crewAIInc / crewAIInc/crewAI

SABLE: one real CrewAI execution → auditable reliability trace in ~10 minutes

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vendor-pitch
Dominant language
Python
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Description

SABLE now has a reusable GitHub Actions path that converts one real CrewAI execution into an auditable sable.submission.v0.9 trace, normalized SABLE trace, and SHA-256 evidence manifest. The path has already been live-validated with LangGraph and CrewAI applications in the SABLE repository; validation, deterministic evaluation, replay integrity, task-success enforcement, and artifact upload all passed.

We'd like one independently maintained CrewAI example/app to run the same path so SABLE can capture an independent external trace.

Minimal integration:

jobs:
  sable:
    uses: socksninja/sable-agent-reliability/.github/workflows/external-verification-reusable.yml@main
    with:
      agent_command: python scripts/run_one_sable_task.py

The command only needs to run one real CrewAI task and write artifacts/submission-v09.jsonl. The reusable workflow handles validation and evidence packaging.

Docs: https://github.com/socksninja/sable-agent-reliability/blob/main/docs/10_MIN_EXTERNAL_VERIFICATION.md
Evidence implementation: https://github.com/socksninja/sable-agent-reliability/pull/18

No endorsement or runtime change is requested. The target is deliberately small: one real task, one run, inspectable evidence.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with docs/10_MIN_EXTERNAL_VERIFICATION.md and the referenced external-verification-reusable.yml workflow. Prepare a CrewAI example with an entry point such as scripts/run_one_sable_task.py, configure the reusable workflow, and run one real task. Done means artifacts/submission-v09.jsonl is produced and validation, evaluation, replay integrity, task-success enforcement, and artifact upload pass.

Written by the indexing model from the issue text.

Assessment

Tech stack
github-actions, python
Domain
ai, ci-cd
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
56/100

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