probe-research 0.9.0

CLI + SDK + read-only MCP client for Probe Research (experiment tracking).

probe-research (probe SDK/CLI + probe-research plugin)

CLI + SDK client for Probe Research, Probe's experiment-tracking platform. It is a thin client over the v3 ingestion contract (CONTRACT.md in the Probe Research backend). Implemented experiment calls map onto real endpoints. Target asset-registry methods are present as an explicit client contract but fail closed with a capability error until the backend routes exist.

Two client surfaces

Probe Research exposes experiment tracking through two separate surfaces over the same backend, for two different workflows:

• probe — SDK + CLI (non-agent). A Python library (import probe) and the probe command-line tool for integrating with existing setups and manual experimentation. Drop it into a training script or pipeline to record runs, metrics, spans, and artifacts. No agent required.
• probe-research — plugin: skills + MCP (agent-centric). Installed into a coding agent (e.g. Claude Code). Its skills teach the agent the experiment workflow, its read-only MCP server lets the agent query experiment state, and writes flow through the probe CLI. This is the surface for agent-driven research loops such as Anthrogen.

Same backend, two entry points: humans-in-code reach for the SDK/CLI; agents-in-the-loop use the plugin.

Package boundaries

src/probe/
├── sdk/       # typed client, uploads, local capture, session adapter ABI
├── cli/       # `probe`: thin shell over the SDK
└── mcp/       # `probe-research-mcp`: strictly read-only tools and resources
skills/
├── track-experiment/
├── manage-research-asset/
└── publish-experiment/

The SDK is the implementation. The CLI, MCP source adapter, future hooks, Python experiments, and passive platform integrations all use it. CLI and SDK therefore have capability parity; they differ only in ergonomics.

Surface	SDK	CLI	Intended caller
Experiment upload	Client.run, Run.log/span/log_artifact/snapshot/link/execute, Client.events, Client.promote	run, log, span, artifact, snapshot, link, exec, event, promote	Researchers, agents, notebooks, training/platform code
Session adapter	Client.sessions.attach/checkpoint/detach	probe hook session ...	Future deterministic hooks/broker only
Asset read/selection	Client.assets.resolve, normally behind MCP	No normal read verb	Agent through read-only MCP
Asset effects	Client.assets.materialize/fork/propose/promote	probe asset ...	Agent/researcher after selecting an exact asset ref
Passive ingestion	Client.ingest	No convenience command yet	Install-once platform integration
Read plane	SDK reads used by probe.mcp	get/bundle diagnostics	MCP for agents; CLI for humans/scripts

Session commands do not upload metrics or experiment outputs. They correlate a coding-agent session with a run and checkpoint redacted transcript metadata. Conversely, event add is normal experiment knowledge upload even when a hook eventually calls it. No hooks are installed in this release.

Install

pip install -e ".[dev]"     # from this directory

Auth

probe login       # browser device flow (RFC 8628 + PKCE) — the default; nothing to paste

Air-gap paste path: probe login --token probe_pat_xxxxxxxx (verified via GET /v1/me); probe login --endpoint-only --base-url … saves the endpoint without minting a token. Both write ~/.config/probe/config.json. Or set env: PROBE_BASE_URL, PROBE_TOKEN (user token, /v1), PROBE_INGEST_TOKEN (ingest token, /ingest), PROBE_HMAC_SECRET (optional body-signature secret). SDK-created runs heartbeat every 60s so the server can reap crashed ones; PROBE_HEARTBEAT_SECONDS tunes the interval (<=0 disables).

You can also skip probe login entirely: the first client.run() / probe run start with no token triggers the same browser approval inline (TTY only) and persists the result. Disable with PROBE_AUTO_LOGIN=0; headless/CI keeps the crisp AuthError and should set PROBE_TOKEN.

The MCP server prefers PROBE_MCP_TOKEN, which should be a separately minted read-only token. It falls back to PROBE_TOKEN for local development, but exposes no mutation tools.

On rented compute (RunPod) with no standing config, the /track-experiment skill seeds PROBE_TOKEN at session start.

SDK (agent-driven / interactive)

import probe

client = probe.Client()  # resolves creds from env / `probe login`

run = client.run(experiment="dockq-sweep", hypothesis="temp 0.7 wins", name="run-1",
                 project="folding", source="runpod", external_id="rp-9931")
# …or with zero identity args: `client.run()` defaults experiment to the git repo /
# script name, name to a timestamp (the server adds a petname short_id), and a NEW
# experiment gets a marked "[auto] …" hypothesis composed from context. Set the real
# one later: client.update_experiment(id, hypothesis="…")  /  probe experiment set.

run.snapshot()                                   # non-disruptive git + deps + GPU capture
run.link(wandb_run_id="abc123", s3_prefix="s3://x/y")

for step in range(100):
    run.log({"loss": ..., "dockq": ...}, step=step)     # POST /v1/runs/{id}/metrics

sid = run.span("rollout", name="rollout-0", step_index=1)   # trajectory span
run.log_artifact("final.sif", uri="r2://bucket/final.sif", kind="artifact")
run.finish()                                     # flushes spool, sets status+ended_at

Structured knowledge and local process capture use the same SDK:

run.execute(["python", "train.py", "--config", "dockq.yaml"])
client.events.add(run.id, "decision", "Use DockQ scorer v3", evidence_refs=["tool:91"])
report = client.check_run(run.id)

Data writes are fail-open by default: on failure they spool to disk (~/.local/state/probe/spool) and return, never blocking the training loop. run.finish() (or probe flush) replays the spool. Appends and queue rewrites are fsync'd and atomic. On rented compute, put the queue on durable storage with PROBE_SPOOL_DIR=/shared/probe/spool or probe --spool-dir /shared/probe/spool …. Pass strict=True to make a write raise.

SDK (install-once / passive push)

client.ingest(
    experiment_slug="dockq", experiment_hypothesis="...",
    run={"name": "r1", "source": "temporal", "external_id": "wf-1", "status": "running"},
    metrics=[{"kind": "model", "key": "loss", "value": 0.5, "step_index": 1}],
    batch_id="deadbeef",          # idempotent redelivery
)

One idempotent push (bearer ingest token + optional HMAC), keyed on (customer_id, source, external_id).

CLI (probe)

RUN=$(probe run start --experiment dockq --hypothesis "temp 0.7 wins" --name run-1 \
        --project folding --source runpod --external-id rp-9931)
probe snapshot $RUN
probe link $RUN --set wandb_run_id=abc --set gpu_job=rp-9931
probe log $RUN loss=0.42 dockq=0.71 --step 42
probe span add $RUN --type rollout --name rollout-0 --step 1
probe artifact add $RUN ./final.sif --kind artifact
probe event add $RUN --kind decision --statement "Use DockQ scorer v3" --evidence tool:91
probe exec $RUN -- python train.py --config dockq.yaml
probe run check $RUN
probe run end $RUN --status completed
probe bundle $RUN            # read: run + series + artifacts

Harbor trial capture (probe trial)

Capture a Harbor trial directory into a run, keyed to the training step — the sandbox↔step join (see docs/2026-07-15-harbor-native-ownership-plan.md for status: what's shipped vs parked):

# rollout span + reward metric + labeled CAS file uploads + kind=harbor_trial
# manifest; a recognized trajectory format (ATIF v1.x built in) also expands
# into turn/tool_call spans under the rollout span
probe trial add $RUN jobs/my-job/trials/swe-fix__x1 --step 600 --env-type skypilot-fork
probe trial add $RUN <dir> --step 601 --no-expand      # raw-only capture
# Copy/checksum a host trial tree onto a durable volume without touching the network.
probe trial stage <host-trial-dir> --to /shared/probe/trial-601 \
  --expect result.json --expect lock.json
# Retry one or every Miles bridge request. The descriptor supplies run/step/correlation.
probe trial export /shared/probe/trial-601/export-request.json
probe trial drain /shared/probe/captures
probe trial watch /shared/probe/captures --interval 5
# Bind descriptors produced during an offline run initialization:
probe trial drain /shared/probe/captures --run "$PROBE_RUN_ID"
# retroactively expand a stored trajectory (e.g. after a fork's parser ships);
# deterministic span ids make this idempotent — re-runs upsert, never duplicate
probe trial expand $RUN <manifest-artifact-id> --max-spans 0

Query it back: client.list_run_artifacts(run_id, kind="harbor_trial", step_from=599, step_to=601). Fork parsers plug in via probe.connectors.atif.register_trajectory_parser("their-format", fn); unknown formats are captured raw (never rejected) and expanded later.

probe trial stage and the probe-harbor-export/1 consumer keep an atomic .probe-capture.json beside the durable trial bytes. Its collection status is separate from remote-upload status, so an exporter outage leaves a precise, retryable list of unconfirmed files instead of losing their paths. Stable external keys and span IDs make retries update the same rollout; arbitrary Miles/Osmosis correlation fields are preserved under the harbor_trial manifest's source.context.

The completeness claim is intentionally bounded: it covers declared regular files in the host Harbor trial directory. Public Harbor tears down the sandbox before Trial.run() returns, so a post-run SDK consumer cannot know about undeclared state Harbor never materialized. The ledger reports that state as unknown, inventories explicitly declared missing files, and treats hidden files/symlinks as visible skips. A true pre-teardown guarantee requires the producer or environment implementation to invoke durable collection from its lifecycle hook.

The following commands are reserved for future hook configuration and are not part of the normal researcher workflow:

probe hook session attach RUN --session-id SESSION --transcript-path PATH --cwd DIR
probe hook session checkpoint RUN --session-id SESSION --transcript-path PATH --reason pre_compact
probe hook session detach RUN --session-id SESSION --reason session_end

They currently encode session links in run.metadata.agent.sessions[] and transcript checkpoints as redacted local-reference artifacts. Until managed artifact upload exists, transcript portability remains explicitly false.

Read-only MCP server

Run the stdio server with probe-research-mcp. It exposes three tools, plus five deprecated aliases that are removed next release:

Tool	Answers
browse_research	"What exists here?" — the structured project → experiment → run tree
search_knowledge	"Find things about X" — one-index exact+semantic search with per-result provenance
get_entity	"Show me this thing" — one entity through a purpose-shaped view

research_context, research_search, research_get, research_compare and research_resolve still answer, with their OLD signatures and OLD payloads. They exist because MCP tools are served by the SERVER and .mcp.json pins one url for every plugin version, so renaming a tool breaks every installed client the moment the image rolls — a plugin version bump is not a cutover mechanism. Migrate off them; they go next release.

Thin harness, fat skills. Coverage grows through get_entity's view and filters parameters, never through more tools. browse_research is the one addition that cleared that bar: it answers a question the others structurally cannot, because search ranks by relevance to a query and therefore needs you to already know what to search for.

get_entity(ref, view=..., filters=..., token_budget=..., cursor=...), where ref is run:<id>, experiment:<id>, asset:<name>, project:<id>, group:<id>, or a bare id:

Kind	Views
run	card · trajectory · metrics · artifacts · reproduce · handoff · lineage · events
experiment	card · artifacts · lineage · groups · versions
asset	card · versions
project, group	card

card (the default) returns available_views for that entity, so one call tells you what else you can ask for — the matrix above is documentation, not something to memorise.

Assets resolve by NAME, because the reuse check has a name and not an id. get_entity(ref="asset:<name>", view="versions", filters={"requirement": ">=2"}) is where research_resolve went. A name that does not exist raises not-found; a name that exists with no satisfying version returns state="no_match" with the versions that do exist, so you can see the real ceiling. Requirements match monotonic integers and labels, not semver — ">=2.0" is rejected rather than silently matching nothing.

trajectory reads a run's spans (the run bundle carries span_type counts only). metrics returns series summaries, and filters={"key": "<key>"} drills to raw points. reproduce resolves env_ref through its execution record. token_budget bounds the row-shaped part of a view and hands back a next_cursor; reproduce is atomic and reports token_budget_exceeded rather than truncating a manifest into something that reproduces nothing.

There is no trace-file tool: no backend trace index has ever existed, so it answered matches: [] to every query, which agents read as "this file has no lineage". To trace a path/URI/hash, use search_knowledge (its exact channel matches artifacts) and follow get_entity(view="lineage").

MCP reads through the Probe Research API—never directly from Postgres or R2. Its logical sources are control identity/tenant scope, the structured experiment store, the asset/manifest registry, the one-index search door (POST /v1/search: exact SQL channel + the KB engine's semantic channel; search capabilities are discovered against the live backend with one cached probe), and object-store resource pointers returned by the API. W&B, RunPod, Kubernetes, Git, and local transcript paths are not live MCP sources; adapters upload their identifiers and evidence first.

Skills

• experiment mentally boxes result-producing work and uploads concise evidence.
• manage-research-asset resolves before create, reuses exact versions, forks immutable bases, and proposes candidates without filename-based sprawl.
• publish-experiment requires explicit approval and refuses to imitate official promotion when manifest/asset capabilities are unavailable.

Asset-reuse hooks are deliberately deferred. The track-experiment skill contains the reuse-before-create rule; deterministic enforcement can be added later without changing the SDK, CLI, MCP, or skill contracts.

What maps to what (v3 endpoints)

Client call	Endpoint
client.run() / run.child()	POST /v1/experiments, POST /v1/experiments/{id}/runs
run.log() / run.log_hw()	POST /v1/runs/{id}/metrics
run.span() / run.step()	POST /v1/runs/{id}/spans | /steps
run.log_artifact()	POST /v1/runs/{id}/artifacts
run.link()	PATCH /v1/runs/{id} (merges metadata.foreign_keys)
run.finish()	PATCH /v1/runs/{id}
client.events.add()	POST /v1/runs/{id}/artifacts (kind=research_event, v3 encoding)
client.sessions.*	PATCH /v1/runs/{id} + transcript artifact metadata (hook ABI)
client.ingest()	POST /ingest/v1/runs
client.run_bundle() / run_lineage()	GET /v1/runs/{id}/bundle | /lineage
client.search() (used by research_search)	POST /v1/search (exact+semantic, sectioned)

v0.4.0.0 ingestion fold-in (Phase 1)

Most earlier gaps are closed by Probe Research v0.4 (PR #13). Now wired:

• Real metric dimensions. log_hw(..., device=3, host="n1") sends dimensions (fold #9); log(..., dimensions={...}). No more key-encoding.
• Presign artifact upload. log_artifact(path=...) runs presign → PUT to R2 → confirm (fold #16), carrying kind/meta so byte uploads are labeled like reference artifacts (Harbor-ownership Phase 0). Fails open to a reference on error.
• Execution records. snapshot() posts a content-addressed execution-record (fold #7); client.execution_record(...).
• Asset registry. client.assets.register() + add_version() + resolve() (fold #5). The aspirational fork/propose/promote-candidate surface was dropped (promotion tiers rejected upstream).
• Experiment versions. client.experiment_version() mints the immutable manifest (fold #6). This replaces the removed run-level promote.
• Lineage edges. client.add_edge() / run.edges() (fold #2).
• foreign_keys. first-class on the ingest path (run['foreign_keys'], fold #8) and surfaced on reads (run.foreign_keys, run.short_id).
• Events read. client.events.list() / for_run() (server-emitted lifecycle log). Research notes moved to client.notes.add() (stored as kind="note" artifacts).

Remaining

• MCP semantic/KB search is now wired to POST /v1/search (workspaces+kb fold-in) with an honest keyword fallback on older backends; transcript evidence is not indexed yet. Session hooks remain later work.
• Harbor-native ownership Phases 1–3 (trial capture connector, capture-at-source, platform surface): see docs/2026-07-15-harbor-native-ownership-plan.md.

(Previously listed here and since shipped: RunPatch foreign_keys/env_ref parity, asset materialize, upload kind/meta, and server-side artifact list filters ?kind=&step_from=&step_to=.)

Typed models (generated from the OpenAPI contract)

Request/response models are generated from the backend's OpenAPI schema, not hand-written, so the client cannot silently drift from the contract. The write paths (log/span/log_artifact/ingest/assets/edges/execution-records) build their payloads through the generated models, so a renamed or removed field fails client-side instead of as a server 422. /ingest/v1/runs is now declared in the schema too (Probe Research PR #12), so the passive push is generated and validated like every other path.

• schema/openapi.json - a snapshot of Probe Research's FastAPI schema.
• src/probe/_generated/models.py - generated, never hand-edited.
• src/probe/models.py - the stable import seam the SDK uses.

Refresh when the contract moves:

make regen        # dump-openapi (RESEARCH_OS=../../research-os) + gen-models
# or step by step:
RESEARCH_OS=/path/to/research-os python scripts/dump_openapi.py
python scripts/gen_models.py

RESEARCH_OS points at a local checkout of the Probe Research backend source repo (directory name research-os); it is only used to regenerate the schema snapshot.

CLI grammar note

The CLI is built on typer. Connection flags are global and go before the command: probe --token probe_pat_x log RUN loss=0.1. probe login also accepts them directly (probe login --token ...).

Tests

pytest        # 29 mocked/unit tests + a real-git snapshot test; no live server