NeMo Flow

nemo-flow is the NeMo Flow package for Python applications. It gives Python code access to a portable agent runtime for execution scopes, middleware, plugins, lifecycle events, adaptive behavior, and observability around tool and LLM calls.

The package wraps the shared Rust runtime, so Python applications use the same runtime semantics as the Rust and Node.js surfaces.

Why Use It?

• 🧭 Own execution context in Python: Group agent, tool, and LLM work into one scope tree from Python application code.
• 🛡️ Package policy around callbacks: Use guardrails and intercepts to block work, sanitize observability payloads, rewrite requests, or wrap execution.
• 📡 Emit one lifecycle stream: Send runtime events to in-process subscribers, Agent Trajectory Interchange Format (ATIF), OpenTelemetry, or OpenInference workflows.
• 🧩 Integrate without a framework migration: Wrap framework or provider callbacks while preserving the application’s orchestration model.

What You Get

• ✅ Scope, tool, and LLM helpers: Managed boundaries that emit lifecycle events and run middleware in a consistent order.
• ✅ Middleware APIs: Guardrails and intercepts for tool and LLM requests, responses, and execution.
• ✅ Subscribers and exporters: Event consumers for observability and diagnostics.
• ✅ Plugin and typed helpers: Public modules for plugins, codecs, typed wrappers, adaptive runtime behavior, and observability plugin configuration.
• ✅ Shared Rust runtime semantics: Python behavior aligned with the Rust and Node.js surfaces.

Installation

Install the published package with uv:

uv add nemo-flow

If you are not using uv, install it with pip:

pip install nemo-flow

Optional Dependencies

LangChain Integration

LangChain integration is available with the langchain extra:

# With uv
uv add "nemo-flow[langchain]"

# With pip
pip install "nemo-flow[langchain]"

LangGraph Integration

LangGraph integration is available with the langgraph extra, this builds upon and includes the langchain extra as well.

# With uv
uv add "nemo-flow[langgraph]"

# With pip
pip install "nemo-flow[langgraph]"

Deep Agents Integration

Deep Agents integration is available with the deepagents extra. This extra builds upon and includes the langgraph and langchain extras.

# With uv
uv add "nemo-flow[deepagents]"

# With pip
pip install "nemo-flow[deepagents]"

LangChain NVIDIA Integration

The LangChain NVIDIA extra builds upon the langchain extra adding a compatible version of the langchain-nvidia-ai-endpoints package.

# With uv
uv add "nemo-flow[langchain-nvidia]"

# With pip
pip install "nemo-flow[langchain-nvidia]"

To install this along with the langgraph extra, use:

# With uv
uv add "nemo-flow[langgraph,langchain-nvidia]"
# With pip
pip install "nemo-flow[langgraph,langchain-nvidia]"

Getting Started

Register a subscriber, create a scope, and emit a mark event:

import nemo_flow


def on_event(event) -> None:
    print(f"{event.kind} {event.name}")


nemo_flow.subscribers.register("printer", on_event)

with nemo_flow.scope.scope("demo-agent", nemo_flow.ScopeType.Agent) as handle:
    nemo_flow.scope.event("initialized", handle=handle, data={"binding": "python"})

nemo_flow.subscribers.deregister("printer")

Package Surface

The public package modules are:

• nemo_flow.scope
• nemo_flow.tools
• nemo_flow.llm
• nemo_flow.guardrails
• nemo_flow.intercepts
• nemo_flow.subscribers
• nemo_flow.plugin
• nemo_flow.adaptive
• nemo_flow.observability
• nemo_flow.typed
• nemo_flow.codecs

Integrations

• nemo_flow.integrations.langchain
• nemo_flow.integrations.langgraph
• nemo_flow.integrations.deepagents

The compiled extension is exposed as nemo_flow._native.

Documentation

NeMo Flow Documentation: https://nvidia.github.io/NeMo-Flow