Agently Docs

EN

中文

EN

中文

Appearance

Framework Landscape ​

This page summarizes public English sources to clarify positioning and common misconceptions across popular frameworks/platforms. It is not a ranking, only a structured comparison for selection.

Two categories ​

  • Framework / SDK: code-level building blocks (orchestration, output control, data indexing).
  • Platform / Product: visual configuration and operational features for cross‑role collaboration.

Positioning at a glance ​

  • Agently: engineering-first framework with output control, event-driven TriggerFlow, and async-first design.
  • LangChain family: described as a “platform for reliable agents,” expanded with LangGraph (orchestration) and LangSmith (observability/evaluation).
  • LlamaIndex: data framework focused on connecting, indexing, and retrieving external data for LLM apps.
  • AutoGen: multi-agent application framework emphasizing agent collaboration and autonomy.
  • CrewAI: multi-agent automation framework emphasizing lightweight, independent orchestration.
  • Dify: open-source LLM application platform with workflows, RAG, model management, and observability.

Comparison table (positioning / scenarios / boundaries) ​

Scenarios and boundaries are inferred from official positioning and public descriptions for clarity, not hard limits.

Framework / PlatformOfficial positioning (English sources)Typical scenariosBoundaries / notes
AgentlyEngineering‑first AI app framework (output control + TriggerFlow + async‑first)High‑reliability output, event‑driven orchestration, production deliveryPrimarily a framework; platform capabilities are external
LangChain“The platform for reliable agents.”General LLM app composition, tools, retrieval building blocksLangGraph/LangSmith are separate products in the same ecosystem
LangGraphLow‑level orchestration framework for long‑running, stateful agents.Stateful, multi‑step agent workflowsPrimarily orchestration; data/observability can be layered separately
LangSmithDebug, evaluate, and monitor language models and intelligent agents.Evaluation, tracing, monitoringObservability layer, not orchestration or data layer
LlamaIndexA data framework for LLM applications.RAG, data ingestion, indexing and retrievalData‑layer focus; orchestration/observability are separate concerns
AutoGenFramework for multi‑agent AI apps that act autonomously or alongside humans.Multi‑agent collaboration and autonomyFocus on collaboration/autonomy patterns
CrewAIFast, flexible multi‑agent automation framework; independent of other agent frameworks.Role‑based agent automationFocused on agent collaboration patterns; complex orchestration can be layered
DifyOpen‑source platform for developing LLM apps with workflows, RAG, model management, observability.Visual configuration, collaboration, workflow‑driven appsPlatform‑first, emphasizing workflows and ops features

Selection tips (scenario‑driven) ​

  • Output stability & engineering control: consider Agently for output control + event‑driven orchestration.
  • Data/RAG as the center: LlamaIndex is a strong data layer; combine with orchestration if needed.
  • Multi‑agent collaboration: AutoGen or CrewAI for agent‑centric design; add output control for reliability.
  • Visual collaboration & ops: Dify fits platform‑first teams and workflow‑driven iteration.
  • Observability/evaluation: LangSmith serves as a dedicated monitoring layer.

English references ​