Traversal

Concurrent DAG execution for Python, powered by Rust. Free, MIT licensed, in-process, and built for ordinary Python functions and tool calls.

Traversal owns dependencies, readiness, concurrency limits, and structured node outcomes. Your functions own the work. Use an external scheduler to start runs. No service, account, telemetry, or Python runtime dependency is required.

Install

python -m pip install 'traversal==0.2.0'

Includes graph execution, local run history, explicit JSON result reuse and guarded resume. Supports conventional CPython 3.11–3.14 and free-threaded CPython 3.14t. Release wheels target Linux x86_64 (glibc 2.28+), macOS Apple Silicon, and Windows x86_64. Source builds require Rust and Maturin.

A graph in a few lines

from traversal import Graph

graph = Graph("report")
source = graph.add("source", lambda: 5)
left = graph.add("left", lambda n: n * 2, source)
right = graph.add("right", lambda n: n + 3, source)
total = graph.add("total", sum, [left, right])

plan = graph.compile(total)
print(plan.describe())
report = plan.run(max_concurrency=2).raise_for_status()
print(report.outputs["total"])  # 18

Left and right are independently eligible after source completes. Total waits for both. Only requested targets and their ancestors run. Nested lists, tuples, and dictionary values can contain Node references. after=[node] adds an ordering dependency without passing a result. after is a reserved API keyword.

Async functions run on the event loop; ordinary functions use a bounded thread pool. Inside an event loop, use await plan.arun(). On free-threaded CPython 3.14t, independent synchronous Python functions can execute on multiple cores. Regular CPython still limits Python CPU threads through the GIL. Cancellation stops new admission and drains blocking work; tasks should set their own I/O timeouts.

Inspect report.states, report.outputs, and report.failures. Failures include exception type, message and traceback. Independent branches continue; descendants of a failed node are blocked. Calling raise_for_status() is optional.

Plans freeze topology and binding containers. Arbitrary Python objects remain references; shared mutation requires caller coordination. Reports retain all successful intermediate outputs until released. Rerunning performs work again: there is no exactly-once guarantee for external actions.

Multicore Python (0.2.0)

Use a separate free-threaded environment for CPU-heavy Python branches:

uv python install 3.14t
uv venv --python 3.14t .venv-t
uv pip install --python .venv-t 'traversal==0.2.0'
uv run --python .venv-t --no-project python -c "import traversal, sys; print(sys._is_gil_enabled())"
# False: the GIL is disabled in this interpreter.

The same plan.run(max_concurrency=4) API works. Check GIL status after importing all workload dependencies: another extension can enable it. This is an optional Python build, not a flag that turns ordinary CPython into a free-threaded build. Use synchronous functions for CPU branches; CPU work inside async def still blocks the event loop. Coordinate shared mutable inputs explicitly.

In one macOS arm64 measurement, eight workers ran a Python CPU graph in 80 ms on 3.14t versus 275 ms on regular 3.14.5. This is workload-specific, not a promised speedup. Tiny tasks should be batched. See performance evidence and the skill's parallelism guide.

Remember runs and reuse results

from traversal import Cache, Graph, History

history = History(".traversal/runs.sqlite")
graph = Graph("totals", version="1", store=history)
source = graph.add("source", lambda: [1, 2, 3], cache=Cache("input-v1"))
total = graph.add("total", sum, source, cache=Cache("sum-v1"))
plan = graph.compile(total)
first = plan.run().raise_for_status()
print(history.latest(graph="totals"))
print(plan.explain_run(previous=first.run_id))
second = plan.resume(first.run_id)  # Reuses compatible JSON results.

Cache is an explicit declaration that a computation is safe to repeat or skip. Its key must cover changing code, inputs and configuration. All ancestors need keys for downstream reuse. Previously started non-cache work requires a named rerun decision during resume; unknown external effects are never silently replayed. History is opt-in and local. Default records omit task payloads and error messages; caching explicitly opts into bounded JSON result storage. No pickle.

See the complete API and boundaries.

Install the agent skill

The version-matched skill ships in the wheel:

python -m traversal skill --output ~/.codex/skills/traversal

Use your agent's skill directory, or export to any new directory. The command refuses to overwrite an existing folder. The canonical skill teaches PyPI setup, graph construction, execution, and failure inspection. Its examples are executed in CI against the installed wheel. A standalone download is also available in the CI run's traversal-skill artifact.

Develop

Install Rust and uv, then:

uv sync --locked --no-install-project
uv run --no-sync maturin develop --release
uv run --no-sync pytest
cargo test --locked -p traversal-core

Rust topology/state and PyO3 bindings are separate crates. Python adapters have no third-party runtime dependency. PyPy, process pools, distributed workers, connectors and calendar scheduling are outside this release.

Owner: Trevor Ewert / KWIP LLC. MIT license.

Project links

Data verified by PyPI on Sep 15, 2026
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Credits

Author: KWIP LLC

GitHub Statistics

Data verified by PyPI on Sep 15, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.11

Classifiers

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