glaip-sdk 0.8.3

Python SDK and CLI for GL AIP (GDP Labs AI Agent Package) - Build, run, and manage AI agents

GL AIP — GDP Labs AI Agents Package

GL stands for GDP Labs—GL AIP is our AI Agents Package for building, running, and operating agents.

Python SDK and CLI for GL AIP - Connect, configure, and manage AI agents on the GDP Labs AI Agents Package.

🚀 Quick Start

Installation

Installing glaip-sdk provides both the Python SDK and the aip CLI command in a single package.

# Using pip (recommended)
pip install --upgrade glaip-sdk

# Using uv (fast alternative)
uv tool install glaip-sdk

# Using pipx (CLI-focused, isolated environment)
pipx install glaip-sdk

Requirements: Python 3.11 or 3.12

Updating: The aip CLI automatically detects your installation method and uses the correct update command:

• If installed via pip: Uses pip install --upgrade glaip-sdk
• If installed via uv tool install: Uses uv tool install --upgrade glaip-sdk
• You can also update manually using the same command you used to install

🐍 Hello World - Python SDK

Perfect for building applications and integrations.

Step 1: Environment Setup

Create a .env file:

# .env
AIP_API_URL=https://your-gl-aip-instance.com
AIP_API_KEY=your-api-key

Step 2: Basic Python Script

# hello_world.py
from glaip_sdk import Client
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize client
client = Client()

# Create a simple agent
agent = client.agents.create(
    name="hello-sdk",
    instruction="You are a helpful assistant who responds clearly and concisely."
)

# Run the agent
result = agent.run("Hello world, what's 2+2?")

print(f"Agent response: {result}")

Step 3: Run Your Script

python hello_world.py

Step 4: Advanced Example with Streaming

# streaming_example.py
from glaip_sdk import Client
import os
from dotenv import load_dotenv

load_dotenv()
client = Client()

# Create agent with streaming
agent = client.agents.create(
    name="streaming-agent",
    instruction="You are a helpful assistant. Provide detailed responses."
)

# Stream the response
print("Streaming response:")
client.agents.run_agent(
    agent.id,
    "Explain quantum computing in simple terms",
    verbose=True,
)
print("--- Stream complete ---")

🎉 SDK Success! You're now ready to build AI-powered applications with Python.

---

💻 Hello World - CLI

Perfect for quick testing and command-line workflows.

Step 1: Configure Connection

# Interactive setup (recommended)
aip configure

Or set environment variables:

export AIP_API_URL="https://your-gl-aip-instance.com"
export AIP_API_KEY="your-api-key"

Step 2: Verify Connection

aip status

Step 3: Create & Run Your First Agent

# Create a simple agent
aip agents create --name "hello-cli" --instruction "You are a helpful assistant"

# List agents to get the ID
aip agents list

# Run the agent with input
aip agents run <AGENT_ID> --input "Hello world, what's the weather like?"

🎉 CLI Success! You're now ready to use the CLI for AI agent workflows.

✨ Key Features

• 🤖 Agent Management: Create, run, and orchestrate AI agents with custom instructions and streaming
• 🧠 Language Models: Choose from multiple AI models per agent with manual PII tag mapping
• 🛠️ Tool Integration: Extend agents with custom Python tools and script management
• 🔌 MCP Support: Connect external services through Model Context Protocols with tool discovery
• 🔄 Multi-Agent Patterns: Hierarchical, parallel, sequential, router, and aggregator patterns
• 🎙️ Audio Interface (beta): Local-only LiveKit voice sessions for talking to agents (install with glaip-sdk[audio])
• 💻 Modern CLI: Rich terminal interface with fuzzy search and multiple output formats

🎙️ Local Voice (LiveKit, Beta)

You can run a local voice loop that joins a LiveKit room, transcribes your speech, routes text into an agent, and speaks the reply back.

Prerequisites

• LiveKit server running (monorepo dev: make -C python/aip-agents livekit-up)
• LiveKit Meet open in browser (monorepo dev: make -C python/aip-agents livekit-meet-open)
• OPENAI_API_KEY set (used by livekit-plugins-openai for STT/TTS)

Monorepo Demo Sequence

# One-time install
make -C python/aip-agents install-audio

# Terminal 1: LiveKit server
make -C python/aip-agents livekit-up

# Terminal 2: Join with browser (enable mic)
make -C python/aip-agents livekit-meet-open

# Terminal 3: Run agent (recommended: debug logs)
AIP_AUDIO_DEBUG=1 make -C python/aip-agents audio-agent-up

# Optional: validate join/disconnect (no browser, no mic)
make -C python/aip-agents livekit-smoke-join

Tip: If STT shows odd fragments, it's often speaker-to-mic echo; use headphones.

Example .env values for the repo defaults:

LIVEKIT_URL=ws://localhost:7880
LIVEKIT_API_KEY=devkey
LIVEKIT_API_SECRET=devsecretdevsecretdevsecretdevsecret
LIVEKIT_ROOM_NAME=aip-audio-demo
OPENAI_API_KEY=...

Install

pip install "glaip-sdk[audio]"

Run the SDK example

From the repo:

cd python/glaip-sdk
poetry install --extras "audio"
poetry run python examples/sdk/05_audio_session.py

More details:

• python/glaip-sdk/docs/how-to-guides/audio-interface.md
• python/glaip-sdk/examples/sdk/livekit-local-dev.md

🌳 Live Steps Panel

The CLI steps panel now streams a fully hierarchical tree so you can audit complex agent runs without leaving the terminal.

• Renders parent/child relationships with │├└ connectors, even when events arrive out of order
• Marks running steps with spinners and duration badges sourced from SSE metadata before local fallbacks
• Highlights failures inline (✗ reason) and raises warning glyphs on affected delegate branches
• Derives deterministic “💭 Thinking…” spans before/after each delegate or tool action to show scheduling gaps
• Flags parallel work with a dedicated glyph and argument-derived labels so simultaneous tool calls stay readable
• Try it locally: poetry run python scripts/replay_steps_log.py --transcript tests/fixtures/rendering/transcripts/parallel_research.jsonl --output /tmp/parallel.log

📚 Documentation

📖 Complete Documentation - Visit our GitBook for comprehensive guides, tutorials, and API reference.

Quick links:

• Quick Start Guide: Get your first agent running in 5 minutes
• Agent Management: Complete agent lifecycle management
• Custom Tools: Build and integrate custom tools
• MCP Integration: Connect external services
• API Reference: Complete SDK reference

🧪 Simulate the Update Notifier

Need to verify the in-session upgrade flow without hitting PyPI or actually running pip install? Use the bundled helper:

cd python/glaip-sdk
poetry run python scripts/mock_update_notifier.py
# or customize the mock payload:
# poetry run python scripts/mock_update_notifier.py --version 3.3.3 --marker "[nightly build]"

The script:

• Launches a SlashSession with prompt-toolkit disabled (so it runs cleanly in tests/CI).
• Forces the notifier to believe a newer version exists (--version 9.9.9 by default).
• Appends a visible marker (default [mock update]) to the banner so you can prove the branding reload happened; pass --marker "" to skip.
• Auto-selects “Update now”, mocks the install step, and runs the real branding refresh logic.
• Resets module metadata afterwards so your environment remains untouched.

You should see the Rich banner re-render with the mocked version (and optional marker) at the end of the run.