Agently
[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application
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What is Agently?
Agently is a Python-based GenAI application development framework that bridges the gap between large language models and real-world applications. It provides engineered output control and event-driven orchestration to make model outputs verifiable and workflows orchestrable. The framework turns model uncertainty into system determinism through structured output control, key field guarantees, instant parsing, and TriggerFlow event-driven orchestration.
Key features include: structured output control with verifiable contracts, instant structured streaming parsing that makes partial structures available in real-time, TriggerFlow for signal-driven workflow orchestration (when→to→emit pattern), async-first design for high concurrency, response objects with unified result reading (text/data/event streams from one request), configurable YAML prompt management with version control, Session/Memo for fixed multi-turn state, model hot-swap without rewriting business logic, tools and extensions (auto_func, MCP integration), and structured knowledge base injection with case studies.
Agently is designed for product engineers and developers building AI assistants, teams moving from
Agently pricing
Pricing model: Freemium
Agently is open source under Apache 2.0 license and free to use. A free 2-year commercial license is available upon request by contacting [email protected]. Commercial users must display
Agently pros
- Structured output control with verifiable contracts
- Key field guarantees (ensure_keys) for critical data
- Instant structured streaming parsing for real-time signals
- TriggerFlow event-driven orchestration with when-to-emit pattern
- Async-first design optimized for high concurrency
- Model hot-swap without rewriting business logic
- Unified response object for text/data/metadata/event streams
- Configurable YAML prompt management with versioning
- Session and Memo for fixed multi-turn conversation state
- Built-in support for OpenAICompatible model interface
- Event bus with runtime data triggering downstream tasks
- Structured knowledge base injection with KB case studies
- Tool调用 and auto_func for extensible capabilities
- MCP integration for connecting to external systems
- 1.5k GitHub stars with active community
- Apache 2.0 open source license
- Free 2-year commercial license available on request
- 5-minute quickstart with minimal setup
- Event-driven rather than static drag-and-drop workflows
- Real-time streaming output visible during generation
Agently cons
- Primarily Python-based (limited to Python ecosystem)
- Requires understanding of event-driven programming concepts
- Learning curve for TriggerFlow signal-driven orchestration
- Most documentation appears in Chinese (limited English docs)
- No built-in no-code workspace (requires coding)
- Requires external LLM API keys (OpenAI or compatible)
- No official hosted platform (self-hosted framework only)
- Async API required for production concurrency
- Instant parsing requires structured output format setup
- No official GUI for workflow visualization
- Limited prebuilt agent templates compared to no-code platforms
- Commercial use requires attribution display
- No official pricing tier structure (open source only)
- Sparse English tutorials compared to Chinese content
- Requires manual setup of model configuration
Frequently asked questions about Agently
What is Agently?
Agently is a Python-based GenAI application development framework that provides engineered output control and event-driven orchestration. It bridges the gap between large language models and real-world applications by making model outputs verifiable and workflows orchestrable. The framework helps teams move from 'the model can do it once' to 'the application must do it reliably'.
How do I install Agently?
Install Agently via pip with the command: pip install -U Agently. You can also use uv pip install -U Agently or pipx install Agently if you use uv or pipx. After installation, import features using from agently import Agently.
What models does Agently support?
Agently v4 uses OpenAICompatible as its unified interface. You can set up any OpenAI-compatible model including OpenAI's official API (gpt-4o-mini, etc.), Ollama models (like qwen2.5:7b), and other providers. Model settings are configured via Agently.set_settings() with base_url, api_key, and model parameters.
What is TriggerFlow?
TriggerFlow is Agently's signal-driven orchestration system. It uses a when→to→emit pattern where flows are triggered by signals from events, runtime data, and flow-level data rather than static drag-and-drop workflows. Processing blocks automatically emit completion signals when done, triggering downstream tasks. Runtime data updates also trigger events that can be intercepted.
What is instant structured streaming?
Instant structured streaming allows you to consume partial structured output in real-time while the model is still generating. This makes local structures available immediately as real-time signals to trigger runtime logic, enabling边生成边行动 (acting while generating) for推理, actions, and output to reach the frontend earlier in a single request.
Can I switch models without rewriting code?
Yes. Agently supports model hot-swap through its OpenAICompatible unified interface. You can switch between different models and providers by only changing the model configuration in Agently.set_settings() without rewriting your main business logic.
What use cases is Agently suitable for?
Agently is suitable for: PRD to test case generation, surveys/multi-turn interviews, knowledge base Q&A/customer service assistants, real-time interaction/embodied feedback systems, AI assistants, KB multi-turn QA with ChromaCollection + vector models, and any production GenAI applications requiring stable outputs, observability, and robust workflows.
How do I manage prompts in Agently?
Agently uses configurable YAML prompt management with分层提示词 (layered prompts), variable mapping, and versioning. You create prompt.yaml files with .agent (system prompt) and .request (input/output format) sections, then load them using agent.load_yaml_prompt() with field mappings for production maintainability.