Introduction to GL AI Agent Package

GL AIP is an agent package with an SDK-first developer experience. The primary interface is the Python Agent object from glaip-sdk. You can run agents locally for fast iteration, then deploy and run remotely when you need centralized execution.

Start Here (Python SDK, Agent-First)

The fastest path is a minimal Agent:

from glaip_sdk import Agent

agent = Agent(
    name="hello",
    instruction="You are a helpful assistant.",
)

# Undeployed agents run locally (requires `glaip-sdk[local]`).
print(agent.run("Hello!"))

When you deploy an agent, Agent.run() uses server-backed execution by default. You can still force local execution for a deployed agent with local=True.

Recommended Reading Path

• Getting started: install, configure, and quick start.

• Guides: build (agents, tools, MCPs), run (files, HITL), operate (security, config, automation).

• CLI: interactive ops workflows (accounts, runs/transcripts, export/import).

• Resources: Python SDK reference first; REST API is reference-only for internal integrators.

Documentation Map

Use these sections in order when exploring the SDK and CLI:

• Get Started — Install, configure, complete the quick start, and run curated examples.

• Guides — Deep dives on lifecycle management, automation, integrations, and governance.

• Multi-Agent System Patterns — Runnable orchestration templates for complex workflows.

• CLI — Interactive ops workflows (accounts, runs/transcripts, export/import).

• Reference — Definitive API, SDK, and CLI commands for implementation details.

• Resources — Glossary, upgrade checklists, and cookbook pointers.

Role-Based Entry Points

Choose the track that matches how you work today.

Engineers — Ship agents in applications and automation

Why it matters: You need reliable APIs, typed clients, and testable workflows that fit existing services.

Start here:

• Quick Start Guide for first-run success.

• Python SDK Reference for signatures and streaming behaviour.

• Automation & Scripting for CI, cron, and deployment patterns.

Product Managers — Validate agents via GLChat

Why it matters: You review agent behaviour for stakeholders. GLChat gives you fast access, but the CLI helps you list available agents and capture verbose output when needed.

Start here:

• CLI quick start — run an agent end-to-end and see detailed logs.

• CLI landing — task-focused pages (status, agents, runs/transcripts).

• Agents guide › List & run — discover agent IDs/names before launching them in GLChat.

• Automation & Scripting › Choose the right output — switch between rich and plain responses when sharing findings.

Data Developers — Curate prompts, evaluations, and linguistic QA

Why it matters: You iterate on prompts, run guided evaluations, and need to inspect agent transcripts without writing code.

Start here:

• CLI quick start — validate access and run agents.

• Runs & transcripts — capture and review outputs.

• Configuration management guide — master CLI export/import loops for prompt iteration.

• CLI Commands Reference — look up flags for runs, exports, and transcript capture.

Choose Your Interface

Pick the surface that matches your environment. Default path is Python SDK first.

Interface	When to use it	Start here
Python SDK (recommended)	Application code, notebooks, CI, local iteration, type-safe workflows.	Quick Start (SDK) · Python SDK reference
CLI	Interactive ops, demos, export/import promotion loops, transcripts.	CLI · CLI commands reference
REST API (reference-only)	Internal integrations (for example GLChat) or non-Python environments.	REST API reference

Platform Capabilities at a Glance

Symbols: ✅ fully supported · 🛠️ partial via customization/workarounds · 🚧 roadmap

REST exists as a reference-only surface for internal integrations. SDK and CLI are the supported day-to-day surfaces for most users.

Capability	What it covers	REST API (ref)	Python SDK	CLI
Agent lifecycle & metadata	Create/list/update/delete agents with tools, MCPs, and sub-agents	✅	✅	✅
Streaming execution & artifacts	Runs, SSE output, file attachments, artifact links	✅	✅	✅
Tools (native + custom)	Attach catalog tools, upload tools, tool configs	✅	✅	✅
MCP connectors	MCP CRUD, connect/test, tool discovery	✅	✅	✅
HITL approvals	Pause, approve/reject, resume	✅	✅	🛠️
Run history	List runs, statuses, transcripts	✅	✅	✅
Scheduling	Cron schedules and run history	✅	✅	🚧
Memory & persistence	Memory config and chat history	✅	✅	🛠️
Security & privacy	pii_mapping, tool output sharing, secrets hygiene	✅	✅	🛠️

Architecture (Reference)

How the SDK, CLI, and platform fit together

Components:

• glaip-sdk: user-facing SDK and CLI.

• aip-agents: local execution engine used by both local runs and the platform.

• ai-agent-platform: remote execution and management.

Setup:

• Local mode: configure LLM provider credentials (for example OPENAI_API_KEY).

• Remote mode: configure platform credentials (CLI: aip accounts add/use; Python SDK: AIP_API_URL and AIP_API_KEY).

Notes on Execution Modes

• Local mode: runs in your Python process (fast iteration).

• Remote mode: runs on the platform (centralized execution, shared agents).

• The CLI uses the Python SDK under the hood for most operations.

• REST API documentation is kept as reference for internal integrations.

Start Building

Ready to go from prototype to production? Follow this path to ship quickly:

1. Install & configure — Set up credentials and the CLI with Install & Configure.

2. Run the quick start — Use the Agent-first pattern (recommended). Treat Client workflows as legacy/advanced admin paths in the Quick Start Guide.

3. Explore patterns — Use the Hands-on Examples to pick the right pattern (single agent, multi-agent, class pattern, runtime config, local execution, report automation).

4. Iterate on prompts — Use the CLI export/import loop in Configuration management to refine instructions safely.

5. Add real workflows — Explore Tools, File processing, or Multi-agent patterns as you expand capabilities.

The GL AIP package, SDK, and CLI give you a single, consistent toolkit to build, test, and operate AI agents anywhere.