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.

Quickstart

from weaver import ServiceClient

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

if __name__ == "__main__":
    main()

Select a scope by human-readable references, or discover and resolve available scopes:

with ServiceClient(organization="research", project="alignment") as client:
    client.ensure_session()

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.

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.

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.

Key dates

PyPI data
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  • Released:
Newer release available (1.14.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for nex-agi from gravatar.com nex-agi

Credits

Author: Nex-AGI

License

Proprietary

Requires

Python >=3.10

Provides Extra

dev

Classifiers

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