AI-Parrot

AI-Parrot is an async-first Python framework for building, extending, and orchestrating AI Agents and Chatbots. Built on top of navigator-api, it provides a unified interface for interacting with various LLM providers, managing tools, conducting agent-to-agent (A2A) communication, and serving agents via the Model Context Protocol (MCP).

Whether you need a simple chatbot, a complex multi-agent orchestration workflow, or a robust production-ready AI service, AI-Parrot exposes the primitives to build it efficiently.

Monorepo Structure

AI-Parrot is organized as a monorepo with four packages:

Package	PyPI Name	Description
`packages/ai-parrot`	`ai-parrot`	Core framework: agents, clients, memory, orchestration
`packages/ai-parrot-tools`	`ai-parrot-tools`	Tool and toolkit implementations (Jira, AWS, Slack, etc.)
`packages/ai-parrot-loaders`	`ai-parrot-loaders`	Document loaders for RAG pipelines (PDF, YouTube, audio, etc.)
`packages/ai-parrot-pipelines`	`ai-parrot-pipelines`	Specialized pipelines such as planogram compliance workflows

The core package (ai-parrot) provides the base abstractions (AbstractTool, AbstractToolkit, @tool) and lightweight built-in tools. Heavy tool implementations, document loaders, and specialized pipelines are split into their own packages so you only install what you need.

Installation

Core framework

uv pip install ai-parrot

Quick Setup (CLI)

After installing, use the parrot CLI to configure your environment interactively:

# Interactive setup wizard — select LLM provider, enter API keys, generate .env
parrot setup

# Initialize configuration directory structure (env/ and etc/)
parrot conf init

The parrot setup wizard will guide you through:

Selecting an LLM provider (OpenAI, Anthropic, Google, etc.)
Entering your API credentials
Writing them to the correct .env file
Optionally creating a starter Agent and bootstrap files (app.py, run.py)

Additional CLI commands:

# Start an MCP server from a YAML config
parrot mcp --config server.yaml

# Deploy an autonomous agent as a systemd service
parrot autonomous create --agent my_agent.py
parrot autonomous install --agent my_agent.py --name my-agent

LLM Providers

Install only the providers you need:

# Google Gemini
uv pip install "ai-parrot[google]"

# OpenAI / GPT
uv pip install "ai-parrot[openai]"

# Anthropic / Claude
uv pip install "ai-parrot[anthropic]"

# Groq
uv pip install "ai-parrot[groq]"

# X.AI / Grok
uv pip install "ai-parrot[xai]"

# All LLM providers at once
uv pip install "ai-parrot[llms]"

Additional providers supported out of the box (no extra install needed):

HuggingFace (hf) — uses the HuggingFace Inference API
vLLM (vllm) — connects to a local vLLM server
OpenRouter (openrouter) — routes to any model via OpenRouter API
Ollama / Local — via OpenAI-compatible endpoints

Embeddings & Vector Stores

# Sentence transformers, FAISS, ChromaDB, etc.
uv pip install "ai-parrot[embeddings]"

Tools

# Install the tools package
uv pip install ai-parrot-tools

# Or with specific tool extras
uv pip install "ai-parrot-tools[jira]"
uv pip install "ai-parrot-tools[aws]"
uv pip install "ai-parrot-tools[slack]"
uv pip install "ai-parrot-tools[finance]"
uv pip install "ai-parrot-tools[all]"       # All tool dependencies

Available tool extras: jira, slack, aws, docker, git, analysis, excel, sandbox, codeinterpreter, pulumi, sitesearch, office365, scraping, finance, db, flowtask, google, arxiv, wikipedia, weather, messaging.

Document Loaders

# Install the loaders package
uv pip install ai-parrot-loaders

# Or with specific loader extras
uv pip install "ai-parrot-loaders[youtube]"
uv pip install "ai-parrot-loaders[pdf]"
uv pip install "ai-parrot-loaders[audio]"
uv pip install "ai-parrot-loaders[all]"     # All loader dependencies

Available loader extras: youtube, audio, pdf, web, ebook, video.

Pipelines

# Install the pipelines package
uv pip install ai-parrot-pipelines

Backward-compatible imports from parrot.pipelines continue to work when the package is installed.

Platform & Security Tools

AI-Parrot includes tools for cloud security auditing and infrastructure management. These tools rely on external Docker images that must be installed before use:

# Security tools
parrot install cloudsploit    # AWS security scanner (CloudSploit)
parrot install prowler        # Cloud security posture management

# Platform tools
parrot install pulumi         # Infrastructure as Code CLI

The parrot install command pulls and configures the required Docker containers automatically, so the tools are ready to be used by your agents.

Quick Start

Create a simple weather chatbot in just a few lines of code:

import asyncio
from parrot.bots import Chatbot
from parrot.tools import tool

# 1. Define a tool
@tool
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is Sunny, 25C"

async def main():
    # 2. Create the Agent
    bot = Chatbot(
        name="WeatherBot",
        llm="openai:gpt-4o",  # Provider:Model
        tools=[get_weather],
        system_prompt="You are a helpful weather assistant."
    )

    # 3. Configure (loads tools, connects to memory)
    await bot.configure()

    # 4. Chat!
    response = await bot.ask("What's the weather like in Madrid?")
    print(response)

if __name__ == "__main__":
    asyncio.run(main())

Using LLM Clients Directly

Beyond the Chatbot abstraction, you can access any LLM provider client directly for lower-level operations like image generation, embeddings, or custom completion calls:

import asyncio
from parrot.clients.google.client import GoogleGenAIClient
from parrot.models.outputs import ImageGenerationPrompt
from parrot.models.google import GoogleModel

async def main():
    prompt = ImageGenerationPrompt(
        prompt="A realistic passport-style photo with white background",
        styles=["photorealistic", "high resolution"],
        model=GoogleModel.IMAGEN_3.value,
        aspect_ratio="16:9",
    )

    client = GoogleGenAIClient()
    async with client:
        response = await client.image_generation(prompt_data=prompt)
        for img_path in response.images:
            print(f"Image saved to: {img_path}")

if __name__ == "__main__":
    asyncio.run(main())

Each provider client (GoogleGenAIClient, OpenAIClient, AnthropicClient, etc.) implements AbstractClient and can be used as an async context manager. This gives you full access to provider-specific features — image generation, audio transcription, structured outputs — while still benefiting from AI-Parrot's unified configuration and credential management.

Running as a Server

AI-Parrot is not only a library — it is also a full aiohttp-based application server that exposes your agents as REST APIs, WebSocket endpoints, and more. This is powered by Navigator, an async web framework built on aiohttp.

How it works

When you run parrot setup, it generates two files:

app.py — Defines your application handler, registers agents with BotManager, and configures routes.
run.py — The entry point that starts the aiohttp server.

app.py (generated by parrot setup):

from parrot.manager import BotManager
from parrot.conf import STATIC_DIR
from parrot.handlers import AppHandler
from agents.my_agent import MyAgent


class Main(AppHandler):
    app_name: str = "Parrot"
    enable_static: bool = True
    staticdir: str = STATIC_DIR

    def configure(self) -> None:
        self.bot_manager = BotManager()
        self.bot_manager.register(MyAgent())
        self.bot_manager.setup(self.app)

run.py (generated by parrot setup):

from navigator import Application
from app import Main

app = Application(Main, enable_jinja2=True)

if __name__ == "__main__":
    app.run()

Built-in endpoints

Once the server starts, BotManager.setup() automatically registers these routes:

Endpoint	Method	Description
`/api/v1/agents/chat/{agent_id}`	POST	Chat with an agent (JSON, HTML, or Markdown response)
`/api/v1/agents/chat/{agent_id}`	PATCH	Configure tools/MCP servers for a session
`/api/v1/bot_management`	GET	List registered bots
`/api/v1/bot_management/{bot}`	GET/POST/PATCH/DELETE	CRUD operations on bots
`/api/v1/agent_tools`	GET	List available tools
`/api/v1/ai/client`	GET	LLM provider configuration
`/ws/userinfo`	WebSocket	Real-time user notifications

Starting the server

Development (single process, auto-reload):

python run.py

The server starts on http://0.0.0.0:5000 by default (configurable via APP_HOST / APP_PORT environment variables).

Production (Gunicorn with async workers):

# Install gunicorn
uv pip install "ai-parrot[deploy]"

# Run with aiohttp-compatible workers
gunicorn run:app \
    --worker-class aiohttp.worker.GunicornUVLoopWebWorker \
    --workers 4 \
    --bind 0.0.0.0:5000 \
    --timeout 360

The long timeout (360s) accommodates agent queries that involve multi-step tool execution or LLM calls.

Talking to your agents via REST

Once the server is running, any registered agent is accessible via HTTP:

# Chat with an agent
curl -X POST http://localhost:5000/api/v1/agents/chat/my-agent \
  -H "Content-Type: application/json" \
  -d '{"message": "What is the weather in Madrid?"}'

# Request markdown output
curl -X POST "http://localhost:5000/api/v1/agents/chat/my-agent?output_format=markdown" \
  -H "Content-Type: application/json" \
  -d '{"message": "Summarize the latest news"}'

Architecture

AI-Parrot is designed with a modular architecture enabling agents to be both consumers and providers of tools and services.

graph TD
    User["User / Client"] --> API["AgentTalk Handlers"]
    API --> Bot["Chatbot / BaseBot"]

    subgraph "Agent Core"
        Bot --> Memory["Memory / Vector Store"]
        Bot --> LLM["LLM Client (OpenAI/Anthropic/Etc)"]
        Bot --> TM["Tool Manager"]
    end

    subgraph "Tools & Capabilities"
        TM --> LocalTools["Local Tools (@tool)"]
        TM --> Toolkits["Toolkits (OpenAPI/Custom)"]
        TM --> MCPServer["External MCP Servers"]
    end

    subgraph "Connectivity"
        Bot -.-> A2A["A2A Protocol (Client/Server)"]
        Bot -.-> MCP["MCP Protocol (Server)"]
        Bot -.-> Integrations["Telegram / MS Teams"]
    end

    subgraph "Orchestration"
        Crew["AgentCrew"] --> Bot
        Crew --> OtherBots["Other Agents"]
    end

Core Concepts

Agents (`Chatbot`)

The Chatbot class is your main entry point. It handles conversation history, RAG (Retrieval-Augmented Generation), and the tool execution loop.

bot = Chatbot(
    name="MyAgent",
    model="anthropic:claude-3-5-sonnet-20240620",
    enable_memory=True
)

Tools

Functional Tools (`@tool`)

The simplest way to create a tool. The docstring and type hints are automatically used to generate the schema for the LLM.

from parrot.tools import tool

@tool
def calculate_vat(amount: float, rate: float = 0.20) -> float:
    """Calculate VAT for a given amount."""
    return amount * rate

Class-Based Toolkits (`AbstractToolkit`)

Group related tools into a reusable class. All public async methods become tools.

from parrot.tools import AbstractToolkit

class MathToolkit(AbstractToolkit):
    async def add(self, a: int, b: int) -> int:
        """Add two numbers."""
        return a + b

    async def multiply(self, a: int, b: int) -> int:
        """Multiply two numbers."""
        return a * b

OpenAPI Toolkit (`OpenAPIToolkit`)

Dynamically generate tools from any OpenAPI/Swagger specification.

from parrot.tools import OpenAPIToolkit

petstore = OpenAPIToolkit(
    spec="https://petstore.swagger.io/v2/swagger.json",
    service="petstore"
)

# Now your agent can call petstore_get_pet_by_id, etc.
bot = Chatbot(name="PetBot", tools=petstore.get_tools())

Orchestration (`AgentCrew`)

Orchestrate multiple agents to solve complex tasks using AgentCrew.

Supported Modes:

Sequential: Agents run one after another, passing context.
Parallel: Independent tasks run concurrently.
Flow: DAG-based execution defined by dependencies.
Loop: Iterative execution until a condition is met.

from parrot.bots.orchestration import AgentCrew

crew = AgentCrew(
    name="ResearchTeam",
    agents=[researcher_agent, writer_agent]
)

# Define a Flow — Writer waits for Researcher to finish
crew.task_flow(researcher_agent, writer_agent)

await crew.run_flow("Research the latest advancements in Quantum Computing")

Scheduling (`@schedule`)

Give your agents agency to run tasks in the background.

from parrot.scheduler import schedule, ScheduleType

class DailyBot(Chatbot):
    @schedule(schedule_type=ScheduleType.DAILY, hour=9, minute=0)
    async def morning_briefing(self):
        news = await self.ask("Summarize today's top tech news")
        await self.send_notification(news)

Connectivity & Exposure

Agent-to-Agent (A2A) Protocol

Agents can discover and talk to each other using the A2A protocol.

Expose an Agent:

from parrot.a2a import A2AServer

a2a = A2AServer(my_agent)
a2a.setup(app, url="https://my-agent.com")

Consume an Agent:

from parrot.a2a import A2AClient

async with A2AClient("https://remote-agent.com") as client:
    response = await client.send_message("Hello from another agent!")

Model Context Protocol (MCP)

AI-Parrot has first-class support for MCP.

Consume MCP Servers:

mcp_servers = [
    MCPServerConfig(
        name="filesystem",
        command="npx",
        args=["-y", "@modelcontextprotocol/server-filesystem", "/home/user"]
    )
]
await bot.setup_mcp_servers(mcp_servers)

Expose Agent as MCP Server: Allow Claude Desktop or other MCP clients to use your agent as a tool.

Platform Integrations

Expose your bots natively to chat platforms:

Telegram
Microsoft Teams
Slack
WhatsApp

Supported LLM Providers

Provider	Extra	Identifier	Example
OpenAI	`openai`	`openai`	`openai:gpt-4o`
Anthropic	`anthropic`	`anthropic`, `claude`	`anthropic:claude-sonnet-4-20250514`
Google Gemini	`google`	`google`	`google:gemini-2.0-flash`
Groq	`groq`	`groq`	`groq:llama-3.3-70b-versatile`
X.AI / Grok	`xai`	`grok`	`grok:grok-3`
HuggingFace	(included)	`hf`	`hf:meta-llama/Llama-3-8B`
vLLM	(included)	`vllm`	`vllm:model-name`
OpenRouter	(included)	`openrouter`	`openrouter:anthropic/claude-sonnet-4`
Ollama	(included)	via OpenAI endpoint	—

Contributing

Development setup (from source)

AI-Parrot uses uv as its package manager and provides a Makefile to simplify common tasks.

git clone https://github.com/phenobarbital/ai-parrot.git
cd ai-parrot

# Create the virtual environment (Python 3.11)
make venv
source .venv/bin/activate

# Full dev install — all packages, all extras, dev tools
make develop

# Run tests
make test

Makefile targets

The Makefile covers the entire development lifecycle. Run make help for the full list.

Development install variants:

Target	What it installs
`make develop`	All packages + all extras + dev tools (full environment)
`make develop-fast`	All packages, base deps only (no torch/tensorflow/whisperx)
`make develop-ml`	Embeddings + audio loaders (heavy ML stack)

Production install variants:

Target	What it installs
`make install`	All packages, base deps only (no extras)
`make install-core`	Core with LLM clients + vector stores
`make install-tools`	Core + tools with common extras (jira, slack, aws, etc.)
`make install-tools-all`	Core + tools with ALL extras
`make install-loaders`	Core + loaders with common extras (youtube, web, pdf)
`make install-loaders-all`	Core + loaders with ALL extras (includes whisperx, pyannote)
`make install-all`	Everything with ALL extras

Other useful targets:

make format          # Format code with black
make lint            # Lint with pylint + black --check
make test            # Run pytest + mypy
make build           # Build all packages (sdist + wheel)
make release         # Build + publish to PyPI
make lock            # Regenerate uv.lock
make clean           # Remove build artifacts
make generate-registry  # Regenerate TOOL_REGISTRY from source
make bump-patch      # Bump patch version (syncs across all packages)

Manual install (without Make)

If you prefer not to use Make:

uv venv --python 3.11 .venv
source .venv/bin/activate

# Full install
uv sync --all-packages --all-extras

# Or selective extras
uv sync --extra google --extra openai

Project layout

ai-parrot/
├── packages/
│   ├── ai-parrot/           # Core framework
│   │   └── src/parrot/
│   ├── ai-parrot-tools/     # Tool implementations
│   │   └── src/parrot_tools/
│   └── ai-parrot-loaders/   # Document loaders
│       └── src/parrot_loaders/
├── tests/
├── examples/
├── Makefile                  # Build, install, test, release shortcuts
└── pyproject.toml            # Workspace root

Releasing to PyPI

AI-Parrot publishes three packages on every GitHub release:

Package	PyPI Project	Build Method
`ai-parrot`	ai-parrot	cibuildwheel (Cython + Rust/Maturin)
`ai-parrot-tools`	ai-parrot-tools	uv build (pure Python)
`ai-parrot-loaders`	ai-parrot-loaders	uv build (pure Python)

The release workflow (.github/workflows/release.yml) runs 3 parallel build jobs and a single deploy job:

release event
    ├── build-core   — cibuildwheel for ai-parrot (Cython + Rust)
    ├── build-tools  — uv build for ai-parrot-tools
    ├── build-loaders — uv build for ai-parrot-loaders
    └── deploy       — twine upload all artifacts to PyPI

To create a release:

Bump the version in each package's pyproject.toml (or use make bump-patch to sync all three).
Create a GitHub release — the workflow triggers automatically on the release: created event.

First-time PyPI setup (required once):

Create ai-parrot-tools and ai-parrot-loaders projects on PyPI under the same account as ai-parrot.
Ensure the NAV_AIPARROT_API_SECRET GitHub secret holds a PyPI API token with upload scope for all 3 projects. A scoped token per project or a single account-level token both work.

Independent versioning:

Each package has its own version number in its pyproject.toml. All three are built and published on the same release event — there is no requirement to keep versions in sync.

Guidelines

All code must be async-first — no blocking I/O in async contexts
Use type hints and Google-style docstrings on all public APIs
Use Pydantic models for structured data
Run pytest after any logic change
Tools with heavy dependencies must use lazy imports to avoid bloating the core

Issues & Support

Issues: GitHub Tracker
Discussion: GitHub Discussions

License

MIT

Built with care by the AI-Parrot Team

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AI-Parrot

Monorepo Structure

Installation

Core framework

Quick Setup (CLI)

LLM Providers

Embeddings & Vector Stores

Tools

Document Loaders

Pipelines

Platform & Security Tools

Quick Start

Using LLM Clients Directly

Running as a Server

How it works

Built-in endpoints

Starting the server

Talking to your agents via REST

Architecture

Core Concepts

Agents (Chatbot)

Tools

Functional Tools (@tool)

Class-Based Toolkits (AbstractToolkit)

OpenAPI Toolkit (OpenAPIToolkit)

Orchestration (AgentCrew)

Scheduling (@schedule)

Connectivity & Exposure

Agent-to-Agent (A2A) Protocol

Model Context Protocol (MCP)

Platform Integrations

Supported LLM Providers

Contributing

Development setup (from source)

Makefile targets

Manual install (without Make)

Project layout

Releasing to PyPI

Guidelines

Issues & Support

License

Agents (`Chatbot`)

Functional Tools (`@tool`)

Class-Based Toolkits (`AbstractToolkit`)

OpenAPI Toolkit (`OpenAPIToolkit`)

Orchestration (`AgentCrew`)

Scheduling (`@schedule`)