Add retry policy with transient vs permanent failure classification - #37
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The live graph planner now distinguishes transient failures (timeouts, rate limits, connection drops) from permanent failures (bad input, auth errors, missing resources) and automatically retries transient failures up to a configurable bound. Permanent failures fail fast without retry. Changes: - RetryPolicy dataclass in core/live_graph/core.py classifies errors using pattern matching against known transient/permanent signatures - RetryAwarePlanner in runtime.py wraps any planner to intercept task_failed events and re-enqueue transient failures as retry nodes - 15 new tests covering: transient retry, permanent fail-fast, max retries exhaustion, future-node ordering, cancellation leak guard, and adversarial result-after-cancellation attack - Part 1 benchmark script (part1_proof.py) for the four required cases - README section documenting the extension with full traces Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
theschoolofai
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Aug 10, 2026
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Approved for a strong retry-policy implementation and adversarial test suite; contribution credit is compared against earlier retry PRs.
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Summary
RetryPolicythat classifies task failures as transient (timeouts, rate limits, connection drops) or permanent (bad input, auth errors, missing resources), and aRetryAwarePlannerthat wraps any existing planner to automatically retry transient failures up to a configurable boundFiles changed
s13code/core/live_graph/core.pyRetryPolicydataclass + transient/permanent pattern setss13code/runtime.pyRetryAwarePlannerwrapper + wiring into the runtimes13code/core/live_graph/__init__.pyRetryPolicytests/test_retry_policy.pypart1_proof.pyREADME.md.gitignorepart1_traces.jsonTest plan
uv run pytest -q) — 44 original + 15 newuv run ruff check .)uv run python part1_proof.py)🤖 Generated with Claude Code