Weaver Python SDK

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Python client for the NexWeave Weaver server. The SDK mirrors the REST API exposed by weaver-server and provides ergonomic helpers for training, sampling, telemetry, and operations management.

Installing locally

pip install nex-weaver

Configuration

Configuration can be provided via keyword arguments or environment variables:

• WEAVER_API_KEY
• WEAVER_ORGANIZATION_ID / WEAVER_PROJECT_ID for canonical IDs
• WEAVER_ORGANIZATION / WEAVER_PROJECT for UUIDs, slugs, or display names

Canonical IDs take precedence. When no organization or project is configured, the server keeps its stable personal-organization/default-project fallback.

Training tensor transport

Training tensors use inline JSON by default, preserving the behavior of existing clients. To opt into compressed binary transport, configure the service client:

from weaver import ServiceClient

with ServiceClient(
    tensor_transport="http-binary",
    tensor_compression="zstd",  # Optional: Zstandard is the binary-pack default.
) as client:
    ...

AsyncServiceClient accepts the same options. The runnable Pig Latin examples expose the same settings as command-line options:

python examples/pig_latin.py \
  --base-model Qwen/Qwen3.5-0.8B:262144 \
  --lora-rank 16 \
  --tensor-transport http-binary \
  --tensor-compression zstd

All clients also honor environment variables, so the same example can be configured without command-line options:

WEAVER_TENSOR_TRANSPORT=http-binary \
WEAVER_TENSOR_COMPRESSION=zstd \
python examples/pig_latin.py

tensor_compression (or WEAVER_TENSOR_COMPRESSION) only takes effect with http-binary. For token-in requests with any supported loss, the SDK moves eligible dense input tensors into the binary pack; control metadata and other values remain JSON. When an operation returns output tensors, the SDK downloads and materializes them back into the legacy public response shape automatically. Datum construction, training calls, and result handling therefore do not need to change. Keep the default transport when connecting to an older Weaver server/trainer deployment that does not support binary tensor packs.

Quickstart

from weaver import ServiceClient

def main():
    with ServiceClient() as client:
        session = client.ensure_session()
        print(session)

if __name__ == "__main__":
    main()

Give a new Session an optional experiment name and searchable string labels while selecting its scope by human-readable references:

with ServiceClient(
    organization="research",
    project="alignment",
    name="PPO baseline",
    labels={"dataset": "math", "environment": "staging"},
) as client:
    client.ensure_session()

Empty name/labels are omitted from the create request, preserving compatibility with legacy servers and existing call sites.

weaver organizations list
weaver projects list --organization research
weaver scope resolve --organization research --project alignment

Organization slugs are globally unique. Project slugs and names are unique inside their organization; an ambiguous display name is rejected instead of guessed.

Supported models and training modes

The backwards-compatible call still returns model names. Request details when choosing between the independently priced LoRA and Full-FT modes:

models = client.list_supported_models(detailed=True)
for model in models:
    for mode in model.training_modes:
        print(model.name, mode.display_name, mode.prices)

training_modes contains only modes that the model explicitly supports and that have all four effective prices. A missing Full-FT quote means Full-FT is not supported; the SDK never fabricates a 10× display price.

The CLI uses one compact row per mode with Train, Input, Cached input, and Output columns. Filter to one mode, emit names only, or request stable JSON when needed:

weaver list supported-models
weaver list supported-models --mode full-ft
weaver list supported-models --format names
weaver list supported-models --format json

Managed datasets

Managed datasets expose authorized catalog metadata and stable sample references. Every immutable dataset version declares content_visibility: protected responses hide token identities, while public responses may preserve them. The first release is deliberately SFT-only for both visibility modes. Select an explicit dataset name and version, then ask the model-bound training client for effective lengths before packing whole samples:

from weaver import ServiceClient
from weaver.types import Datum, SampleRef

with ServiceClient() as service:
    service.ensure_session()
    for dataset in service.datasets.list(compatible_model="Qwen/Qwen3-8B"):
        print(
            dataset.name,
            dataset.version,
            dataset.sample_count,
            dataset.content_visibility,
        )

    ref = SampleRef(dataset="hq-math", version="2026-08", sample_idx=42)
    trainer = service.create_model(
        base_model="Qwen/Qwen3-8B",
        training_max_sequence_length=4096,
    )
    resolved = trainer.resolve_sample_ref_lengths([ref])[0]
    length = resolved.input_token_count
    print(length)

    datum = Datum.from_sample_ref(
        dataset=ref.dataset,
        version=ref.version,
        sample_idx=ref.sample_idx,
        datum_id="batch-7-item-3",
    )
    result = trainer.forward_backward([datum], "cross_entropy")

The model fixes the tokenizer, chat template, and complete pre-shift token limit; clients cannot override them per reference. input_token_count is the resulting autoregressive training length after shifting (and is smaller than the pinned full-token limit), which is sufficient for client-side whole-sample packing. Every SampleRef, regardless of content_visibility, supports only the built-in cross_entropy forward_backward operation. Managed references cannot be used with forward, custom/surrogate losses, sample, or compute_logprobs, and the first release does not expose dataset download. These limits do not affect ordinary token-in datums. Managed and ordinary datums may be mixed with http-binary tensor transport. Dense ordinary tensors go into the binary pack; SampleRef identifiers stay inline. Reference-only requests have no dense pack and use JSON automatically. The built-in cross-entropy aggregation configuration is forwarded unchanged, including sum. Requests may pass request metadata and caller-owned loss_fn_inputs; those inputs remain attached to their original datum. The server supplies model_input, target_tokens, loss_mask, and weights, so callers cannot use those names in a managed datum's loss_fn_inputs.

A protected response carries a server-resolved content_visibility="protected"; any token-identity array contains only the response-only -8 sentinel at the true length, and label-dependent per-token outputs such as logprobs and elementwise loss are rejected. Never feed -8 back into ModelInput or target_tokens. A public response may contain real non-negative token identities, but has the same SFT-only request boundary.

Usage

See examples/weaver_walkthrough.ipynb for an interactive walkthrough of the full SDK workflow using a Pig Latin translation task — covering data preparation, LoRA / full fine-tuning, sampling, and checkpoint management.

For a complete runnable script, see examples/pig_latin.py. For large packed datasets, examples/streaming_sft.py shows bounded token-budget batching and submit-ahead.

Generation control (full fine-tuning only)

RL weight swaps need in-flight generation to stop before new weights land. A sampling client can freeze its inference engine and resume it afterwards:

with sampling_client.paused(mode=PauseMode.ABORT):
    path = training_client.save_weights_for_sampler(name="step-42")
    new_client = service.create_sampling_client(
        model_path=path, model_id=model_id, base_model=base_model
    )

Two things to know before using it:

• The pause is engine-wide, not per sampling session. It freezes every in-flight request on the engine serving that model, including ones issued through an earlier sampling session. That is what makes it usable for weight swaps — the requests you want to abort belong to the previous weight epoch — but it also means a pause is never scoped to "just my requests".
• Full fine-tuning only. Those models get a dedicated engine. LoRA adapters are served from one shared engine per base model, where a pause would abort generation for unrelated tenants, so the call is rejected before any request is sent.

Prefer paused() over calling pause_generation() / continue_generation() directly: a pause that never reaches its resume leaves the engine frozen indefinitely, and there is no server-side auto-resume. The async client mirrors this as async with.

HuggingFace weights export

Checkpoints are stored in the trainer's native distributed format. export_weights() converts one into a HuggingFace directory — a full model for full fine-tuning, a PEFT adapter for LoRA — and download_weights() fetches it to disk:

artifact = training_client.export_weights()          # save current weights + export
artifact = training_client.export_weights(checkpoint=ckpt)  # export an existing checkpoint

service.download_weights(artifact, "./hf-weights")

weaver checkpoint export weaver://<model>/checkpoints/step-42
weaver checkpoint download weaver://<model>/checkpoints/step-42 -o ./hf-weights

Three things to know:

• Export is explicit, download never triggers one. Converting a full model is minutes of compute and tens of GB of storage, so download_weights() fails with a "run export_weights first" error rather than silently starting a conversion.
• Artifacts expire independently of their checkpoint (7 days by default, ttl_seconds to change). Deleting the source checkpoint does not delete the artifact, and vice versa.
• LoRA exports an adapter by default. Pass merge_adapter=True to fold it into the base model and get a full HF model instead; it is rejected for full fine-tuning models.

Downloads run in parallel, resume interrupted files, refresh expired URLs, and verify each file's sha256 before publishing it. Both methods have Async* twins.

Deploying a checkpoint

deploy_checkpoint() publishes a checkpoint as a public, OpenAI-compatible endpoint: the server converts it to HuggingFace format, launches a dedicated inference workload, and registers that workload on the NorthGate gateway under the name you choose.

deployment = training_client.deploy_checkpoint(ckpt, name="my-chat-model")
print(deployment.endpoint)          # OpenAI-compatible URL

service.list_deployments()
service.get_deployment(deployment.id)
service.delete_deployment(deployment.id)

weaver deployment create weaver://<model>/checkpoints/step-42 --name my-chat-model
weaver deployment list
weaver deployment get <deployment-id>
weaver deployment delete <deployment-id>

Four things to know:

• Publishing is permission-gated and off by default. The deployment.publish capability is granted by principal origin, not by Weaver role: an SSO session always qualifies, an API key only when it was minted under an IAM biz_code on the server's allowlist, and a service credential never. A server with the feature switched off answers 503. Both cases raise a WeaverAPIError that names what has to change. Listing, reading and deleting your own deployments need no capability — whoever published an endpoint can always take it down.
• A deployment is independent and long-lived. It does not share the training inference instance, it outlives the training session, and it holds its GPUs until you delete it. It also pins the source checkpoint and the exported artifact against garbage collection.
• The name is global and must be valid everywhere it lands. It is the served model name, the gateway's model_name, and a Kubernetes label at once: at most 63 characters of letters, digits, ., - and _, starting and ending alphanumerically. overwrite=True replaces an existing gateway registration; it does not free a name Weaver already uses.
• It takes tens of minutes, dominated by the conversion. Pass wait=False to get an OperationHandle instead of blocking. Every method has an Async* twin.

Ecosystem

NexRL is the companion RL training framework. In its training-service mode, NexRL orchestrates the full RL loop (rollouts, trajectory collection, policy updates) while Weaver handles the underlying training and inference services.

Use Cases

OpenClaw Autonomous Learning — MetaClaw has integrated Weaver as an RL backend. By setting rl.backend=weaver, MetaClaw turns every live conversation into a learning signal and uses Weaver for cloud-based LoRA training, enabling personal agents to continuously evolve without a local GPU.

Deep Dive

For more technical details, see Deep Dive into Weaver.