dow 2.0.3

Drift Observation Workbench - Git for AI behavior.

dow - Drift Observation Workbench

Git for AI behavior. Track how your AI's behavior changes across versions.

Full documentation, logo, design notes, demo, and issue tracker live in the source repository: https://github.com/theflyingrahul/dow

Version the complete inference specification (prompt, model identity and version, sampling settings, evaluation configuration), execute it, and measure semantic drift, stability, and regressions between versions - with causal attribution. Versioning is automatic and Git is a hidden storage backend; you never run git commands.

dow is deliberately slim and data-structure agnostic. Its job is to be extremely reliable at tracking what you changed (prompt, model, sampling, params) and the metrics you care about - and nothing more. It ships no coefficients and no plotting library (you plug those in), it carries any per-item data in an opaque payload it never interprets, and if your captured output is not text you set embedding_model: none to skip the built-in lexical drift. How your project represents or stores its data never dictates dow's design.

Install

pip install dow

Optional providers:

pip install "dow[openai]"         # hosted models and embeddings
pip install "dow[local]"          # local sentence-transformers embeddings
pip install "dow[mcp]"            # Model Context Protocol server (dow-mcp)

Use

dow init           # scaffold specs/summarization.yaml + evals.py
# edit specs/summarization.yaml (your prompt, model, sampling, metrics)
dow commit         # captures v1
# edit specs/summarization.yaml again (e.g. change temperature)
dow commit         # captures v2 (custom metrics run automatically)
dow compare        # v1 vs v2: output diff + drift + stability + verdict (defaults to last two)
dow explain        # why behavior changed: attributes it to a config field
dow tag good v1    # label a version (good, golden, baseline, bad, ...)
dow eval           # run your custom metrics; compare vs previous and last-good
dow history        # list captured versions, stability, and tags
dow inspect v1     # one version's spec, runtime capture, outputs, tags, eval
dow tree           # visualize how behavior evolves across versions
dow tree -o evolution.md   # export a Mermaid diagram; open the Markdown preview

Versions are named automatically (v1, v2, ...); refer to them by name, the shortcuts last and prev, or any label you applied with dow tag. They form a tree - dow commit --from v1 branches from an earlier version. Runs fully offline by default (mock provider + built-in hashing embedder); no API key required.

Backends

The model sits behind one provider interface, selected by model.provider in the spec. dow runs fully offline by default and never requires a backend.

provider	Backend	Notes
mock	Deterministic offline mock (default)	No network or keys; ideal for demos and golden tests
python	A local Python callable (path.py:function)	Version your own generator, fully offline
openai	OpenAI hosted models	pip install "dow[openai]"; set OPENAI_API_KEY
ollama	Local Ollama runtime	Talks to http://localhost:11434
vllm	vLLM OpenAI-compatible server, local or remote	HTTP only - no extra dependency

vLLM (local or remote)

provider: vllm talks to a vLLM server (https://docs.vllm.ai) over its OpenAI-compatible HTTP API, so dow itself needs no GPU or vLLM library - just a reachable server. One provider covers both deployments; only the endpoint changes.

Point dow at a remote server with an environment variable:

export VLLM_BASE_URL="https://vllm.internal.example.com/v1"
export VLLM_API_KEY="..."     # only if the server was started with --api-key
dow commit

VLLM_BASE_URL defaults to http://localhost:8000/v1. The spec's model.version (falling back to model.name) is sent as the request's model and must match the server's --served-model-name.

Custom metrics

Plug in your own evaluators - plain functions that receive an EvalContext and return a score or named scores - and reference them from the spec:

evaluation:
  metrics:
    - evals.py:avg_word_count      # local file : function
    - my_pkg.metrics:accuracy      # importable module : function

# evals.py
def avg_word_count(ctx):
    return sum(len(o.split()) for o in ctx.outputs) / max(1, len(ctx.outputs))

dow eval runs them, saves the scores with the version, and compares against the previous version and the last one you tagged (dow tag good). Evaluation is automatic on dow commit and reused thereafter unless you pass --rerun.

Paired comparators

Some metrics compare one version against another, item by item - agreement and reliability coefficients (weighted kappa, Krippendorff's alpha, Gwet AC2, ICC, flip rate) and their confidence intervals. dow ships none of them: you plug in your own paired callables under evaluation.comparators. A comparator receives a CompareContext with both versions (a = baseline, b = variant), each exposing its per-item payload, and may return a number or a {estimate, ci_low, ci_high} band:

evaluation:
  comparators:
    - metrics.py:weighted_kappa    # local file : function

# metrics.py
def flip_rate(cctx):
    a, b = cctx.a.payload["labels"], cctx.b.payload["labels"]
    return sum(x != y for x, y in zip(a, b)) / len(a)

Comparators run on dow compare and dow explain, so the same attribution that pins a change to a single field also reports how far the coefficient moved. The payload a comparator reads is any structured per-item data a python provider returns alongside its text output; dow keeps it out of git (content-addressed under .dow/artifacts/) and rehydrates it on read.

Cohort aggregators (N-way)

Some checks compare a whole set of versions at once - the agreement of a label across K seeds, K judges, or K prompt wordings (ICC, Fleiss/Gwet AC2, Krippendorff's alpha over K raters, with bootstrap CIs). Comparators see two versions; aggregators see the whole cohort. Plug your own in under evaluation.aggregators; dow ships none of the coefficients:

evaluation:
  aggregators:
    - metrics.py:seed_reliability   # ICC / AC2 / alpha over K seeds

An aggregator receives a CohortContext whose members is one context per version (each with its payload), aligns them by your own key, and returns the same shapes as a comparator. dow aggregate selects the cohort (an explicit version list, every version carrying a --tag, or all of them), runs the aggregators, and saves a durable, git-tracked bundle under .dow/aggregations/; dow aggregate --list and --show <id> retrieve past results.

Pluggable plots

dow can render results to figures without shipping a plotting library: reference your own plot functions under evaluation.plots. Each receives a PlotContext (the analysis results plus an out_dir to write into) and returns the figure path(s):

evaluation:
  plots:
    - plots.py:forest_plot          # your matplotlib (or any) code; dow ships none

Run dow compare --plot or dow aggregate --plot. dow copies each figure into the content-addressed artifact store (.dow/artifacts/, git-ignored) and records its hash and size; for an aggregation the figure is referenced from the persisted bundle, so it stays regenerable while the bytes stay out of git.

Non-text outputs

dow's built-in signals - semantic drift, stability, output difference - assume the captured output is text. When a version's behavior is not free text (an aligned vector of ordinal labels, a cluster assignment, a numeric score), set embedding_model: none:

evaluation:
  embedding_model: none    # outputs aren't text; skip dow's built-in lexical drift

dow then tracks the specification change (prompt, model, sampling, params) and runs your own metrics, comparators, and aggregators, without inventing a meaningless lexical number; compare, history, tree, and inspect simply omit the built-in drift. The structured per-item data rides in the payload a python provider returns, and dow persists it whatever its in-memory type - numpy arrays, sets, dataclasses, and numpy scalars all degrade to a faithful JSON-native form. Your project never has to pre-convert its data to satisfy dow.

MCP server

Prefer to drive dow from an AI agent? dow-mcp exposes the core workbench over the Model Context Protocol (https://modelcontextprotocol.io) over stdio, so an MCP client can scaffold specs, capture versions, and compare/explain drift on your behalf. It runs on the same engine as the CLI, so the two surfaces never drift apart, and works fully offline by default (mock provider + built-in embedder).

pip install "dow[mcp]"    # install the server
dow-mcp                    # serve over stdio (usually launched by your MCP client)

Point an MCP client at the dow-mcp command and set the project directory it should operate on:

{
  "mcpServers": {
    "dow": {
      "command": "dow-mcp",
      "env": { "DOW_PROJECT_DIR": "/path/to/your/project" }
    }
  }
}

The 14 tools mirror the CLI: dow_list_specs, dow_init, dow_read_spec, dow_write_spec, dow_commit, dow_compare, dow_explain, dow_eval, dow_aggregate, dow_history, dow_inspect, dow_tag, dow_tree, and dow_docs. They return structured JSON (config diffs, metrics, comparator and aggregator results, the version tree, and Mermaid - plus, for text outputs, drift scores and verdicts), so a client can run the full edit, commit, compare loop. The server also exposes read-only resources an MCP client can attach as context: dow://overview, dow://docs/<command>, dow://specs, and dow://spec/<name>.

Documentation and the manual page

Each command's help lives in a single editable text file that feeds both dow help <command> (in the terminal) and the Unix man page, so they never drift apart. dow ships that man page: after a normal pip install run man dow, or run dow man to print it (roff) to stdout and pipe it anywhere.

The full design (PROJECT_PLAN.md), the changelog, the runnable demo, and the architecture notes live in the source repository: https://github.com/theflyingrahul/dow

License

MIT. The full license text ships in the package (LICENSE) and is available in the repository: https://github.com/theflyingrahul/dow