Flowra
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@stepmethods, a single entry point, and typed spec/result contracts - Persistent state —
Scalar[T]andMutableList[T]with incremental dirty-tracking and pluggable storage (in-memory, file-based, or custom) - Tool integration —
@tooldecorator for local functions, MCP server support, DI into tool handlers, agents as tools for LLM-driven delegation - LLM abstraction — provider-agnostic
LLMProviderinterface with immutable message types and real-time streaming (shipsAnthropicVertexProvider,GoogleVertexProvider,OpenAIProvider,OpenAIResponsesProvider) - Agents as tools —
@agent_tooldecorator exposes an agent as a tool the LLM can call autonomously; sub-agent runs its own system prompt and tool loop - Cooperative interrupts —
InterruptTokenfor graceful cancellation across the entire execution tree - Pre-built agents —
ChatAgent(multi-turn chat with session history) andTurnAgent(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 LLMProvider, SystemMessage, TextBlock
from flowra.llm.providers.anthropic_vertex import AnthropicVertexProvider
async def main() -> None:
async with AnthropicVertexProvider() as provider:
config = ChatConfig(
llm_config=LLMConfig(provider=provider, model="claude-sonnet-4-5@20250929"),
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(user_message=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
Release files for flowra 0.0.73
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| flowra-0.0.73-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 735.8 kB
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