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Agently LangChain To Agently

This skill covers migration from LangChain's agent-side surface into Agently. It focuses on create_agent, tools, structured output, short-term memory, middleware-like behavior, retrieval, service exposure, and LangChain-side multi-agent patterns. It does not cover graph-orchestration-first migration from LangGraph; use agently-langgraph-to-triggerflow for that.

Prerequisite: Agently >= 4.0.8.5.

Scope

Use this skill for:

• create_agent
• response_format, ProviderStrategy, or ToolStrategy
• tools
• structured output
• short-term memory
• middleware or guardrails
• human approval or HITL behavior attached through middleware
• retrieval or RAG
• service exposure around an agent
• LangChain-side handoff or specialist-agent patterns

Do not use this skill for:

• StateGraph, interrupts, persistence, or subgraph migration as the main problem
• direct TriggerFlow API implementation as the main problem
• non-migration Agently implementation work that already starts from Agently

Workflow

1. Start with references/concept-map.md to translate LangChain concepts into the current Agently skill tree.
2. Read references/common-migration-recipes.md when the source code resembles a common LangChain production shape.
3. Read references/non-1to1-differences.md when the migration should change design rather than mimic syntax.
4. Read references/current-skill-map.md to choose the target Agently skill combination.
5. If the source code is actually graph-orchestration-heavy, switch to agently-langgraph-to-triggerflow.
6. If behavior still looks wrong, use references/troubleshooting.md.

Core Mental Model

LangChain's high-level agent API bundles several concerns together.

In Agently, the same solution usually splits across several clearer capability surfaces:

• request and provider setup
• input composition
• output control
• tools or MCP
• session continuity
• retrieval
• service exposure
• optional multi-agent or workflow orchestration

So the migration target is usually:

• fewer all-in-one abstractions
• more explicit capability composition

Selection Rules

• LangChain create_agent with one main model path -> agently-model-request-playbook
• response_format or structured output strategy -> agently-output-control
• tools or tool loop -> agently-tools
• MCP-backed tool ecosystem -> agently-mcp
• short-term conversational continuity -> agently-session-memo
• retrieval or KB-backed answer -> agently-knowledge-base-and-rag
• service exposure through FastAPI -> agently-fastapi-helper
• middleware-based approval, guardrail escalation, or resume-after-approval behavior -> and often

Important Boundaries

• there is no single Agently create_agent replacement that absorbs all LangChain concerns
• LangChain ProviderStrategy and ToolStrategy do not map to one Agently strategy enum; in Agently the target is usually explicit output schema plus provider and request choices
• LangChain middleware often becomes explicit request logic, output control, tool policy, or workflow logic in Agently
• short-term memory does not map literally to LangGraph thread/checkpointer semantics; in Agently, conversational continuity usually belongs to session-backed memory
• middleware-based human approval usually becomes explicit interrupt-and-resume workflow design, not one middleware list
• do not migrate old langchain-classic abstractions literally if the Agently target can be simpler

References

• references/source-map.md
• references/concept-map.md
• references/common-migration-recipes.md
• references/non-1to1-differences.md
• references/current-skill-map.md
• references/troubleshooting.md

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Repository

agentera/agently-skills

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