Run it once. Explain it forever. Replay it anywhere.
bijux turns complex systems into governed execution: explicit inputs, stable contracts, and artifact-backed provenance you can replay and audit.
Prioritizes transparency over convenience: sharp failure modes, explicit boundaries, and verifiable outputs.
About
Scope & philosophy
bijux is a strict execution model: explicit inputs, contract-first boundaries, and artifact-backed provenance—built to replay runs without guessing.
What breaks in modern systems
Most ecosystems optimize for speed: implicit state, hidden nondeterminism, and “best-effort” execution that cannot be replayed.
That failure mode is universal—AI/ML pipelines, data platforms, DevOps tooling, and scientific workflows all degrade when behavior is not explainable.
What bijux enforces
bijux is a strict toolkit for systems you can defend. Runs are defined by explicit inputs and validated boundaries, producing artifact-backed outputs
with traceable decisions and replayable execution trails. Failures are sharp and local—no silent fallbacks, no hidden recovery.
Roadmap (Near-Term)
bijux's trajectory emphasizes ecosystem consolidation: a unified Python execution paradigm where composition, provenance, replayability, and verification are absolute.
Pure determinism provides stability but limited expressiveness; bijux uniquely integrates governed non-determinism (adaptive agents, probabilistic retrieval) via declarative contracts,
policy constraints, and audits, ensuring bounded, traceable, and accountable behavior for elite, versatile systems in AI, data, and bioinformatics.
Ecosystem layers:
- Foundations: bijux-cli for deterministic resolution and injection; bijux-dag (forthcoming) for resilient DAG semantics, enabling predictable pipelines.
- Engines: bijux-vex as vector execution with bounded approximations and reproducible search; bijux-agent as state-machine orchestration with artifact-backed traces.
- Elite Systems: bijux-rag and bijux-rar as autonomous, verifiable RAG/RAR platforms; agentic-flows for governed execution, replay, and audit trails; agentic-proteins for lab-grade protein design with explicit evaluations; bijux-dna (new) for end-to-end genomics with deterministic transforms and provenance.
Principles:
- Governed Determinism — Identical inputs yield identical outputs; non-determinism is declared, bounded, policy-enforced, and replayable.
- Foundational Priority — CLI/DAG bedrock first; layers accrete without destabilizing invariants.
- Contractual Rigor — Construction-time validation; undefined behavior as defects, with policy-governed audits.
- Failure Precision — Early, emphatic, local failures; no implicit recovery.
- Artifact Provenance — Immutable anchors for inspection, diffing, review, and reproduction.
- Defensible Executions — Runs explicable across time and environments from artifacts alone.
Projects
Production-grade systems
The ecosystem spans agents, retrieval, reasoning, vector execution contracts, and reproducible scientific workflows.
A deterministic, artifact-first protein design runtime and CLI: traceable runs, structured evaluation, and explicit human-in-the-loop decision points—built for lab-grade reproducibility.
Protein design is treated as a verifiable execution model: inputs, plans, evaluations, and acceptance criteria are recorded as artifacts.
This aligns naturally with GLP-style traceability and review expectations in drug discovery workflows.
Structure prediction is provider-gated (explicit opt-in); the default path remains deterministic, inspectable, and auditable. No hidden side effects. No untracked choices.
Used when protein candidates must be compared, reviewed, and reproduced with audit-quality provenance.
Governed execution and replay for high-integrity workflows: deterministic runs, contract-first invariants,
and audit-grade traces backed by immutable artifacts.
agentic-flows defines how runs are planned, executed, traced, replayed, and compared—backed by immutable artifacts and
contract-checked invariants for high-integrity workflows.
Non-determinism is declared and governed, not hidden. The CLI supports plan, run, replay,
diff, and inspect so runs stay reviewable months later.
Used when workflows must be reproducible, reviewable, and provably replayable from artifacts.
Related libraries: bijux-agent, bijux-rag, bijux-rar, bijux-vex.
The substrate for bijux: deterministic command resolution, explicit composition, dependency injection, and an interactive REPL—so every bijux system stays stable as it grows.
Conventional CLI stacks accumulate implicit configuration and fragile discovery that collapses under complexity. bijux-cli forces construction-time guarantees and replayable behavior.
Designed as infrastructure: composable, testable, and predictable across environments.
Used when interfaces and execution semantics must remain reproducible across time, teams, and machines.
Retrieval pipelines as explicit, testable flows: strict boundaries, provenance-aware execution, and replayable outcomes.
Designed to eliminate hidden prompt state and unreplayable retrieval that undermines deployed systems.
Functional core, explicit contracts, immutable dataflow.
Used when retrieval must be attributable and stable under change.
Reasoning as a verifiable process: explicit premises, artifact-backed evidence, deterministic proofs, and replayable verification.
Designed to replace persuasive outputs with inspectable structure and evidence trails.
Proof-like reasoning, not probabilistic hand-waving.
Used when decisions require defensible evidence trails.
Deterministic agents as explicit state machines: artifact-backed actions, structured traces, and replayable execution.
Designed to replace opaque orchestration with inspectable decisions and local, debuggable failure modes.
Strict by design: effects are isolated and every action is traced.
Used when agent behavior must be auditable and reproducible.
Vector search with explicit execution contracts: deterministic exact search or bounded approximate execution, with provenance.
Designed to eliminate hidden nondeterminism and unstable rankings that break reproducibility.
Approximation is explicit, parameterized, and replayable.
Used when search rankings must remain stable across runs.
Coursebooks
Teaching & pedagogy
Coursebooks that teach the discipline implemented across bijux projects:
- Python Functional Programming: underpins immutability and effect isolation used throughout bijux-rag and bijux-agent.
- Python Meta-Programming: informs controlled runtime behavior and predictable composition in bijux-cli.
- Deep Dive Snakemake: codifies reproducible workflow semantics for scientific and bioinformatics pipelines.
- Deep Dive Make: covers dependency truth and parallel safety—mirrors bijux’s verification-first philosophy.
Production-grade functional Python: purity, immutability, monadic flows, algebraic data types, streaming pipelines, property-based testing.
Designed for engineers who need reproducibility and correctness, not convenience-driven mutation.
Functional discipline as an operational advantage.
By the end, you can build Python systems that remain stable, testable, and explainable under change.
A precise runtime model of Python: object creation, class construction, attribute resolution, invocation.
Designed to replace “metaprogramming tricks” with predictable engineering mechanics.
Traceable rules, not folklore.
By the end, you can reason about Python runtime behavior with engineering-level confidence.
Build invariant pipelines: explicit rules, cacheable artifacts, controlled side effects, provenance, deterministic execution.
Designed for research and production workflows that must reproduce across environments.
Provenance is a deliverable.
By the end, you can ship reproducible workflows with principled execution semantics.
Correctness in Make at scale: truthful dependency graphs, atomic publication, parallel safety, self-checking invariants.
Designed to eliminate cycles, races, and accidental dependencies that break reliability.
Build systems you can trust under parallelism.
By the end, you can make complex build systems predictable, parallel-safe, and maintainable.
