القائمة
Workflow patterns and gotchas for LangChain. Directs to RAG for implementation.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
This skill should be used when the user asks about "visual builder", "no-code agent builder", "drag and drop", "ThinkingConfig", "extended thinking", "chain of thought", "reasoning", or needs guidance on using ADK's visual development tools or configuring advanced reasoning capabilities.
This skill should be used when the user asks about "callbacks", "lifecycle hooks", "before_model_call", "after_tool_call", "plugins", "session state", "state management", "artifacts", "file uploads", "events", "EventActions", "human-in-the-loop", "confirmation", "memory", "MemoryService", "long-term memory", "remember across sessions", "RAG", "retrieval augmented generation", "grounding", "knowledge base", "vector search", or needs guidance on customizing agent behavior, intercepting execution, managing state across turns, implementing approval workflows, or implementing persistent memory or grounding agent responses in external knowledge.
This skill should be used when the user asks about "creating a new ADK project", "initializing ADK", "setting up Google ADK", "adk create command", "ADK project structure", "YAML agent configuration", "creating an agent", "LlmAgent", "BaseAgent", "custom agent", "agent with different model", "Claude with ADK", "OpenAI with ADK", "LiteLLM", "multi-model agent", or needs guidance on bootstrapping an ADK development environment, authentication setup, choosing between Python code and YAML-based agent definitions, agent configuration, model selection, system instructions, or extending the base agent class for non-LLM logic.
This skill should be used when the user asks about "multi-agent systems", "sub-agents", "delegation", "agent routing", "orchestration", "SequentialAgent", "ParallelAgent", "LoopAgent", "agent-to-agent", "A2A protocol", "agent hierarchy", "streaming", "real-time responses", "SSE", "server-sent events", "websocket", "bidirectional", "Live API", "voice", "audio", "video", "multimodal streaming", or needs guidance on building systems with multiple specialized agents working together or implementing real-time communication patterns.
This skill should be used when the user asks about "deploying", "production", "Agent Engine", "Vertex AI", "Cloud Run", "GKE", "Kubernetes", "hosting", "scaling", "guardrails", "safety", "content filtering", "input validation", "output validation", "authentication", "OAuth", "API keys", "credentials", "security plugins", "testing agents", "evaluation", "evals", "benchmarks", "tracing", "Cloud Trace", "logging", "observability", "AgentOps", "LangSmith", "user simulation", or needs guidance on deploying ADK agents to production environments, implementing safety measures, access control, secure authentication, testing, debugging, monitoring, or evaluating ADK agent quality.
This skill should be used when the user asks about "adding a tool", "FunctionTool", "creating tools", "MCP integration", "OpenAPI tools", "built-in tools", "google_search tool", "code_execution tool", "long-running tools", "async tools", "third-party tools", "LangChain tools", "computer use", or needs guidance on extending agent capabilities with custom functions, API integrations, or external tool frameworks.
| name | LangChain |
| description | Workflow patterns and gotchas for LangChain. Directs to RAG for implementation. |
| Need | Component | RAG Query |
|---|---|---|
| Basic LLM call | ChatModel | "ChatOpenAI initialization" |
| Prompt templating | PromptTemplate | "prompt template variables" |
| Composable steps | LCEL chains | "LCEL chain composition" |
| Tool execution | Tool/StructuredTool | "langchain tool definition" |
| Document retrieval | Retriever | "retriever vector store" |
| Conversation memory | Memory classes | "conversation buffer memory" |
Query RAG: mcp__agentic-rag__query_sdk("component example", sdk="langchain", mode="build")
These trip up everyone:
chain = prompt | llm | parser, not legacy chainsainvoke - Sync methods block; use async for concurrencyRAG Query: mcp__agentic-rag__query_sdk("langchain installation packages", sdk="langchain", mode="explain")
RAG Query: mcp__agentic-rag__query_sdk("ChatOpenAI ChatAnthropic setup", sdk="langchain", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("ChatPromptTemplate messages", sdk="langchain", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("LCEL chain pipe operator", sdk="langchain", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("StructuredTool from_function", sdk="langchain", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("vector store retriever", sdk="langchain", mode="build")
| Symptom | Likely Cause | RAG Query |
|---|---|---|
| Chain doesn't work | Wrong pipe order | "LCEL chain debugging" |
| Output is raw text | Missing parser | "output parser structured" |
| Context lost | Missing passthrough | "RunnablePassthrough" |
| Async timeout | Using sync method | "langchain async ainvoke" |
| Memory not working | Not connected | "memory chain integration" |
| Use Case | Choose |
|---|---|
| Linear pipelines | LangChain LCEL |
| Conditional branching | LangGraph |
| Cycles/loops | LangGraph |
| Simple retrieval | LangChain |
| Complex agent workflows | LangGraph |
Query RAG when you need:
"langchain streaming callback""langchain callbacks tracing""custom runnable class""langchain caching responses""chain fallback retry"