This release is a pre-release and may not be stable for production use.
PenguiFlow
A Python-native runtime for typed, steerable, bounded AI agents — and the deterministic pipelines under them.
PenguiFlow runs async node graphs where every hop validates its data, every run stays inside a budget, and the same core powers both a deterministic data pipeline and a tool-using agent. It is asyncio-only and built on Pydantic v2, with no heavy runtime dependencies.
Why PenguiFlow
Many agent and pipeline frameworks are loosely-typed loops: a node returns the wrong shape and you find out several hops later, a planner runs past its budget with no ceiling, a crash loses the run's state, and approving a risky step means not automating it. PenguiFlow treats those as the framework's responsibility, not yours:
- Typed at every boundary. Each node validates its input and output against Pydantic models, so malformed data is caught at its source instead of downstream.
- Bounded by design. Bounded queues apply real backpressure; per-trace deadlines, hop budgets, and cancellation keep loops and fan-outs from running away.
- Steerable mid-run. Pause for human approval (HITL), inject steering events, and resume — without losing the trajectory so far.
- Durable and observable. An optional
StateStorepersists events for audit and recovery; every run carries atrace_id, can stream partial output, and records its trajectory. - One runtime for agents and pipelines. The
ReactPlanner(JSON-first tool orchestration, parallel fan-out and joins, pause/resume) runs on the exact same typed, bounded core as a plain data flow.
Architecture at a glance
┌─────────────────────────────────────────────────────────────┐
│ Agents ReactPlanner · ToolNode (MCP / UTCP / HTTP) │
│ JSON tool loop · HITL pause/resume · fan-out/join│
├─────────────────────────────────────────────────────────────┤
│ Flow async node graph · bounded queues (backpressure)│
│ runtime routers · subflows · streaming │
├─────────────────────────────────────────────────────────────┤
│ Envelope Message: trace_id · deadline · hop budget · meta │
│ Reliability per-node retries / timeouts · per-trace cancel │
├─────────────────────────────────────────────────────────────┤
│ Ops StateStore (durable events) · metrics / hooks │
└─────────────────────────────────────────────────────────────┘
emit() ──► typed in/out validated at every node ──► fetch()
Concepts at a glance
- Flow: a directed graph (runtime) you
run(),emit()into, andfetch()results from. - Node: an async function plus a
NodePolicy(validation, retries, timeout). - Message (recommended for production):
Message(payload=..., headers=Headers(tenant=...), trace_id=...)enabling trace correlation, cancellation, deadlines, and streaming. - ReactPlanner (agents): a JSON-first planning loop over your tools, with pause/resume, parallel calls, and trajectory logging.
- StateStore (optional): durability, audit, and event persistence for distributed, ops-ready deployments.
Install
Requirements: Python 3.11+
pip install penguiflow
Common extras:
pip install "penguiflow[planner]" # ReactPlanner + ToolNode integrations
pip install "penguiflow[llm]" # native LLM provider SDKs
pip install "penguiflow[a2a-server]" # A2A HTTP+JSON server bindings
pip install "penguiflow[a2a-client]" # A2A client bindings
If you use uv:
uv pip install penguiflow
Quickstart
PenguiFlow has two entry points that share the same runtime: a typed pipeline you wire yourself, and an agent scaffolded from a template.
1) Typed pipeline (runtime)
from __future__ import annotations
import asyncio
from pydantic import BaseModel
from penguiflow import ModelRegistry, Node, NodePolicy, create
class In(BaseModel):
text: str
class Out(BaseModel):
upper: str
async def to_upper(msg: In, _ctx) -> Out:
return Out(upper=msg.text.upper())
async def main() -> None:
node = Node(to_upper, name="to_upper", policy=NodePolicy(validate="both"))
registry = ModelRegistry()
registry.register("to_upper", In, Out)
flow = create(node.to())
flow.run(registry=registry)
await flow.emit(In(text="hello"))
result: Out = await flow.fetch()
await flow.stop()
print(result.upper)
if __name__ == "__main__":
asyncio.run(main())
2) Agent (ReactPlanner via CLI — fastest path)
uv run penguiflow new my-agent --template react
cd my-agent
uv sync
uv run penguiflow dev --project-root .
Documentation
- Docs site (MkDocs): https://hurtener.github.io/penguiflow/
- API reference (every public symbol): https://hurtener.github.io/penguiflow/reference/api/
- Source docs in repo: docs/
Suggested starting points (in-repo sources):
- Getting started: docs/getting-started/quickstart.md
- Core runtime: docs/core/flows-and-nodes.md, docs/core/messages-and-envelopes.md
- Planner: docs/planner/overview.md
- Tool integrations: docs/tools/configuration.md
- Deployment runbooks: docs/deployment/production-deployment.md
- Observability runbooks: docs/observability/metrics-and-alerts.md
- CLI: docs/cli/overview.md
Stability, versioning, and public API
PenguiFlow is on the 3.x line and follows SemVer with a documented public API surface — additions are additive, and breaking changes are called out in the changelog.
- Changelog: CHANGELOG.md
- Versioning & deprecations: VERSIONING.md
- Public API surface: docs/reference/public-api.md
Contributing, security, and support
- Contributing: CONTRIBUTING.md
- Code of Conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Support: SUPPORT.md
License
MIT — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for penguiflow 3.11.3a2
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