Agently
Open-source Python framework for building AI application backends with structured outputs, observable actions, workflows, and streaming.
At a Glance
Agently open-source core framework under Apache License 2.0; installable via pip. Trademark, certification, partner or endorsement claims are not granted by the license; commercial authorization via business@agently.tech.
Engagement
Available On
Listed Oct 2026
About Agently
Agently is an open-source Python framework from AgentEra for building AI service backends. It combines structured model outputs, controlled Actions, MCP support, runtime Skills, streaming, and recoverable workflows. The framework requires Python 3.10+, installs with pip, and its README lists current framework version 4.1.4.8.
What It Is
Agently is an AI application runtime framework aimed at product engineers and platform teams who need model behavior to fit stable application contracts. A request is composed from prompt slots such as role, info, instruct, input, and output. Results can be read as parsed data, text, or metadata. The output schema supports required fields, retries, and validation handlers, so downstream code receives typed fields rather than free text.
Structured Output and Streaming
The .output(...) schema defines fields the model must return. In instant streaming mode, consumers can react to individual fields while the model is still generating, which suits UI patches, SSE routes, and workflow signals. Provider setup is normalized across OpenAI-compatible endpoints (including DeepSeek, Ollama, and Qwen) and Anthropic-compatible endpoints, so switching models happens through settings. A Model Pool and API key pools support multiple models and failover.
Actions, Skills, and MCP
Actions are model-callable capabilities. They can be local functions registered with @agent.action_func, MCP servers via agent.use_mcp(...), Search/Browse packages, or helpers for Python, shell, Node.js, SQLite, and workspace files. Action results carry logs and artifact references. Runtime Skills are reusable guidance packages bound to an execution, and skill requirements do not grant side-effect permissions on their own.
Workflows with TriggerFlow and Dynamic Task
TriggerFlow is the event-driven workflow layer, with branching, fan-out, runtime streams, pause/resume, save/load, and close snapshots. Dynamic Task turns model-generated or application-submitted DAG data into validated, observable task execution. FastAPIHelper exposes agents and flows through POST, SSE, and WebSocket endpoints.
Companion Tooling
Agently-Skills is a package of installable skills that guides coding agents such as Codex, Claude Code, and Cursor in generating Agently code. The optional agently-devtools package provides local observation, evaluation, a playground, and project scaffolding. The open-source core uses Apache 2.0, and the project describes an open-core model with separate commercial agreements for enterprise extensions.
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Pricing
Open Source (Apache 2.0)
Agently open-source core framework under Apache License 2.0; installable via pip. Trademark, certification, partner or endorsement claims are not granted by the license; commercial authorization via business@agently.tech.
- Apache License 2.0 core
- Install with pip install -U agently
- Python 3.10+
- Structured outputs, Actions, Skills, MCP, TriggerFlow workflows
- Trademark/official certification not automatically granted by license
Capabilities
Key Features
- Structured output schemas with required fields, retries, and validation
- Instant structured streaming of field deltas
- Provider-agnostic model setup with model pools and API key pools
- Action Runtime with function calling, MCP, and sandboxed execution
- Runtime Skills bound to agent executions
- TriggerFlow event-driven workflows with pause/resume and persistence
- Dynamic Task validated DAG planning and execution
- Session memory
- FastAPIHelper for POST, SSE, and WebSocket endpoints
- Optional DevTools for observation, evaluation, and playground
- Agently-Skills guidance for coding agents
