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Flowra

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Flow infra for building stateful, persistent LLM agents with tool use, parallel execution, and crash recovery. Requires Python 3.12+.

Features

  • State machine agents — define agents as Agent[Spec, Result] classes with @step methods, a single entry point, and typed spec/result contracts
  • Persistent state — Scalar[T] and MutableList[T] with incremental dirty-tracking and pluggable storage (in-memory, file-based, or custom)
  • Tool integration — @tool decorator for local functions, MCP server support, DI into tool handlers, agents as tools for LLM-driven delegation
  • LLM abstraction — provider-agnostic LLMProvider interface with immutable message types and real-time streaming (ships AnthropicVertexProvider, AnthropicFoundryProvider, OpenAIProvider, AzureOpenAIProvider, OpenAIResponsesProvider, GoogleVertexProvider)
  • Agents as tools — @agent_tool decorator exposes an agent as a tool the LLM can call autonomously; sub-agent runs its own system prompt and tool loop
  • Cooperative interrupts — InterruptToken for graceful cancellation across the entire execution tree
  • Pre-built agents — ChatAgent (multi-turn chat with session history) and TurnAgent (single-turn LLM tool loop with hooks and caching)

Installation

# Base package (no LLM providers)
pip install flowra

# With specific providers
pip install flowra[anthropic]
pip install flowra[openai]
pip install flowra[google]

# All providers
pip install flowra[providers]

Quick start

import asyncio

from flowra.agent import AgentRuntime, InMemorySessionStorage
from flowra.lib import LLMConfig
from flowra.lib.chat import ChatAgent, ChatConfig, ChatResult, ChatSpec
from flowra.llm import SystemMessage, TextBlock
from flowra.llm.providers.openai import OpenAIProvider


async def main() -> None:
    async with OpenAIProvider(api_key="sk-...") as provider:
        config = ChatConfig(
            llm_config=LLMConfig(provider=provider, model="gpt-4o"),
            system=[SystemMessage(blocks=[TextBlock(text="You are a helpful assistant.")])],
        )

        runtime = AgentRuntime(
            agents={"chat": ChatAgent},
            storage=InMemorySessionStorage(),
            services={ChatConfig: config},
        )

        while True:
            user_input = input("You: ")
            if not user_input:
                break

            result = await runtime.run(agent=ChatAgent, spec=ChatSpec.text(user_input))

            if isinstance(result, ChatResult) and result.response:
                print(f"Assistant: {result.response}")


asyncio.run(main())

Package structure

flowra/
├── llm/        # LLM abstraction (messages, blocks, provider interface)
├── tools/      # Tool definition, registration, execution
├── agent/      # Agent framework + execution engine + persistence
└── lib/        # Pre-built agents (ChatAgent, TurnAgent, hooks, caching)

Documentation

  • Getting Started — from installation to a working chatbot with tools in 5 minutes
  • Working with LLMs — providers, streaming, structured output, caching, extended thinking
  • Tools — tool groups, MCP servers, service injection
  • Agents — custom agents, state machines, control flow, parallel execution
  • Patterns — multi-agent patterns: router, pipeline, race, fan-out
  • Observability — hooks, spans, MLflow and OTel integrations

Development

make deps      # install dependencies (uv sync)
make check     # lint + test
make chat      # run interactive console chat example

Metadata

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