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Agently

Agently is an Apache-2.0 Python framework for building AI application and service backends with structured outputs, observable Actions, runtime Skills, MCP capabilities, process streams and recoverable workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agently — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Agently when an AI feature needs explicit output contracts, tool execution, streaming structure or coordinated workflow logic inside a Python backend.

Who it is for

  • Product engineers building assistants, internal copilots, knowledge tools, operational workflows or AI-backed APIs.
  • Platform teams that need extension points for models, tools, MCP servers, workflows and observability.
  • Technical leads comparing Python AI frameworks for explicit execution and model portability.

Documented uses

  • Build AI service backends with structured results.
  • Coordinate multi-step GenAI processing through event-driven flows.
  • Connect applications to local tools and MCP capabilities.
  • Expose streaming AI responses to application interfaces.
  • Develop model-flexible agent applications without rewriting business logic for each provider.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the Apache-2.0 license.

  • Python is the primary language.
  • The framework documents model-provider abstractions intended to reduce business-logic changes when switching models.
  • Structured outputs include schema, parsing, required-field and validation controls.
  • Actions can include local functions, built-in actions and MCP servers through a shared runtime.
  • TriggerFlow supports event-driven handling of complex GenAI logic.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Define structured results for an Agently application

Configure an Agently application to request and validate structured model output.

Before you start

  • Python development environment
  • Agently project and model setup
  • An output schema supported by the application

Steps

  1. Configure the model provider through Agently's model setup and request abstractions.

  2. Define the expected result with Agently's output schema controls.

  3. Use required-field extraction, parsing feedback, validation handlers or retry controls as needed.

  4. Read the resulting structured response through the documented model-response interfaces.

Expected outcome

The application handles model results through a defined structured-output contract rather than relying only on free-form text.

Guide limitations

  • The supplied repository material does not provide a complete implementation example or provider-specific configuration.
  • Source-based guidance does not verify execution in a particular environment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
3 October 2026
Original website title
AgentEra/Agently · Official homepage title · retained in its source language
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