TRACER integration notes

TRACER (Trace-Based Adaptive Cost-Efficient Routing) turns fixed-label teacher decisions from traces into task-specific ML students. In this hybrid architecture, students learn easy, repetitive decisions; calibrated deferral retains the teacher for requests the students decline. The teacher can be an LLM, a foundation model for System 1 decisions, or another classifier. It supplies the reference labels in the traces. A fitted student predicts labels directly from embeddings; an acceptance policy and optional distance guard control which predictions to return. The caller handles deferred requests, optionally through its teacher. Student fitting and inference can run on CPU; fit() trains the classifier and policy without fine-tuning the teacher or semantic encoder. Read the overview, API reference and change log for the current scope.

Source and release status

These instructions describe this repository checkout. Final-policy certification, staged artifact publication and strict OOD-loading corrections are unreleased source changes. Do not imply they are already present in the PyPI package merely because pip install tracer-llm succeeds. To use the corrected source, install from a checkout containing the changes:

python -m pip install -e .

Python 3.12+ is required. Core dependencies are NumPy, scikit-learn and joblib. The library is MIT licensed: local fitting, saved artifacts and self-hosted prediction do not require a Tracer account or license fee. Embeddings, teacher calls and hosting still have their own resource costs. This checkout supplies a Python API and a small HTTP server; the JavaScript package records traces. Do not invent a command-line interface, a general chat model, an OpenRouter listing or parity with a separately deployed hosted service.

Identify the decision contract first

Establish the label vocabulary, input representation, teacher and quality target from the user's existing task. Preserve their selected quality requirement. Training examples have this shape:

{"input": "What is my balance?", "teacher": "check_balance"}
{"input": "Send $50 to Alice", "teacher": "transfer_money"}

Optional fields are id, ground_truth and metadata. Example rows illustrate the format; a usable policy needs representative training and held-out evidence. The student learns the label set from the development partition. Unseen labels are not zero-shot supported classes. Multi-turn context can be encoded into input by the application, but this library does not reconstruct agent state.

Use an existing authorized dataset and encoder when available. Resolve missing contract information before issuing paid teacher calls or sending private data to an external embedding endpoint. Existing user authorization takes precedence over this guide; do not invent another approval step for already authorized work.

Fit, inspect, load

import numpy as np
import tracer

X = np.load("traces.npy")
result = tracer.fit(
    "traces.jsonl",
    embeddings=X,
    config=tracer.FitConfig(target_teacher_agreement=0.95),
)
print(result.manifest.certification)

if result.manifest.selected_method is None:
    raise RuntimeError("No deployable policy; inspect the certificate and data.")

router = tracer.load_router(".tracer")
out = router.predict(np.load("new_request.npy"))

The matrix must contain one finite embedding per trace, in the same row order. Use the same encoder weights, dimension, normalization and preprocessing during fitting and prediction. Matching dimensions alone does not establish this.

The fitting search includes linear classifiers, compact MLPs and tree models; classical ML can make the final accepted decision. Fitting does not fine-tune an embedding model. Do not describe the library as training only a deferral gate.

Some candidate implementations use multiple CPU cores. Respect workstation and cloud resource limits; do not run a full fit merely to validate a documentation change. skip_candidates can bound the candidate search, but it must not be used to silently lower the user's quality target or bypass the final check.

Runtime results and teacher fallback

embedder = tracer.Embedder.from_callable(my_embedding_function)
router = tracer.load_router(".tracer", embedder=embedder)
text = "What is my balance?"
out = router.predict(text, fallback=lambda: call_my_teacher(text))

The callable embedder accepts a list of strings and returns a matrix. A fallback accepts no arguments and is invoked only after deferral. The return contract is:

An OOD distance rejection is a plain deferred result. There is no public third decision state or reason field. accept_score is not a calibrated correctness probability. A fallback response still has decision="deferred".

predict_batch() returns labels, decisions, the handled mask, internal prediction indices and stage IDs. It does not call a teacher for deferred rows. The caller must process those rows explicitly.

Certification and failure handling

The default reserves 20% of rows before fitting, label balancing or candidate selection. Development data selects the student, acceptance thresholds and policy. The final serving policy, including its OOD guard, is frozen before one check on the reserve. Do not reuse this reserve to rescue a failed candidate.

The one-sided Clopper–Pearson lower bound on accepted teacher agreement must meet target_teacher_agreement. The default certification_alpha=0.10 gives a 90% one-sided confidence level under independent, representative sampling. Accepted coverage must also meet min_deploy_coverage. No accepted examples means no certificate, not perfect agreement.

This measures agreement with the teacher, not ground-truth accuracy, individual correctness or a per-class guarantee. The reserve is a random row split. Duplicates, correlated sessions, distribution shift and repeated tuning against held-out results weaken or invalidate that interpretation. Maintain independent session/time evaluations and report accepted coverage alongside quality.

If selected_method is null, load_router() refuses to load a serving policy. A completed fit() or update() that fails certification may intentionally publish this null-policy generation. A fitting/publication exception instead preserves the prior generation through staged rollback. Do not describe every failed check as preserving the old router, or promise zero-downtime concurrent readers. Use one writer and reload readers only after inspecting the new manifest.

Expected OOD metadata and reference files are part of the policy. Missing or corrupt required files fail loading; do not remove them to make loading succeed. Older artifacts without final-certification metadata are not retroactively certified by a newer loader.

Embeddings, updates and deployment

Local sentence-transformers support is optional:

python -m pip install -e '.[embeddings]'

Embedder.from_sentence_transformers(model) may download a model; Embedder.from_endpoint(url, ...) calls the configured external HTTP endpoint. Embedder.from_callable(fn) supports an existing encoder. The generic endpoint adapter expects configured JSON keys; do not claim it implements every provider's API schema automatically. Precomputed embeddings need no embedding API call.

tracer.update("new_traces.jsonl", new_embeddings=X_new)
tracer.generate_html_report(".tracer")

Keep the complete .tracer/ directory, including stored traces and embedding index needed by updates. New data must use the same representation. Collect a representative sample of all traffic when estimating total coverage, rather than only collecting the old router's deferrals. Coverage can rise or fall.

For a local HTTP sidecar:

tracer.serve(".tracer", host="127.0.0.1", port=8000)

The server accepts POST /predict with an embedding, POST /predict_batch with an embedding matrix and GET /health. Encoding and teacher calls are the caller's responsibility. Authentication and TLS belong in the integrating application.

watch() records model calls locally by default, with optional export to an explicitly configured backend. scan() reports cluster diagnostics; it does not train or certify a serving policy. See the separate watch and scan guides.

Paper and claims

Preserve the research title and citation: TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification, Adam Rida, arXiv:2604.14531.

The paper's historical benchmark results are not measurements of this revised certification implementation. Do not invent achieved coverage, latency, cost savings or superiority over another model. Report measured outcomes and their data/split assumptions.