Agently

One-Minute Overview#

Agently is a Python framework specifically designed for building generative AI applications. It addresses the core challenge of integrating AI models into real-world applications: controlling output formats, managing tool calls, and building maintainable workflows. If you're a developer looking to quickly build reliable AI applications without being bogged down by underlying model complexities, Agently is the tool for you.

Core Value: Transforming AI model uncertainty into predictable engineering components, enabling GenAI application development from concept to production readiness.

Getting Started#

Installation Difficulty: Low - Simple pip installation with intuitive API design and abundant example code

# Install the latest version
pip install Agently

# Clone repository and install locally
git clone git@github.com/AgentEra/Agently.git
cd Agently
pip install -e .

Is this suitable for my needs?

• ✅ Rapid Prototyping: Use structured outputs and streaming responses to quickly build AI application prototypes
• ✅ Production Deployment: Features for ensuring output reliability and tool call traceability
• ✅ Multi-Model Switching: Seamlessly switch between different AI models without code rewrites
• ❌ Simple Chatbots: May be overkill for projects requiring only basic chat functionality

Core Capabilities#

1. Structured Output Control - Solving "text returned instead of structured data" issues#

• Supports defining output schemas to ensure model returns expected data structures
• Provides ensure_keys and retry mechanisms to guarantee critical fields exist Actual Value: Prevents parsing errors, ensures AI outputs can be directly used in downstream processing, significantly improving application stability

2. Streaming User Experience - Enabling real-time interaction effects#

• Supports three streaming modes: delta, instant, and typed_delta
• Allows processing partial results during generation for "robot speaking while actions trigger" effects Actual Value: Reduces user waiting time, creates more natural interactions, perfect for chatbots and virtual assistants

3. Tool Planning and Calling - Providing traceable tool usage#

• Supports built-in tools (search, browse) and custom tools
• Provides tool call logging for debugging and auditing Actual Value: Solves tool unpredictability issues, makes every call traceable, improves system reliability

4. TriggerFlow Workflow Engine - Managing complex AI logic#

• Event-driven workflow system supporting branching, concurrency limits, and loops
• Converts visual "low-code graphs" into readable code while maintaining maintainability Actual Value: Visual logic transforms into maintainable code implementation, supports complex AI workflows without chaos

5. Multi-Provider Compatibility - Supporting various AI models#

• Unified configuration interface compatible with OpenAI APIs
• Configurable for local hosting or proxy services Actual Value: Avoids vendor lock-in, flexibly choose the most suitable AI models based on needs, reduces migration costs

Tech Stack & Integration#

Development Language: Python Key Dependencies: Built on Python, compatible with OpenAI API format Integration Method: Used as a Python library, imported via from agently import Agently

Ecosystem & Extensions#

• Plugins/Extensions: Supports custom tool functions easily extended through decorators
• Integration Capabilities: Seamless integration with existing Python systems, supports various deployment methods

Maintenance Status#

• Development Activity: Continuously updated with an active contributor community
• Recent Updates: Recently released v4, indicating ongoing active development
• Community Response: Provides GitHub discussion forum, Twitter, and WeChat group channels with active community support

Documentation & Learning Resources#

• Documentation Quality: Comprehensive, including official documentation website, step-by-step tutorials, and example code
• Official Documentation: https://agentera.github.io/Agently/
• Sample Code: Abundant examples covering both basic and advanced scenarios