Agently 4.1.3.9 - AI Application Runtime Framework

Build AI service backends with structured outputs, observable Actions, runtime Skills, MCP capabilities, process streams, and recoverable workflows.

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Who This README Is For

Agently is for teams moving from "the model can do it once" to "the application must do it reliably":

• product engineers building assistants, internal copilots, knowledge tools, operation workflows, or AI-backed APIs
• platform teams that need clear extension points for model providers, tools, MCP servers, sandboxes, workflows, and observability
• technical leads comparing AI frameworks for maintainability, explicit control, debuggability, and production handoff
• coding-agent users who want a framework whose recommended patterns can be encoded as reusable project guidance

The main design question is simple: how do you keep model behavior useful while still giving application code stable contracts, observable execution, and restart-safe workflow boundaries?

Agently 4.1.3.9 promotes Workspace retrieval and Session memory as shared framework substrate: workspace.retrieve(...) packages record/file evidence with keyword/tag candidates, optional vector/hybrid retrieval, structure-gated rerank, refill, and compact model-hot projections; SessionMemory plus AgentlyMemory stores durable GLOBAL_MEMORY and SESSION_MEMORY in Workspace; AgentTask scoped retrieval uses the same retrieval substrate; and public typing covers the new Workspace vector seam plus common dict payloads on TaskBoard update helpers. Read the 4.1.3.9 Release Notes, 4.1.3.8 Release Notes, 4.1.3.7 Release Notes, 4.1.3.6 Release Notes, 4.1.3.5 Release Notes, 4.1.3.4 Release Notes, 4.1.3.3 Release Notes, 4.1.3.2 Release Notes, 4.1.3.1 Release Notes, and 4.1.3 Release Notes for the full release story.

Why Agently

Many AI frameworks are strong at exploration or at assembling broad integration stacks. Agently is optimized for the engineering layer that makes model applications survive model changes, output drift, streaming UX, action execution, workflow signals, and service boundaries.

Agently is a good fit when you care about:

• AI services should be runtime executions, not prompt glue - one Agent execution can declare candidate Actions, Skills, MCP services, Dynamic Task planning, process streams, Workspace-backed retrieval, and output contracts, then execute through the same runtime surface. Read 4.1.3.9 Release Notes, Agent Auto Orchestration examples, and Skills Executor examples.
• Model switching should not rewrite business logic - Agently normalizes provider setup, prompt slots, response p