aip-agents-binary 0.5.25b1

A library for managing agents in Gen AI applications.

AIP Agents

Description

The core agent library for local execution in GL AIP (GDP Labs AI Agents Package). aip-agents is part of the GL AIP ecosystem:

• aip-agents (this library) — The underlying agent library for local execution
• ai-agent-platform — The platform that provides remote server/run capabilities (uses aip-agents internally)
• glaip-sdk — The SDK that end users use to run agents either locally (directly via aip-agents) or on the remote server (via ai-agent-platform)

You can use aip-agents directly for local execution, or let glaip-sdk manage local vs remote mode for you. See the GL AIP overview for the full architecture.

Installation

Prerequisites

• Python 3.11 - 3.12 - Install here
• Pip (if using Pip) - Install here
• uv (if using uv) - Install here
• Git (if using Git) - Install here
• For git installation:
  • Access to the GDP Labs SDK repository

1. Installation from the GDP Labs registry

This package is published to the internal GDP Labs registry. Ensure your uv/pip config includes the registry: https://glsdk.gdplabs.id/gen-ai-internal/simple/.

Using pip

pip install aip-agents

Using uv

uv pip install aip-agents

2. Development Installation (Git)

For development purposes, you can install directly from the Git repository:

uv pip install "git+ssh://[e-mail]/GDP-ADMIN/glaip-sdk.git#subdirectory=python/aip-agents"

3. Recommended: install via glaip-sdk for local mode

If you want local + remote compatibility, install the SDK's local extra instead:

pip install "glaip-sdk[local]"

Optional Extras

pip install "aip-agents[memory]"
pip install "aip-agents[privacy]"

Managing Dependencies

1. Go to the aip-agents module root, e.g. cd python/aip-agents.
2. Run uv sync --extra dev to install the aip-agents requirements.
3. Run uv lock if you change any dependency versions in pyproject.toml.

Contributing

Please refer to this Python Style Guide to get information about code style, documentation standard, and SCA that you need to use when contributing to this project

1. Activate pre-commit hooks using pre-commit install
2. Run uv sync --extra dev to install the aip-agents requirements.
3. Run which python to get the path to be referenced at Visual Studio Code interpreter path (Ctrl+Shift+P or Cmd+Shift+P)
4. Try running the unit test to see if it's working:

uv run pytest -s tests/unit_tests/

Hello World Examples

Prerequisites

• Python 3.11 - 3.12
• Install the package:

pip install aip-agents

• For OpenAI: Set your API key in the environment:

export OPENAI_API_KEY=your-openai-key

• For Google ADK: Set your API key in the environment:

export GOOGLE_API_KEY=your-google-api-key

Run the Hello World Examples

The example scripts are located in aip_agents/examples in the source repo. You can run them individually or use the run_all_examples.py script.

1. Running Individual Examples:

Navigate to the library's root directory (e.g., python/aip-agents if you cloned the repository).

LangGraph (OpenAI):

python aip_agents/examples/hello_world_langgraph.py

LangGraph with GL Connectors (OpenAI):

python aip_agents/examples/hello_world_langgraph_gl_connector_twitter.py

LangGraph Streaming (OpenAI):

python aip_agents/examples/hello_world_langgraph_stream.py

LangGraph Multi-Agent Coordinator (OpenAI):

python aip_agents/examples/hello_world_a2a_multi_agent_coordinator_server.py

Google ADK:

python aip_agents/examples/hello_world_google_adk.py

Google ADK Streaming:

python aip_agents/examples/hello_world_google_adk_stream.py

LangChain (OpenAI):

python aip_agents/examples/hello_world_langchain.py

LangChain Streaming (OpenAI):

python aip_agents/examples/hello_world_langchain_stream.py

HITL (Human-in-the-Loop) Approval Demo:

python aip_agents/examples/hitl_demo.py

2. Running MCP Examples

Prerequisites

Ensure you have set the environment variables for API keys:

export OPENAI_API_KEY="your-openai-key"
export GOOGLE_API_KEY="your-google-api-key"

For examples that use stateful MCP tools like browser automation, start the Playwright MCP server in a separate terminal:

npx @playwright/mcp@latest --headless --port 8931

Note: Use the --headless flag to run the server without a visible browser window, which is recommended if the browser is not installed yet to avoid failures. For using an actual (non-headless) browser, refer to the Playwright MCP documentation.

Local MCP Servers

For STDIO, SSE, and HTTP transports using local servers, open a terminal in the library root (python/aip-agents) and run:

• For STDIO:

uv run python aip_agents/examples/mcp_servers/mcp_server_stdio.py

• For SSE:

uv run python aip_agents/examples/mcp_servers/mcp_server_sse.py

• For HTTP:

uv run python aip_agents/examples/mcp_servers/mcp_server_http.py

Note: Start the appropriate server before running the client examples for that transport.

Running Examples

All examples are run from the library root using uv run python aip_agents/examples/<file>.py. Examples support OpenAI for LangGraph/LangChain and Google ADK where specified.

LangChain Examples

STDIO Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_langchain_mcp_stdio.py

• Streaming:

uv run python aip_agents/examples/hello_world_langchain_mcp_stdio_stream.py

SSE Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_langchain_mcp_sse.py

• Streaming:

uv run python aip_agents/examples/hello_world_langchain_mcp_sse_stream.py

HTTP Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_langchain_mcp_http.py

• Streaming:

uv run python aip_agents/examples/hello_world_langchain_mcp_http_stream.py

Google ADK Examples

STDIO Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_google_adk_mcp_stdio.py

• Streaming:

uv run python aip_agents/examples/hello_world_google_adk_mcp_stdio_stream.py

SSE Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_google_adk_mcp_sse.py

• Streaming:

uv run python aip_agents/examples/hello_world_google_adk_mcp_sse_stream.py

HTTP Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_google_adk_mcp_http.py

• Streaming:

uv run python aip_agents/examples/hello_world_google_adk_mcp_http_stream.py

LangGraph Examples (OpenAI)

STDIO Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_langgraph_mcp_stdio.py

• Streaming:

uv run python aip_agents/examples/hello_world_langgraph_mcp_stdio_stream.py

SSE Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_langgraph_mcp_sse.py

• Streaming:

uv run python aip_agents/examples/hello_world_langgraph_mcp_sse_stream.py

HTTP Transport

• Non-Streaming:

uv run python aip_agents/examples/hello_world_langgraph_mcp_http.py

• Streaming:

uv run python aip_agents/examples/hello_world_langgraph_mcp_http_stream.py

Multi-Server Example

This LangChain example uses multiple MCP servers: Playwright (for browser actions) and a random name generator (SSE transport) with persistent sessions across multiple arun calls.

1. Start the Playwright server:

npx @playwright/mcp@latest --headless --port 8931

2. In another terminal, start the Name Generator SSE server:

uv run python aip_agents/examples/mcp_servers/mcp_name.py

3. Run the multi-server client example:

uv run python aip_agents/examples/hello_world_langchain_mcp_multi_server.py

3. Running Individual A2A Examples:

• Navigate to the library's root directory (e.g., libs/aip-agents if you cloned the repository).
• Open a new terminal and navigate to the aip_agents/examples directory to run the A2A server.

LangChain Server:

python hello_world_a2a_langchain_server.py

• Open a new terminal and navigate to the aip_agents/examples directory to run the A2A client.

LangChain Client:

python hello_world_a2a_langchain_client.py

LangChain Client Integrated with Agent Workflow:

python hello_world_a2a_langchain_client_agent.py

LangChain Client Streaming:

python hello_world_a2a_langchain_client_stream.py

Human-in-the-Loop (HITL) Approval

AIP Agents supports Human-in-the-Loop approval for tool execution, allowing human operators to review and approve high-risk tool calls before they execute.

Features

• Configurable Approval Policies: Set approval requirements per tool with customizable timeouts and behaviors
• Interactive CLI Prompts: Clear, structured prompts showing tool details and context
• Structured Logging: All approval decisions are logged with full metadata
• Timeout Handling: Configurable behavior when approval requests time out
• Non-blocking: Tools without HITL configuration execute normally

Quick Start

Configure HITL for specific tools in your agent:

from aip_agents.agent import LangGraphReactAgent
from aip_agents.agent.hitl.config import ToolApprovalConfig

# Create agent with tools
agent = LangGraphReactAgent(
    name="My Agent",
    tools=[send_email_tool, search_tool],
)

# Configure HITL via tool_configs
agent.tool_configs = {
    "tool_configs": {
        "send_email": {"hitl": {"timeout_seconds": 300}}
    }
}

When the agent attempts to use the send_email tool, it will:

1. Emit a pending approval event via DeferredPromptHandler
2. Wait for ApprovalManager.resolve_pending_request() to be called
3. Execute the tool only if approved
4. Log the decision for audit purposes

Configuration Options

Option	Type	Default	Description
timeout_seconds	int	300	Seconds to wait for operator input

Logging

All HITL decisions are logged with structured data:

{
  "event": "hitl_decision",
  "tool": "send_email",
  "decision": "approved",
  "operator_input": "A",
  "latency_ms": 2500,
  "timestamp": "2025-09-25T10:15:00Z"
}

Demo

Run the interactive demo to see HITL in action:

python aip_agents/examples/hitl_demo.py

Architectural Notes

Memory Features

The library supports Mem0 as a memory backend for long-term conversation recall. Key features:

• Automatic persistence of user-agent interactions via memory_backend="mem0".
• Semantic search for relevant past conversations.
• New built_in_mem0_search tool for explicit recall by time period (e.g., "yesterday", "last week", "July 2025").
• Date range parsing for natural language time filters using dateparser.
• Conditional auto-augmentation (disabled by default to reduce noise; enable with memory_auto_augment=True).

Mem0 Date Recall Example

Use the coordinator example with memory enabled:

uv run python aip_agents/examples/hello_world_a2a_mem0_coordinator_server.py

In client:

agent = LangGraphAgent(
    name="client",
    instruction="...",
    model="gpt-4o-mini",
    memory_backend="mem0",
)

Test recall: After some interactions, query "What did we discuss yesterday?" – agent uses tool to filter by created_at.

Deep Agents Middleware

The Deep Agents Middleware system provides composable components for enhancing agent capabilities with planning, context management, and custom lifecycle hooks.

Quick Start

Enable deep agent capabilities with a single parameter:

from aip_agents.agent.langgraph_react_agent import LangGraphReactAgent

# Enable planning + filesystem for complex multi-step tasks
agent = LangGraphReactAgent(
    name="research_agent",
    model="gpt-4",
    planning=True,  # Enables TodoListMiddleware for task decomposition
    tools=[search_tool, calculator_tool],
)

Understanding Planning vs Filesystem

Important: planning and filesystem are completely independent features:

• planning=True

  • Adds write_todos tool for task decomposition
  • Stores todos in in-memory dictionary (per thread_id)
  • Does NOT use or require filesystem
  • Perfect for breaking down complex tasks into steps
  • Example: "Research quantum computing" → agent creates 5 subtasks

• filesystem=True

  • Adds file operation tools: ls, read_file, write_file, edit_file, grep
  • Stores data in pluggable backend (default: InMemoryBackend)
  • Does NOT interact with planning/todos
  • Perfect for offloading large tool results to prevent context overflow
  • Example: Web search returns 50KB → agent writes to /research/results.txt

• Both together (planning=True, filesystem=True)

  • Agent can plan tasks AND manage large data
  • Todos stored separately in memory, files in backend
  • Most powerful combination for complex research/analysis tasks

Planning Only

For task decomposition without filesystem:

agent = LangGraphReactAgent(
    name="planner_agent",
    model="gpt-4",
    planning=True,
    tools=[...],
)

Filesystem Only

For context offloading without planning:

agent = LangGraphReactAgent(
    name="data_processor",
    model="gpt-4",
    filesystem=True,  # Enables FilesystemMiddleware
    tools=[...],
)

Custom Middleware

Create domain-specific middleware by implementing the AgentMiddleware protocol:

from aip_agents.middleware.base import AgentMiddleware, ModelRequest

class CustomMiddleware:
    def __init__(self):
        self.tools = []  # Add custom tools here
        self.system_prompt_additions = "Custom instructions..."

    def before_model(self, state: dict) -> dict:
        # Hook executed before model invocation
        return {}

    def modify_model_request(self, request: ModelRequest, state: dict) -> ModelRequest:
        # Modify the model request (add tools, adjust params, etc.)
        return request

    def after_model(self, state: dict) -> dict:
        # Hook executed after model invocation
        return {}

# COMPOSITION (not override): Custom middlewares EXTEND built-in middleware
agent = LangGraphReactAgent(
    name="custom_agent",
    model="gpt-4",
    planning=True,        # Adds TodoListMiddleware
    filesystem=True,      # Adds FilesystemMiddleware
    middlewares=[CustomMiddleware()],  # EXTENDS (doesn't replace) the above
    tools=[...],
)
# Result: Agent has ALL THREE middleware active:
#   1. TodoListMiddleware (from planning=True)
#   2. FilesystemMiddleware (from filesystem=True)
#   3. CustomMiddleware (from middlewares parameter)

Key Points:

• ✅ middlewares parameter extends (never replaces) auto-configured middleware
• ✅ planning and filesystem are independent - use either, both, or neither
• ✅ planning=True stores todos in memory (does NOT require filesystem)
• ✅ Execution order: built-in middleware (planning, filesystem) → custom middlewares
• ✅ All hooks from all middleware execute in sequence

Common Combinations:

# Planning only (no filesystem)
# → Todos stored in memory, no file operations available
agent = LangGraphReactAgent(planning=True)
# → [TodoListMiddleware]

# Filesystem only (no planning)
# → File operations available, no todo planning
agent = LangGraphReactAgent(filesystem=True)
# → [FilesystemMiddleware]

# Both planning and filesystem
# → Todos in memory + file operations (most powerful combination)
agent = LangGraphReactAgent(planning=True, filesystem=True)
# → [TodoListMiddleware, FilesystemMiddleware]

# Custom only (no auto-configuration)
agent = LangGraphReactAgent(middlewares=[CustomMiddleware()])
# → [CustomMiddleware]

# All together (composition)
agent = LangGraphReactAgent(
    planning=True,
    filesystem=True,
    middlewares=[CustomMiddleware()]
)
# → [TodoListMiddleware, FilesystemMiddleware, CustomMiddleware]

Advanced: Custom Storage Backend

Provide your own storage backend for filesystem operations:

from aip_agents.middleware.backends.protocol import BackendProtocol
from aip_agents.middleware.backends.memory import InMemoryBackend

# Use custom backend (e.g., PostgreSQL, S3, Redis)
custom_backend = MyCustomBackend()

agent = LangGraphReactAgent(
    name="agent",
    model="gpt-4",
    filesystem=custom_backend,  # Pass BackendProtocol instance
    tools=[...],
)

Benefits

• Context Window Management: Automatically offload large tool results to files
• Task Decomposition: Break down complex multi-step tasks into trackable todos
• Incremental Development: Add capabilities gradually (filesystem first, then planning)
• Zero Breaking Changes: Existing agents work unchanged (backward compatible)
• Extensible: Compose custom middleware with built-in components

For detailed documentation, see docs/deep_agents_guide.md (coming soon).

Agent Interface (AgentInterface)

The aip_agents.agent.interface.AgentInterface class defines a standardized contract for all agent implementations within the AIP Agents ecosystem. It ensures that different agent types (e.g., LangGraph-based, Google ADK-based) expose a consistent set of methods for core operations.

Key methods defined by AgentInterface typically include:

• arun(): For asynchronous execution of the agent that returns a final consolidated response.
• arun_stream(): For asynchronous execution that streams back partial responses or events from the agent.

By adhering to this interface, users can interact with various agents in a uniform way, making it easier to switch between or combine different agent technologies.

Inversion of Control (IoC) / Dependency Injection (DI)

The agent implementations (e.g., LangGraphAgent, GoogleADKAgent) utilize Dependency Injection. For instance, LangGraphAgent accepts an agent_executor (like one created by LangGraph's create_react_agent) in its constructor. Similarly, GoogleADKAgent accepts a native adk_native_agent. This allows the core execution logic to be provided externally, promoting flexibility and decoupling the agent wrapper from the specific instantiation details of its underlying engine.