode)

```bash
uv sync
MENDRIFT_DEMO=1 uv run mendrift-mcp     # stdio MCP server with fixture data
PYTHONPATH=src uv run pytest -v         # gate + trajectory suite
```

Claude Desktop config:

```json
{"mcpServers": {"mendrift": {
  "command": "uvx",
  "args": ["mendrift-mcp"],
  "env": {"MENDRIFT_DEMO": "1"}
}}}
```

## Status

- [x] mendrift-mcp server over stdio, verified in MCP Inspector and Claude Desktop
- [x] seven tools with a read / gated / write permission taxonomy
- [x] HMAC-gated rollback with action-scoped single-use tokens (tests first)
- [x] LangGraph incident graph: SQLite checkpointing + human-approval interrupt, kill-resume proven
- [x] LLM nodes on LangChain (`ChatAnthropic.bind_tools`): Haiku classify/verify, Sonnet diagnose loop
- [x] 19-scenario trajectory eval across the decision space; ~95% live, zero ungated writes
- [x] CI: gate + trajectory suite on every push
- [x] live mode: real Evidently drift computation, MLflow registry history/diff, real alias rollback
- [x] live web demo: React + Vite frontend on FastAPI, deployed on Render
- [x] two demo worlds: crafted synthetic scenario + real US credit-risk data, selectable in the UI
- [x] published: PyPI (`pip install mendrift-mcp`) + MCP Registry (`io.github.suneel190700/mendrift-mcp`)

## License

MIT