Agently 4.1.4.1 - 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 4.1.4.1 is the compatibility release over the 4.1.4 execution baseline. AgentExecution is the stable public run surface across direct, flat, and TaskBoard strategies. AgentExecutionResult.get_data() consistently returns business data, get_full_data() exposes the complete route/task envelope, and get_text() remains the user-facing final-text view. The 4.1.4 baseline also brings Workspace evidence and retrieval, ActionRuntime capabilities, validated TaskDAG execution, TriggerFlow orchestration, RuntimeEvents, and public typing into one production-oriented application stack. Read the 4.1.4.1 Release Notes, 4.1.4 Release Notes, and 4.1.3.9 Release Notes for the current release and upgrade path.

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 - AgentExecution owns one run's prompt, strategy, Actions, Skills, process stream, Workspace evidence, and result views across direct, flat, and TaskBoard strategies. Read 4.1.4.1 Release Notes, 4.1.4 Release Notes, and Agent Auto Orchestration examples.
• Model switching should not rewrite business logic - Agently normalizes provider setup, prompt slots, response parsing, action execution, and response reading into one request/runtime contract. Read Model Setup, Models Overview, and Requests Overview.
• Structured output should be a framework guarantee, not only a provider feature - .output(...) schemas, required field extraction, parser feedback, retries, ensure_keys, ensure_all_keys, and validation handlers work together inside Agently. Read Schema as Prompt, Output Control, and examples in examples/basic/.
• Streaming should expose structure before the final token - instant mode lets consumers react to structured fields while the model is still streaming, which is useful for UI updates, SSE routes, and workflow signals. Read Model Response, FastAPI Service Exposure, and examples/fastapi/.
• Actions should be observable and model-portable - local functions, built-in actions, MCP servers, shell/Python/Node/SQLite/workspace helpers, and custom executors produce structured records and can share one Action Runtime. Read Action Runtime, MCP, and .

Current framework version: 4.1.4.1.

Python: >=3.10.

Framework Positioning

The point is not that other frameworks are wrong. They choose different centers of gravity.

Framework	Primary strength	Where Agently is intentionally different
LangChain	Broad integrations, prebuilt agents, and application building blocks	Agently is narrower and more system-shaped: provider adaptation, prompt slots, structured output, response parsing, action execution, settings, and observability are normalized in one request/runtime contract. See Requests, Action Runtime, and examples/action_runtime/.
LangGraph	Low-level orchestration runtime for long-running, stateful agents	TriggerFlow is the orchestration layer inside Agently's model-application stack: workflow signals compose directly with structured response events, actions, runtime streams, pause/resume, execution state, and close snapshots. See TriggerFlow Events and Streams, Persistence and Blueprint, and examples/trigger_flow/.
CrewAI	Multi-agent crews plus flow-controlled agent teamwork	Agently treats multi-agent collaboration as one buildable pattern on top of lower-level request, action, signal, and workflow primitives, not as the only application shape. See Playbooks and examples/step_by_step/.
AutoGen	Conversable agents and multi-agent conversation patterns	Agently emphasizes model-output contracts, explicit action logs, signal-driven workflows, lifecycle snapshots, and service-facing execution handles over open-ended agent chat as the default boundary.
Direct SDK calls	Maximum control with minimal abstraction	Agently adds reusable contracts for output parsing, actions, sessions, configuration, observability, and workflows without forcing a separate orchestration service.

Use Agently when the application needs an AI execution substrate. Stay closer to direct SDK calls when the product is only one or two simple prompts. Use a specialized multi-agent framework when natural-language agent collaboration is the main product primitive.

The practical differences show up in four layers:

• Against LangChain's integration-first style: LangChain is strong when you want a broad, flexible set of model, tool, retrieval, and agent building blocks. Agently's bet is that production model apps need a more uniform request contract: different model providers should still feed the same prompt slots, structured parser, retry/validation path, reusable result readers, and Action Runtime. That reduces the chance that swapping the base model or provider changes the shape expected by downstream business logic. Start with Requests Overview and Action Runtime.
• Against provider-native structured output as the only guarantee: Agently can use model providers, but its output quality path does not depend only on provider-side JSON schema or tool-calling parameters. The framework owns schema-as-prompt authoring, required-field extraction, parser feedback, retries, ensure_keys, ensure_all_keys, and validation handlers. That matters when the target model does not expose the same structured-output or tool-calling semantics as another provider. See Schema as Prompt and Output Control.
• Against graph-only orchestration: LangGraph is strong for graph-shaped stateful agents and durable execution. TriggerFlow's core is event/signal-driven, and Agently's instant response mode can surface structured fields while the model is still streaming. That lets workflow signals be driven by partial structured output, action results, human input, or sub-flow state instead of waiting for a whole model response to finish. See Model Response, [Tri