flowk 0.4.5

A lightweight, modular, and developer-first workflow orchestration engine for AI/LLM pipelines.

Flowk 🌊

Flowk is a lightweight, high-performance workflow orchestration engine specifically designed for AI and LLM pipelines. It offers a simpler, developer-first alternative to complex frameworks like LangGraph.

Everything you need to build Enterprise Agentic Workflows is packed into pure, readable Python: async execution, dynamic routing, CLI visualizers, SQLite/Redis time-travel, Pydantic type-safety, API deployments, streaming, and a local Observability UI.

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🚀 All Features

Core Execution

• Extremely Simple API: Turn standard Python functions into executable graph nodes effortlessly.
• Node Resiliency: Configure Node retries, timeouts, and fallback policies automatically (@g.node(retries=3)).
• Standard Routing: Route branch paths explicitly using standard Python functions (g.route()).
• 🛡️ Type-Safety: Graph states are strictly validated upon every transition using Pydantic.
• ⚡ Async & Streaming: Natively await APIs and stream real-time events (g.astream()).
• Parallel Fan-Out: Split a node into three; Flowk natively runs them exactly concurrently via asyncio.gather.

Intelligence

• 🧠 Zero-Boilerplate Auto-Routing: Eliminate if/else logic by letting OpenAI/Anthropic pick your exact graph branches using strictly validated zero-shot classification (@g.llm_router).
• 📦 Multi-Agent Composition: Build nested agent networks by packaging entire sub-graphs as natively executable Nodes (g.as_node()).

Developer Experience & Tooling

• 🛑 Human-in-the-Loop: Set breakpoints to pause execution and later resume the exact thread stacks.
• 🚀 1-Click Platform Deployment: Turn any Flowk .py into a fully typed FastAPI instance (g.serve()) or spin up the full local workflow environment with flowk dev.
• Terminal Visualization: Render beautiful CLI graphs of your execution layout (g.show()).
• Time Travel Replays: Encounter a bug? Flowk traces everything via Event Sourcing. Replay historical executions in debug mode (g.replay()).
• 📊 Observability Dashboard: Track sessions, step through State Diffs, and view the Event Log visually through the local React dashboard (flowk dev or flowk ui).
• CLI Telemetry: Query your local .flowk/flowk.db persistence layer instantly using flowk runs list and flowk runs inspect <run_id>.
• 🧩 Pluggable Metrics: Hook models (e.g. OpenAIPlugin) into MetricsRegistry to precisely track token consumption and cost.

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📦 Installation

Flowk is modular by design.

# Core execution engine
pip install flowk

# Add-ons:
pip install "flowk[api]"    # 1-Click FastAPI Deployment & Async Jobs
pip install "flowk[ui]"     # React Observability Dashboard
pip install "flowk[llm]"    # Auto-Router & Token Metrics
pip install "flowk[redis]"  # Distributed Persistence

# Install Everything
pip install "flowk[all]"

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⚡ Quick Start

Building your first AI agent pipeline with Flowk takes less than a minute.

import asyncio
from pydantic import BaseModel
from flowk import Graph

# 1. Define strict state
class AgentState(BaseModel):
    query: str
    processed: bool = False

g = Graph(state_schema=AgentState)

# 2. Define Nodes
@g.node(retries=3)  # Built-in resiliency
async def intake(query: str, state: dict):
    state["query"] = query
    print(f"📥 Received: {query}")
    return query

@g.node()
async def agent(query: str, state: dict):
    state["processed"] = True
    print("🤖 Processing context...")
    return f"Processed Output for {query}"

# 3. Connect nodes
g.connect(intake, agent)

# 4. View Architecture
g.show()

# 5. Run async pipeline
if __name__ == "__main__":
    result = asyncio.run(g.arun("Calculate the speed of light."))

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🧠 Zero-Boilerplate LLM Auto-Routing

Why write manual if/else logic when LLMs can intelligently route workflows based directly on your docstrings? Flowk handles the prompts and the deterministic structured outputs for you.

@g.llm_router(
    model="gpt-4o-mini",
    targets={
        "math_node": "Use this if the query contains numbers or equations.",
        "search_node": "Use this if the user asks for real-time news."
    }
)
def supervisor_router(state: dict):
    return state.get("query", "")

g.connect(parse_input, supervisor_router) 

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🚀 1-Click API Gen (FastAPI)

Skip writing API boilerplate. Flowk automatically converts your Graph and Pydantic models into a fully validated FastAPI instance with /docs, /invoke, and /stream.

# Launch app
g = Graph(state_schema=MyState)
g.connect(A, B)

if __name__ == "__main__":
    g.serve(host="0.0.0.0", port=8000)

Invoke your pipeline instantaneously:

curl -X POST "http://localhost:8000/invoke" \
     -H "Content-Type: application/json" \
     -d '{"initial_state": {"user_id": 123}, "input_data": "Search for X"}'

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🛑 Human-in-The-Loop (Interrupts)

Create breakpoints in your workflows. Execution suspends gracefully to allow human review (e.g. paying an invoice), letting you resurrect the session precisely where you left off.

# Set visual breakpoint
g.compile(interrupt_before=["commit_payment_node"])

# Run pipeline until suspended
for event in g.astream(input_data, session_id="user_john"):
    if event["type"] == "interrupt":
        print("Payment halted. Waiting for human approval...")

# Resume from checkpoint using identical session_id
g.arun(None, session_id="user_john")

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📊 Observability Dashboard & Persistence

Flowk effortlessly saves run-histories and execution events. By default, Flowk standardizes its local storage to a .flowk/flowk.db directory structure, automatically keeping your projects clean.

# Native Memory Configurations
g = Graph()                                         # Auto-creates .flowk/flowk.db
g = Graph(checkpoint_db="local_traces.db")          # Custom SQLite Storage
g = Graph(checkpoint_db="redis://localhost:6379/0") # Redis

Spin up the native Production-Grade Dashboard (v2) to review these checkpoints visually with interactive graph topology and state diffing. The recommended workflow is using the dev command to launch the API, the UI, and automatically open your browser:

flowk dev
# Automatically opens http://localhost:8502

The new dashboard provides:

• Interactive SVG Graphs: Visualize your workflow logic and execution paths.
• Event Sourcing Timeline: See every immutable node transition as it happened.
• State Diff Engine: Compare state snapshots perfectly step-by-step.
• Session History: Browse and resume historical agent runs directly from SQLite/Redis.

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📦 Multi-Agent Composition

Build powerful hierarchical orchestrations by compiling smaller sub-graphs and mounting them identically as nodes within a massive supervisor pipeline.

# Internal Research Graph
research_graph = Graph()
research_graph.connect(search_web, summarize)

# Packaged perfectly as a Node
research_node = research_graph.as_node(state_key="research_metadata")

# Plugged into Chief Editor Agent
main_graph = Graph()
main_graph.connect(plan_outline, research_node)

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🐞 Time Travel & Execution Telemetry

If a run fails in production, you can trace exactly what inputs hit what nodes.

# Run your pipeline in debug mode
g.debug("input", session_id="user_1")

# Encountered a crash? Replay the precise global trajectory:
g.replay("run_id_outputted_by_telemetry")

# Track Cost Metrics via extensible Plugins
from flowk.plugins.llm import OpenAIPlugin
from flowk import MetricsRegistry

PluginManager.register(OpenAIPlugin(model="gpt-4o"))
print(MetricsRegistry.get_summary()) # => Evaluated 4040 tokens ($0.12)