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# Flux Agents: A Python First Framework for Building Agentic AI Systems

**[Medium](https://daily.dev/sources/medium_js)** · 20 min read · 3 upvotes · 0 comments

## Summary

Flux Agents is a new open-source Python framework for building production-grade agentic AI systems, designed around three core principles: provider-agnosticism, async-first execution, and middleware-driven cross-cutting concerns. The author built it after frustration with existing frameworks that treat provider flexibility and async as afterthoughts. Key features include a decorator-based tool system that auto-generates schemas from type hints, agent handoffs for multi-agent coordination, composable middleware for logging/guardrails/retries, session and memory management, streaming responses, and a protocol-based provider architecture supporting OpenAI, Anthropic, and Ollama out of the box. The framework targets production use cases requiring audit trails, observability, and graceful degradation — not just demos. Roadmap includes MCP support, a visual workflow builder, Agent Studio, and enterprise observability tooling.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@umarshabbir.ai/flux-agents-a-python-first-framework-for-building-agentic-ai-systems-5deefc39c18c>

## Questions this post answers

### What is Flux Agents and how is it different from LangGraph or CrewAI?

Flux Agents is an open-source, provider-agnostic, async-first Python framework for building single and multi-agent AI systems. Unlike LangGraph, which models orchestration as explicit graphs, or CrewAI, which uses role-based collaboration, Flux uses a protocol-based provider architecture and middleware pipeline, letting the same agent logic run against OpenAI, Anthropic, or Ollama without code changes.

_Developers weighing agent framework tradeoffs can track new entrants like Flux Agents on daily.dev._

### How do you make a Python AI agent work with multiple LLM providers like OpenAI, Anthropic, and Ollama without rewriting the code?

Flux Agents solves this with a Provider Registry where every backend implements the same protocol interface for requests, tool calls, and streaming. An agent is instantiated with a provider object (OpenAIProvider, AnthropicProvider, or OllamaProvider) while the agent's instructions, tools, and logic stay identical, so switching providers is a config change rather than a refactor.

_Teams avoiding vendor lock-in in their AI stack can follow provider-agnostic patterns like this on daily.dev._

### How do I install the Flux Agents Python framework and add provider-specific dependencies?

Install the base package with pip install flux-agents, then add provider extras such as pip install flux-agents[openai], flux-agents[anthropic], or flux-agents[ollama], or install everything with flux-agents[all]. The framework requires Python 3.10 or newer and relies on type hints to auto-generate tool schemas for the model.

_daily.dev helps Python developers keep up with new agentic frameworks worth evaluating for production._

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---

Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents)

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