flock-core 0.4.0b36

Declarative LLM Orchestration at Scale

🐤 Flock 0.4.0 currently in beta - use pip install flock-core==0.4.0b5 🐤

🐤 pip install flock-core will install the latest non-beta version 🐤

🐤 Expected Release for 0.4.0 Magpie: End of April 2025 🐤

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Tired of wrestling with paragraphs of prompt text just to get your AI agent to perform a specific, structured task? 😫

Enter Flock, the agent framework that lets you ditch the prompt-palaver and focus on what you want your agents to achieve through a declarative approach. Define your agent's inputs, outputs, and available tools using clear Python structures (including type hints!), and let Flock handle the complex LLM interactions and orchestration.

Built with real-world deployment in mind, Flock integrates seamlessly with tools like Temporal (optional) for building robust, fault-tolerant, and scalable agent systems right out of the box.

Looking for examples and tutorials? Check out the dedicated 👉 flock-showcase Repository!

✨ Why Join the Flock?

Flock offers a different way to build agentic systems:

Traditional Agent Frameworks 😟	Flock Framework 🐤🐧🐓🦆
🤯 Prompt Nightmare	✅ Declarative Simplicity
Long, brittle, hard-to-tune prompts	Clear input/output specs (typed!)
💥 Fragile & Unpredictable	⚡ Robust & Production-Ready
Single errors can halt everything	Fault-tolerant via Temporal option
🧩 Monolithic & Rigid	🔧 Modular & Flexible
Hard to extend or modify logic	Pluggable Evaluators, Modules, Tools
⛓️ Basic Chaining	🚀 Advanced Orchestration
Often just linear workflows	Dynamic Routing, Batch Processing
🧪 Difficult Testing	✅ Testable Components
Hard to unit test prompt logic	Clear I/O contracts aid testing
📄 Unstructured Output	✨ Structured Data Handling
Parsing unreliable LLM text output	Native Pydantic/Typed Dict support

📹 Video Demo

https://github.com/user-attachments/assets/bdab4786-d532-459f-806a-024727164dcc

💡 Core Concepts

Flock's power comes from a few key ideas (Learn more in the Full Documentation):

1. Declarative Agents: Define agents by what they do (inputs/outputs), not how. Flock uses Evaluators (like the default DeclarativeEvaluator powered by DSPy) to handle the underlying logic.
2. Typed Signatures: Specify agent inputs and outputs using Python type hints and optional descriptions (e.g., "query: str | User request, context: Optional[List[MyType]]").
3. Modular Components: Extend agent capabilities with pluggable Modules (e.g., for memory, metrics, output formatting) that hook into the agent's lifecycle.
4. Intelligent Workflows: Chain agents explicitly or use Routers (LLM-based, Agent-based, or custom) for dynamic decision-making.
5. Reliable Execution: Run locally for easy debugging or seamlessly switch to Temporal (optional) for production-grade fault tolerance, retries, and state management.
6. Tool Integration: Equip agents with standard or custom Python functions (@flock_tool) registered via the FlockRegistry.
7. Registry: A central place (@flock_component, @flock_type, @flock_tool) to register your custom classes, types, and functions, enabling robust serialization and dynamic loading.

💾 Installation - Use Flock in your project

Get started with the core Flock library:

# Using uv (recommended)
uv pip install flock-core

# Using pip
pip install flock-core

Extras: Install optional dependencies for specific features:

# Common tools (Tavily, Markdownify)
uv pip install flock-core[tools]

# All optional dependencies (including tools, docling, etc.)
uv pip install flock-core[all]

🔑 Installation - Develop Flock

git clone https://github.com/whiteducksoftware/flock.git
cd flock

# One-liner dev setup after cloning
pip install poethepoet && poe install

Additional provided poe tasks and commands:

poe install # Install the project
poe build # Build the project
poe docs # Serve the docs
poe format # Format the code
poe lint # Lint the code

🔑 Environment Setup

Flock uses environment variables (typically in a .env file) for configuration, especially API keys. Create a .env file in your project root:

# .env - Example

# --- LLM Provider API Keys (Required by most examples) ---
# Add keys for providers you use (OpenAI, Anthropic, Gemini, Azure, etc.)
# Refer to litellm docs (https://docs.litellm.ai/docs/providers) for names
OPENAI_API_KEY="your-openai-api-key"
# ANTHROPIC_API_KEY="your-anthropic-api-key"

# --- Tool-Specific Keys (Optional) ---
# TAVILY_API_KEY="your-tavily-search-key"
# GITHUB_PAT="your-github-personal-access-token"

# --- Default Flock Settings (Optional) ---
DEFAULT_MODEL="openai/gpt-4o" # Default LLM if agent doesn't specify

# --- Flock CLI Settings (Managed by `flock settings`) ---
# SHOW_SECRETS="False"
# VARS_PER_PAGE="20"

Be sure that the .env file is added to your .gitignore!

⚡ Quick Start Syntax

While detailed examples and tutorials now live in the flock-showcase repository, here's a minimal example to illustrate the core syntax:

from flock.core import Flock, FlockFactory

# 1. Create the main orchestrator
# Uses DEFAULT_MODEL from .env or defaults to "openai/gpt-4o" if not set
my_flock = Flock(name="SimpleFlock")

# 2. Declaratively define an agent using the Factory
# Input: a topic (string)
# Output: a title (string) and bullet points (list of strings)
brainstorm_agent = FlockFactory.create_default_agent(
    name="idea_generator",
    description="Generates titles and key points for a given topic.",
    input="topic: str | The subject to brainstorm about",
    output="catchy_title: str, key_points: list[str] | 3-5 main bullet points"
)

# 3. Add the agent to the Flock
my_flock.add_agent(brainstorm_agent)

# 4. Run the agent!
if __name__ == "__main__":
    input_data = {"topic": "The future of AI agents"}
    try:
        # The result is a Box object (dot-accessible dict)
        result = my_flock.run(start_agent="idea_generator", input=input_data)
        print(f"Generated Title: {result.catchy_title}")
        print("Key Points:")
        for point in result.key_points:
            print(f"- {point}")
    except Exception as e:
        print(f"An error occurred: {e}")
        print("Ensure your LLM API key (e.g., OPENAI_API_KEY) is set in your .env file!")

🐤 New in Flock 0.4.0 Magpie 🐤

Version 0.4.0 brings significant enhancements focused on usability, deployment, and robustness:

🚀 REST API - Deploy Flock Agents as REST API Endpoints

Easily deploy your Flock agents as scalable REST API endpoints. Interact with your agent workflows via standard HTTP requests.

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🖥️ Web UI - Test Flock Agents in the Browser

Test and interact with your Flock agents directly in your browser through an integrated web interface.

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⌨️ CLI Tool - Manage Flock Agents via the Command Line

Manage Flock configurations, run agents, and inspect results directly from your command line.

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💾 Enhanced Serialization - Share, Deploy, and Run Flock Agents by human readable yaml files

Define and share entire Flock configurations, including agents and components, using human-readable YAML files. Load flocks directly from these files for easy deployment and versioning.

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💾 New execution flows

Run Flock in batch mode to process multiple inputs at once or in evaluation mode to test agents with different inputs.

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⏱️ Robust Temporal Integration

Flock 0.4.0 introduces first-class support for Temporal.io, enabling you to build truly production-grade, reliable, and scalable agent workflows. Move beyond simple local execution and leverage Temporal's power for:

• Fault Tolerance: Workflows automatically resume from the last successful step after failures.
• Retries: Configure automatic retries for activities (like LLM calls or tool usage) with exponential backoff.
• Scalability: Distribute workflow and activity execution across multiple worker processes using Task Queues.
• Observability: Gain deep insights into workflow execution history via the Temporal UI.

Flock makes this easy with:

• Declarative Configuration: Define Temporal timeouts, retry policies, and task queues directly within your Flock and FlockAgent configurations (YAML or Python).
• Correct Patterns: Uses Temporal's recommended granular activity execution for better control and visibility.
• Clear Worker Separation: Provides guidance and flags for running dedicated Temporal workers, separating development convenience from production best practices.

Visit the Temporal Documentation for more information on how to use Temporal.

Or check out the Flock Showcase for a complete example of a Flock that uses Temporal or our docs for more information.

Here's an example of how to configure a Flock to use Temporal:

from flock.core import Flock, FlockFactory

from flock.workflow.temporal_config import (
    TemporalActivityConfig,
    TemporalRetryPolicyConfig,
    TemporalWorkflowConfig,
)

# Flock-scoped temporal config
flock = Flock(
    enable_temporal=True,
    temporal_config=TemporalWorkflowConfig(
        task_queue="flock-test-queue",
        workflow_execution_timeout=timedelta(minutes=10),
        default_activity_retry_policy=TemporalRetryPolicyConfig(
            maximum_attempts=2
        ),
    ),
)

# Agent-scoped temporal config
content_agent = FlockFactory.create_default_agent(
    name="content_agent",
    input="funny_title, funny_slide_headers",
    output="funny_slide_content",
    temporal_activity_config=TemporalActivityConfig(
        start_to_close_timeout=timedelta(minutes=1),
        retry_policy=TemporalRetryPolicyConfig(
            maximum_attempts=4,
            initial_interval=timedelta(seconds=2),
            non_retryable_error_types=["ValueError"],
        ),
    ),
)

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✨ Utility: @flockclass Hydrator

Flock also provides conveniences. The @flockclass decorator allows you to easily populate Pydantic models using an LLM:

from pydantic import BaseModel
from flock.util.hydrator import flockclass # Assuming hydrator utility exists
import asyncio

@flockclass(model="openai/gpt-4o") # Decorate your Pydantic model
class CharacterIdea(BaseModel):
    name: str
    char_class: str
    race: str
    backstory_hook: str | None = None # Field to be filled by hydrate
    personality_trait: str | None = None # Field to be filled by hydrate

async def create_character():
    # Create with minimal data
    char = CharacterIdea(name="Gorok", char_class="Barbarian", race="Orc")
    print(f"Before Hydration: {char}")

    # Call hydrate to fill in the None fields using the LLM
    hydrated_char = await char.hydrate()

    print(f"\nAfter Hydration: {hydrated_char}")
    print(f"Backstory Hook: {hydrated_char.backstory_hook}")

# asyncio.run(create_character())

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📚 Examples & Tutorials

For a comprehensive set of examples, ranging from basic usage to complex projects and advanced features, please visit our dedicated showcase repository:

➡️ github.com/whiteducksoftware/flock-showcase ⬅️

The showcase includes:

• Step-by-step guides for core concepts.
• Examples of tool usage, routing, memory, and more.
• Complete mini-projects demonstrating practical applications.

📖 Documentation

Full documentation, including API references and conceptual explanations, can be found at:

➡️ whiteducksoftware.github.io/flock/ ⬅️

🤝 Contributing

We welcome contributions! Please see the CONTRIBUTING.md file (if available) or open an issue/pull request on GitHub.

Ways to contribute:

• Report bugs or suggest features.
• Improve documentation.
• Contribute new Modules, Evaluators, or Routers.
• Add examples to the flock-showcase repository.

📜 License

Flock is licensed under the MIT License. See the LICENSE file for details.

🏢 About

Flock is developed and maintained by white duck GmbH, your partner for cloud-native solutions and AI integration.