daf-jev

Modular, composable Python client and decision toolkit for the TypeSafe Jev (System One) API. One HTTP endpoint, three question primitives, and a set of pure-logic composition patterns built on top of the answers — plus a concurrent batch evaluation harness, an MCP server, a figure registry, and a reproducible manuscript pipeline.

What it provides

• Primitives — noul (yes/no), choice (pick an option from a probability distribution), score (rated on ordered levels). Build questions with noul() / choice() / score() and group them in a QuestionSet; batch any number of questions into a single API call.

• Client — JevClient / AsyncJevClient wrapping POST https://api.typesafe.ai/v1/systemone, with retries (429/529, exponential backoff, Retry-After), typed error mapping, and a models() listing. Retry policy, default timeout, and default model resolve from the environment (see Configuration); every ask call also accepts a per-call timeout override and extra request_headers (merged over the defaults for that call only).

• Composition patterns — pure functions over answers: composite_score (probability-weighted expected value over score levels), confidence_gate (auto-escalate low-confidence answers), route / pick (intent routing by choice).

• Evaluation — Evaluator runs a fixed question set over many states concurrently (thread pool for JevClient, asyncio semaphore for AsyncJevClient) without aborting the batch: per-state failures are captured in EvaluationRecord.error. summary() aggregates per-question means/p95s; to_json() serializes records.

• Usage accounting — UsageLedger (thread-safe) accumulates request counts and token totals across any loop of ask calls; it accepts Usage objects, full responses, or None for error paths, and snapshot().to_dict() is JSON-safe.

• Resilience — an opt-in, composable CircuitBreaker: after failure_threshold consecutive failures it fails fast for cooldown_seconds, then admits a single recovery probe. It wraps any callable, never sleeps, and takes an injectable clock; it complements the per-request retry policy.

• Calibration — pure reliability statistics in daf_jev.calibration (bucket_index, reliability_table, expected_calibration_error, brier_score) over (confidence, correct) pairs, plus a live calibration benchmark (benchmarks/bench_calibration.py).

• Figures & manuscript — a matplotlib figure registry (7 figures + figure_registry.json) and a {{TOKEN}} variable pipeline that keep the 10-section manuscript in manuscript/ free of hardcoded results.

• MCP server — daf-jev serve exposes the toolkit as seven MCP tools (jev_ask, jev_evaluate, jev_models, jev_composite_score, jev_confidence_gate, jev_tiered_gate, jev_docs_verify) plus a jev://docs/snapshot resource, over stdio (see MCP server).

Dependencies: Python >= 3.10, httpx, pyyaml (plus matplotlib for figures). Managed with uv.

Related MCP server: UVG Local MCP Server

How a decision flows

flowchart LR
    S["state<br/>(text / JSON)"] --> Q["typed questions<br/>noul · choice · score"]
    Q --> C["JevClient / AsyncJevClient<br/>POST /v1/systemone"]
    C --> A["typed answers<br/>probability · distribution · rubric"]
    A --> P["compose layer<br/>composite_score · confidence_gate · route/pick"]
    P --> G{"tiered_gate"}
    G -- "≥ high" --> AUTO["automate"]
    G -- "between" --> REV["human review"]
    G -- "< low" --> ESC["escalate"]
    A --> EV["Evaluator<br/>batch over many states"]
    EV --> CAL["calibration<br/>ECE · Brier · reliability"]

The batched call carries all questions at once — live benchmarks show it running up to ~18× faster than sequential single-question calls while the sequential strategy consumes ~4× more tokens (see Tests and benchmarks).

Quickstart

uv sync
export JEV_API_KEY="sk-..."   # or TYPESAFE_API_KEY, or put it in .env

.env at the project root is auto-loaded (there is a .env.example to copy from; .env itself is gitignored).

from daf_jev import JevClient, choice, noul, score

with JevClient() as client:  # api_key resolved from env / .env
    resp = client.ask(
        "Customer message: I was charged twice this month and nobody has responded.",
        {
            "billing": noul("Is this about a billing problem?"),
            "tone": choice("What is the tone?", {"calm": None, "frustrated": "annoyed but civil"}),
            "severity": score("How severe?", ["minor", "noticeable", "blocking"]),
        },
    )

print(resp.nouls["billing"].noul)          # 0.0 (no) .. 1.0 (yes)
print(resp.choices["tone"].choice, resp.choices["tone"].confidence)
print(resp.scores["severity"].score)       # probability-weighted, may be fractional

Composing decisions:

from daf_jev import confidence_gate, composite_score

value = composite_score(resp.scores["severity"])   # expected value over level indices
verdict = confidence_gate(resp.choices["tone"], threshold=0.6, below="review")

Examples

Five runnable scripts live in examples/ (walkthrough per script in examples/README.md). Each resolves the API key from the environment or .env and — when no key is found — prints SKIP: JEV_API_KEY not set and exits 0, so all five are offline-safe:

python examples/quickstart.py         # one mixed ask call; answers, usage, request id
python examples/triage_router.py      # tiered_gate + route over one choice answer
python examples/composite_scoring.py  # composite_score + confidence_gate
python examples/evaluate_corpus.py    # Evaluator over an inline four-state corpus
python examples/gated_fallback.py     # heuristic-first: model called only when it adds value

All five take --model NAME (default: JEV_MODEL, then TYPESAFE_DEFAULT_MODEL, then jev-latest); evaluate_corpus.py also takes --concurrency N (default 2).

Evaluating a corpus

Run a fixed question set over many states, concurrently, with per-state error capture (evaluate never aborts the batch on one bad state).

CLI — --questions-file is a YAML mapping of id to a spec string or a native question mapping; --states-file is one state per line (blank lines skipped) or a JSON array of strings:

uv run daf-jev evaluate \
  --questions-file questions.yaml \
  --states-file states.txt \
  --concurrency 8 \
  --include-records

Python — pass a sync or async client; bare str states get state_0000-style ids. An AsyncJevClient is single-use through evaluate(): the Evaluator closes the async session when the batch completes (its keep-alive connections are bound to the private event loop).

from daf_jev import Evaluator, JevClient, QuestionSet, noul, score

questions = QuestionSet().add(
    "billing", noul("Is this about a billing problem?")
).add(
    "severity", score("How severe?", ["minor", "noticeable", "blocking"]),
)

with JevClient() as client:
    evaluator = Evaluator(client, questions, concurrency=8)
    records = evaluator.evaluate(["state one text", "state two text", ...])

summary = evaluator.summary(records)      # per-question means/p95s + usage
print(evaluator.to_json(records))         # per-state records incl. errors/latency

Usage accounting and resilience

Long-running consumers need receipts and failure isolation beyond the per-request retry policy. Both are small, opt-in, client-side helpers:

from daf_jev import UsageLedger

ledger = UsageLedger()
for state in states:
    try:
        response = client.ask(state, questions)
    except TypeSafeError:
        response = None               # error paths produce no usage
    ledger.record(response)
print(ledger.snapshot().to_dict())    # {"requests": .., "input_tokens": .., ...}

UsageLedger accumulates request counts and token totals across any loop of ask calls; Evaluator.summary() remains the aggregator for batch evaluation runs. reset() returns the pre-reset totals and zeroes the ledger.

from daf_jev import CircuitBreaker, CircuitOpenError

breaker = CircuitBreaker(failure_threshold=5, cooldown_seconds=30.0)
try:
    response = breaker.call(client.ask, state, questions)
except CircuitOpenError as exc:
    ...                               # fail fast while the circuit is open

CircuitBreaker wraps any callable: failure_threshold consecutive failures open the circuit for cooldown_seconds, after which a single probe is admitted. It never sleeps — wait out the cooldown in your own loop (exc.remaining_seconds reports what is left) — and composes with the per-request retry policy.

CLI

uv run daf-jev ask \
  --state "I was charged twice this month." \
  --question billing=noul:Is this about a billing problem? \
  --question tone=choice:What is the tone?:calm=,angry=hostile \
  --question severity=score:How severe?:minor,noticeable,blocking \
  --pretty

uv run daf-jev models                       # list available models
uv run daf-jev models --pick latest         # pick one (latest|first|last)
uv run daf-jev models --pick latest --contains jev

uv run daf-jev evaluate \
  --questions-file questions.yaml --states-file states.txt \
  --concurrency 8 --include-records

uv run daf-jev docs-verify     # re-hash docs/reference/ against MANIFEST.json

All commands print JSON to stdout; exit 0 on success, 2 on usage error, 1 on runtime error.

MCP server

daf-jev serve runs the toolkit as a Model Context Protocol (MCP) server over stdio — the default and only supported transport. The server needs the official mcp SDK, shipped in the optional mcp dependency group. Every surface below calls the same core; there is no second implementation:

flowchart TB
    subgraph CORE["daf-jev core"]
        CLI["CLI<br/>ask · evaluate · models · docs-verify"]
        MCP["MCP server (stdio)<br/>jev_ask · jev_evaluate · jev_models<br/>jev_composite_score · jev_confidence_gate<br/>jev_tiered_gate · jev_docs_verify"]
        SKILL["agent skill<br/>skills/daf-jev/SKILL.md"]
        EX["examples/<br/>4 runnable scripts"]
    end
    CLI --> K["JevClient / compose / calibration"]
    MCP --> K
    SKILL --> K
    EX --> K
    K --> API["TypeSafe Jev API<br/>POST /v1/systemone"]

uv sync --extra mcp
uv run daf-jev serve          # stdio; --transport stdio is the only choice

Tools (each returns JSON-safe values; keys resolve per call from env or .env, and a missing API key surfaces as a tool error):

Point any MCP client at the server with a stdio config, e.g.:

{
  "mcpServers": {
    "daf-jev": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/daf-jev", "daf-jev", "serve"],
      "env": { "JEV_API_KEY": "sk-..." }
    }
  }
}

env may be omitted when a .env file is present in the server's working directory; credentials are never returned in tool output.

Configuration

Everything resolves from the environment (injected env mapping > process env

.env file); unset or invalid values fall back to the defaults below.

Per-field: a bad value keeps only that field's default. Per-call timeout= and request_headers= on ask() win over all of the above for that call.

Figures and manuscript

The repo renders its own paper: 9 manuscript sections under manuscript/, with every measured number injected as a {{TOKEN}} placeholder — nothing is hardcoded in the prose.

uv sync --extra figures
uv run python scripts/generate_figures.py    # 7 figures + figure_registry.json -> output/figures/
uv run python scripts/generate_figures.py --only batching   # single figure by name

Figures architecture, primitives, and confidence are drawn from code; batching, latency, calibration, and graphical_abstract read the newest output/benchmarks/*.json.

uv run python scripts/z_generate_manuscript_variables.py
# 46 tokens -> output/data/manuscript_variables.json, then {{TOKEN}}
# substitution into output/manuscript/ (inside the template checkout)

Rendering and validation run from the template checkout, which resolves the project through a leaf symlink template/projects/ongoing/daf-jev -> ../../../projects/ongoing/Code_Tools/daf-jev (created 2026-09-16; intermediate symlinks are rejected by design):

cd /Volumes/external_drive/Git/template
uv run python scripts/pipeline/stage_03_render.py --project ongoing/daf-jev
uv run python scripts/pipeline/stage_04_validate.py --project ongoing/daf-jev

Stage 04 runs 9 validation checks (including the figure registry and rendered provenance); re-run render + validate after any manuscript or figure change. The rendered PDF lands at output/pdf/daf-jev_combined.pdf.

Tests and benchmarks

uv sync --extra dev --extra bench
uv run pytest tests/unit --cov=src          # 294 unit tests; coverage gate >= 90%
JEV_API_KEY=... uv run pytest tests/live    # 2 live tests against the real API

Unit tests need no key: they run against a real local HTTP stub server (tests/conftest.py). Live tests and benchmarks hit the real API and are skipped with a SKIP: message when JEV_API_KEY is absent.

uv run python benchmarks/bench_batching.py --runs 3   # 1 call with N questions vs N calls
uv run python benchmarks/bench_patterns.py --runs 10  # composite-score / routing latency

Latest recorded results (2026-09-16, output/benchmarks/): batching is 3.5x–20.5x faster (N=5→20) and 2.8x–4.2x cheaper in tokens; decision-pattern pipelines run at ~0.12 s p50.

Calibration benchmark

benchmarks/bench_calibration.py measures how well the live model's reported confidence tracks its behavior, plus noul answer stability:

JEV_API_KEY=... uv run python benchmarks/bench_calibration.py
# flags: --states N (default 6), --repeats N (default 5), --model NAME

It repeats one three-option classification question per state and treats agreement with the modal (majority) choice across repeats as a self-consistency correctness proxy — not ground-truth accuracy — so the resulting error figures quantify confidence-vs-self-consistency, not confidence-vs-correctness. The (confidence, correct) pairs feed the pure daf_jev.calibration functions; a noul question repeated the same way yields a mean pairwise |Δnoul| stability metric. Results land in output/benchmarks/calibration_<YYYYMMDD>.json (latest recorded: 2026-09-16, jev-latest, 6 states x 5 repeats — ECE 0.0730, Brier 0.0252, mean pairwise noul gap 0.0050). Without an API key (env or project .env) it prints SKIP: JEV_API_KEY not set and exits 0; a failing call drops that state's repeats into n_errors instead of aborting the batch.

v0.3.0 is published on Zenodo (deposit 22816188, released 2026-09-17) and mirrored to the public repository at github.com/docxology/daf-jev.

• Concept DOI (all versions, stable): 10.5281/zenodo.22816187

• v0.3.0 version record: https://zenodo.org/records/22816188 (version DOI 10.5281/zenodo.22816188)

• Public repository: https://github.com/docxology/daf-jev

• Rendered manuscript PDF: daf-jev_combined.pdf at the repo root (regenerated to output/pdf/daf-jev_combined.pdf by the template render pipeline; the root copy is refreshed at each release).

• Machine-readable release metadata: CITATION.cff and .zenodo.json at the repo root.

To cite daf-jev, use the metadata in CITATION.cff (cffconvert and Zenodo both render it), or paste this BibTeX:

@software{friedman2026dafjev,
  title   = {daf-jev: A Composable Python Decision Toolkit for the TypeSafe Jev (System One) API},
  author  = {Friedman, Daniel Ari},
  year    = {2026},
  doi     = {10.5281/zenodo.22816187},
  url     = {https://github.com/docxology/daf-jev},
  version = {0.3.0}
}

New releases are added as new version deposits on the same Zenodo concept, so the concept DOI always resolves to the latest published version.

Documentation

• docs/ARCHITECTURE.md — the authoritative design contract (wire facts, module signatures, test and benchmark conventions).

• docs/models.md — sourced technical reference on System One models and Jev, with primary vs third-party claims flagged.

• docs/ — index, including the 108-page hashed snapshot of docs.typesafe.ai in docs/reference/.

• skills/daf-jev/SKILL.md — the agent skill for this toolkit (when-to-use, API surface, CLI, MCP server, pitfalls). To use it with an agent outside the repo, copy the whole skills/daf-jev/ directory into the agent's skills location — see skills/README.md.