nelieo-nsp 0.1.3

Nelieo Substrate Protocol (NSP) Python SDK — give AI agents typed, millisecond-latency access to any running application.

Nelieo NSP

Give AI agents instant, typed, millisecond-latency access to any running application — without source code, without UI automation, without screen scraping.

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The Problem with AI Agents Today

Today's AI agents are essentially blind. They see pixels on a screen or a string of HTML. To control software, they have to:

• Take a screenshot, ask a vision model to find a button, move a virtual mouse, and click — and hope the button didn't move.
• Parse fragile HTML or DOM trees that break every time a UI is redesigned.
• Write brittle macros that fail the moment an application updates.

This is a fundamental architectural limitation. The AI is working around the software, not with it.

Nelieo NSP changes that entirely.

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What is NSP?

NSP (Nelieo Substrate Protocol) is a runtime intelligence layer that runs beneath your applications. It injects a lightweight native probe directly into any running process — Java, .NET, Electron, V8/Chrome — and exposes the application's complete live internal state as a typed, structured API.

Your AI agent doesn't look at the application. It reads the application's brain directly.

Your AI Agent ──→ nelieo-nsp SDK ──→ NSP Daemon ──→ [Live Process Memory]
                                       ↓
                          Typed JSON State + Action Schema

The result:

Capability	Traditional Agent	NSP Agent
Read a value	Vision + OCR + parsing	session.get_float("balance.total")
Click a button	Find pixels, move mouse	session.execute("transfer_funds")
React to changes	Poll screenshots every second	Real-time WebSocket stream, <5ms
Handle UI redesign	Breaks immediately	Zero impact — reading memory, not pixels
Confidence score	None	Per-field, cryptographically attested

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Installation

pip install nelieo-nsp

Requirements:

• Python ≥ 3.11
• Windows 10/11 (64-bit)
• NSP Daemon installed and running

AI Framework Extras

Install with your preferred AI framework to unlock native tool-calling integration:

pip install "nelieo-nsp[openai]"      # OpenAI function calling (GPT-4o, o3, etc.)
pip install "nelieo-nsp[anthropic]"   # Anthropic Claude tool use
pip install "nelieo-nsp[langchain]"   # LangChain / LangGraph agents

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Getting Started

Step 1 — Install the NSP Daemon

The daemon is a lightweight background process that runs on your machine. It handles the actual process injection and state extraction so your Python code stays clean and portable.

Download the NSP Daemon from www.nelieo.com →

The installer:

• Registers axon-daemon.exe as a Windows Service (auto-starts on login)
• Places the daemon at C:\Program Files\Nelieo\axon-daemon.exe
• Creates a default config at C:\ProgramData\Nelieo\axon.toml
• Starts the daemon immediately on port 7842

You can also run the daemon manually for development:

axon-daemon.exe run

Step 2 — Get your API Key

Create your free account and generate an API key at:

platform.nelieo.com →

API keys are scoped, rotatable, and tied to your account. They look like this: nsp_live_sk_...

Step 3 — Write your first agent

import asyncio
from nelieo_nsp import NSPClient

async def main():
    async with NSPClient(api_key="nsp_live_sk_...") as client:

        # See every application the daemon is currently tracking
        apps = await client.list_apps()
        for app in apps:
            print(f"  {app.app_name}  |  {app.runtime}  |  pid={app.pid}")

        # Attach to an application by name
        session = await client.attach("Ghidra")

        # Read live internal state — typed, instant, zero-scraping
        project_name = session.get_str("project.name")
        function_count = session.get_int("program.function_count")
        print(f"Project: {project_name} | Functions analyzed: {function_count}")

asyncio.run(main())

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Core Concepts

The State Tree

When NSP attaches to an application, it maps every live object in memory into a typed, navigable state tree. You can read any field using dot-notation keys:

state = await client.get_state(pid)

# Read any field from the live application memory
print(state.get("workspace.active_file"))       # str
print(state.get("editor.cursor_line"))          # int
print(state.get("project.has_unsaved_changes")) # bool

The daemon refreshes the state continuously. You always get the latest value without polling.

Actions

NSP doesn't just read applications — it can execute internal functions directly. The daemon discovers every callable action inside the application and exposes them with a typed schema.

# List all actions available in this application
schema = await client.get_schema(pid)
for action in schema.actions:
    print(f"  {action.name} ({action.action_class}) — {action.description}")

# Execute a ReversibleWrite action (no confirmation required)
result = await session.execute("open_new_tab")
print(f"Done in {result.latency_ms}ms")

# Execute an IrreversibleWrite action (verify gate required)
result = await session.execute(
    "delete_workspace",
    parameters={"workspace_id": "ws_9f3a"},
    verify=True,
    verify_expression="workspaces.count < 5",
    verify_timeout_ms=3000,
)

Real-Time State Streaming

Subscribe to a live WebSocket stream of state changes. Your agent reacts to what happens inside the application in real-time — no polling, no screenshots.

async with session.watch() as stream:
    async for event in stream:
        print(f"Changed keys: {event.changed_keys}")

        if "compiler.build_status" in event.changed_keys:
            await session.refresh()
            status = session.get_str("compiler.build_status")

            if status == "ERROR":
                # AI agent reads the error and triggers a fix
                error_msg = session.get_str("compiler.last_error")
                await session.execute("open_error_panel")

Waiting for Conditions

Instead of polling in a loop, NSP lets you declare what you are waiting for:

# Wait until the application reaches a specific state
await session.wait_for_key("export.status", "complete")
print("Export finished!")

# Or use a custom predicate
await session.wait_for(
    lambda s: s.get_int("queue.pending_items") == 0,
    timeout=60.0
)

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AI Framework Integration

OpenAI (GPT-4o, o3, o4-mini)

NSP automatically generates OpenAI-compatible tool definitions from the application's live action schema. Your agent can call application functions directly using native function-calling.

import openai
from nelieo_nsp import NSPClient
from nelieo_nsp.adapters import NSPToolAdapter

async def run_agent():
    async with NSPClient(api_key="nsp_live_sk_...") as client:
        session = await client.attach("MyApp")
        adapter = NSPToolAdapter(session)

        messages = [{"role": "user", "content": "Summarize the current project status."}]

        while True:
            response = await openai_client.chat.completions.create(
                model="gpt-4o",
                messages=messages,
                tools=adapter.openai_tools(),  # Auto-generated from app schema
            )

            if not response.choices[0].message.tool_calls:
                print(response.choices[0].message.content)
                break

            # Dispatch tool calls directly into the running application
            for tool_call in response.choices[0].message.tool_calls:
                result = await adapter.dispatch_openai_tool_call(
                    tool_call.function.name,
                    tool_call.function.arguments,
                )
                messages.append({"role": "tool", "content": str(result), "tool_call_id": tool_call.id})

Anthropic Claude

from nelieo_nsp.adapters import NSPToolAdapter

adapter = NSPToolAdapter(session)

response = await anthropic_client.messages.create(
    model="claude-opus-4-5",
    tools=adapter.anthropic_tools(),  # Auto-generated from app schema
    messages=[{"role": "user", "content": "Find all critical bugs in the open project."}],
)

for block in response.content:
    if block.type == "tool_use":
        result = await adapter.dispatch_anthropic_tool_call(block.name, block.input)

LangChain / LangGraph

from nelieo_nsp.adapters import NSPToolAdapter

adapter = NSPToolAdapter(session)
langchain_tools = adapter.langchain_tools()  # Drop directly into any LangChain agent

agent = create_react_agent(llm, langchain_tools)
result = await agent.ainvoke({"messages": [("user", "Archive all completed tasks.")]})

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Supported Runtimes

Runtime	Examples	Attach Method
Java / JVM	Ghidra, IntelliJ IDEA, any JVM app	JVMTI native injection
.NET / CLR	Mission Planner, any .NET app	CLR in-process bridge
JavaScript / V8	Chrome, Electron apps, Node.js	V8 Inspector / CDP

New runtime support is added continuously. Check docs.nelieo.com/probes/overview for the latest.

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Safety & Confidence Model

NSP enforces a multi-layer safety model to ensure AI agents cannot execute destructive operations without explicit verification.

Every action in an application is classified at discovery time:

Action Class	What It Does	Confidence Required	verify Required
Read	Reads memory state only	None	No
ReversibleWrite	Modifiable / undoable change	≥ 0.80	No
IrreversibleWrite	Permanent, non-undoable change	≥ 0.95	Yes — enforced

The SDK enforces these rules client-side before the request even leaves the machine. An IrreversibleWrite called without verify=True raises NSPSafetyError immediately.

from nelieo_nsp.exceptions import NSPSafetyError, NSPConfidenceTooLowError

try:
    await session.execute("permanently_delete_user", verify=True, verify_expression="audit.log_written == true")
except NSPSafetyError:
    print("Safety gate blocked this action.")
except NSPConfidenceTooLowError as e:
    print(f"Confidence too low: {e.actual:.0%} required {e.required:.0%}")
    await session.refresh(fresh_probe=True)

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Error Handling

from nelieo_nsp.exceptions import (
    NSPError,                  # Base — catch all NSP errors
    NSPConnectionError,        # Daemon is not running or unreachable
    NSPAuthError,              # Invalid or expired API key
    NSPNotFoundError,          # PID not tracked, or action not in schema
    NSPRateLimitError,         # Rate limit exceeded
    NSPTimeoutError,           # verify gate timed out
    NSPConfidenceTooLowError,  # State confidence below required threshold
    NSPActionError,            # Action dispatched but failed inside the app
    NSPProcessDetachedError,   # Target process exited while attached
    NSPSafetyError,            # Action blocked by safety model
)

try:
    result = await session.execute("send_report", verify=True)

except NSPConnectionError:
    # NSP Daemon is not running — guide the user to start it
    print("Please ensure the NSP Daemon is running. Download: https://www.nelieo.com/")

except NSPProcessDetachedError:
    # Target app closed — wait for it to reopen
    print("Application exited. Waiting for restart...")
    session = await client.wait_for_app("MyApp", timeout=120.0)

except NSPError as exc:
    print(f"NSP error [{exc.code}]: {exc.message}")

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API Reference

Full API reference is available at docs.nelieo.com/sdk/python/overview.

NSPClient

Method	Description
await client.list_apps()	Returns all applications currently tracked by the daemon
await client.get_state(pid)	Returns the full typed state tree for a PID
await client.get_schema(pid)	Returns the full action schema for a PID
await client.attach(name)	Attach to an application by name, returns NSPSession
await client.attach_by_pid(pid)	Attach by exact PID
await client.wait_for_app(name)	Block until the named application launches, then attach
await client.ping()	Returns daemon health and version

NSPSession

Method	Description
session.get(key)	Read a state key (returns raw value)
session.get_str(key)	Read state key as str
session.get_int(key)	Read state key as int
session.get_float(key)	Read state key as float
session.get_bool(key)	Read state key as bool
await session.refresh()	Fetch the latest state from the daemon
await session.execute(action, ...)	Execute an action inside the application
session.watch()	Open a real-time WebSocket stream of state changes
await session.wait_for(predicate)	Wait until a custom predicate returns True
await session.wait_for_key(key, value)	Wait until state[key] == value

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Frequently Asked Questions

Do I need to install anything on the target machine? Only the NSP Daemon needs to be running. No changes are required to the target application, and no source code access is needed.

Does NSP work with applications I don't own? Yes. NSP attaches to any running process on the machine where the daemon is installed. It does not require source code, developer access, or any modification to the target application.

Is NSP safe to use in production? NSP's safety model is built for production. IrreversibleWrite actions are gated behind verified pre/post conditions that are evaluated inside the process before and after execution. Read operations are completely non-invasive with zero impact on the target application's performance.

Which AI models work with NSP? Any model. NSP's tool adapters generate standard OpenAI-compatible function definitions, Anthropic tool definitions, and LangChain-compatible tools. Any model that supports function/tool calling — GPT-4o, Claude, Gemini, Mistral, and others — works natively.

What is the latency? State reads: < 5ms on first attach, < 1ms on cached reads. Action execution: typically < 10ms (excluding application-side processing time).

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Support & Documentation

Resource	Link
Full Documentation	docs.nelieo.com
API Key & Account	platform.nelieo.com
Email Support	[e-mail]

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