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LobeHub Data Fetching Architecture

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Streamline your data fetching with structured guidelines.

by lobehub81.5k stars on lobehub/lobehub
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Updated Aug 1, 2026
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What LobeHub Data Fetching Architecture does

The LobeHub Data Fetching Architecture skill provides a comprehensive guide for implementing a robust data fetching pipeline in your applications. It emphasizes the importance of using a dedicated service layer, Zustand store, and SWR hooks for efficient data management. By following the outlined principles, developers can ensure clean and maintainable code that adheres to best practices in data handling. This skill is particularly beneficial for those looking to migrate from traditional data fetching methods, such as useEffect, to a more structured approach that enhances performance and reliability.

The architecture is designed to encapsulate all API interactions within a service layer, which acts as a single source of truth for API operations. This encapsulation not only simplifies the interface for making requests but also ensures that all API calls are typed and well-defined. The use of Zustand for state management, combined with SWR hooks for data fetching, allows for efficient caching and state updates, reducing the need for manual state management and improving application responsiveness.

This skill is ideal for developers and designers working on React applications who want to implement a scalable and maintainable data fetching strategy. It is particularly useful for teams looking to standardize their data handling practices across projects, ensuring consistency and reducing the likelihood of bugs related to data fetching and state management. By adopting the principles outlined in this guide, teams can focus on building features rather than wrestling with data management issues.

When to use it

Use this skill when building or refactoring applications that require efficient data fetching and state management.

When not to use it

This skill may not be suitable for simple applications with minimal data fetching needs or for those already using a different architecture effectively.

What you can build with it

Refactoring an Existing Application

Use this skill to transition from useEffect-based data fetching to a structured service layer and Zustand store.

Building a New React App

Implement the outlined architecture from the start to ensure maintainable and efficient data handling.

Standardizing Data Management Practices

Adopt this skill across teams to create consistency in how data fetching is handled in multiple projects.

How to install LobeHub Data Fetching Architecture

View source

1. Install with the skills CLI

npx skills add lobehub/lobehub/data-fetching-architecture --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by lobehub

LobeHub Data Fetching Architecture

Related: store-data-structures covers List vs Detail data shape rationale (Map vs Array).

Architecture Overview

┌─────────────┐
│  Component  │
└──────┬──────┘
       │ 1. Call useFetchXxx hook from store
       ↓
┌──────────────────┐
│  Zustand Store   │
│  (State + Hook)  │
└──────┬───────────┘
       │ 2. useClientDataSWR calls service
       ↓
┌──────────────────┐
│  Service Layer   │
│  (xxxService)    │
└──────┬───────────┘
       │ 3. Call lambdaClient
       ↓
┌──────────────────┐
│  lambdaClient    │
│  (TRPC Client)   │
└──────────────────┘

Core Principles

✅ DO

  1. Use Service Layer for all API calls
  2. Use Store SWR Hooks for data fetching (not useEffect)
  3. Use proper data structures — see store-data-structures skill for List vs Detail patterns
  4. Use lambdaClient.mutate for write operations (create/update/delete)
  5. Use lambdaClient.query only inside service methods
  6. Naming convention — read hooks are useFetchXxx, cache invalidation helpers are refreshXxx (e.g. useFetchBenchmarks / refreshBenchmarks). Mutations then chain refreshXxx() after the service call.

❌ DON'T

  1. Never use useEffect for data fetching
  2. Never call lambdaClient directly in components or stores
  3. Never use useState for server data
  4. Never mix data structure patterns — follow store-data-structures skill

Layer 1: Service Layer

Purpose

  • Encapsulate all API calls to lambdaClient
  • Provide clean, typed interfaces
  • Single source of truth for API operations

Service Structure

// src/services/agentEval.ts
class AgentEvalService {
  // Query methods - READ operations
  async listBenchmarks() {
    return lambdaClient.agentEval.listBenchmarks.query();
  }

  async getBenchmark(id: string) {
    return lambdaClient.agentEval.getBenchmark.query({ id });
  }

  // Mutation methods - WRITE operations
  async createBenchmark(params: CreateBenchmarkParams) {
    return lambdaClient.agentEval.createBenchmark.mutate(params);
  }

  async updateBenchmark(params: UpdateBenchmarkParams) {
    return lambdaClient.agentEval.updateBenchmark.mutate(params);
  }

  async deleteBenchmark(id: string) {
    return lambdaClient.agentEval.deleteBenchmark.mutate({ id });
  }
}

export const agentEvalService = new AgentEvalService();

Service Guidelines

  1. One service per domain (e.g., agentEval, ragEval, aiAgent)
  2. Export singleton instance (export const xxxService = new XxxService())
  3. Method names match operations (list, get, create, update, delete)
  4. Clear parameter types (use interfaces for complex params)

Layer 2: Store with SWR Hooks

Purpose

  • Manage client-side state
  • Provide SWR hooks for data fetching
  • Handle cache invalidation

State Structure

// src/store/eval/slices/benchmark/initialState.ts
export interface BenchmarkSliceState {
  // List data - simple array
  benchmarkList: AgentEvalBenchmarkListItem[];
  benchmarkListInit: boolean;

  // Detail data - map for caching
  benchmarkDetailMap: Record<string, AgentEvalBenchmark>;
  loadingBenchmarkDetailIds: string[];

  // Mutation states
  isCreatingBenchmark: boolean;
  isUpdatingBenchmark: boolean;
  isDeletingBenchmark: boolean;
}

For complete initialState, reducer, and internal dispatch patterns, see the store-data-structures skill.

Actions

// src/store/eval/slices/benchmark/action.ts
const FETCH_BENCHMARKS_KEY = 'FETCH_BENCHMARKS';
const FETCH_BENCHMARK_DETAIL_KEY = 'FETCH_BENCHMARK_DETAIL';

export interface BenchmarkAction {
  // SWR Hooks - for data fetching
  useFetchBenchmarks: () => SWRResponse;
  useFetchBenchmarkDetail: (id?: string) => SWRResponse;

  // Refresh methods - for cache invalidation
  refreshBenchmarks: () => Promise<void>;
  refreshBenchmarkDetail: (id: string) => Promise<void>;

  // Mutation actions
  createBenchmark: (params: CreateParams) => Promise<any>;
  updateBenchmark: (params: UpdateParams) => Promise<void>;
  deleteBenchmark: (id: string) => Promise<void>;

  // Internal methods - not for direct UI use
  internal_dispatchBenchmarkDetail: (payload: BenchmarkDetailDispatch) => void;
  internal_updateBenchmarkDetailLoading: (id: string, loading: boolean) => void;
}

export const createBenchmarkSlice: StateCreator<EvalStore, any, [], BenchmarkAction> = (
  set,
  get,
) => ({
  // Fetch list — simple array stored in benchmarkList
  useFetchBenchmarks: () =>
    useClientDataSWR(FETCH_BENCHMARKS_KEY, () => agentEvalService.listBenchmarks(), {
      onSuccess: (data) => {
        set({ benchmarkList: data, benchmarkListInit: true }, false, 'useFetchBenchmarks/success');
      },
    }),

  // Fetch detail — null key disables the request when id is missing
  useFetchBenchmarkDetail: (id) =>
    useClientDataSWR(
      id ? [FETCH_BENCHMARK_DETAIL_KEY, id] : null,
      () => agentEvalService.getBenchmark(id!),
      {
        onSuccess: (data) => {
          get().internal_dispatchBenchmarkDetail({
            type: 'setBenchmarkDetail',
            id: id!,
            value: data,
          });
          get().internal_updateBenchmarkDetailLoading(id!, false);
        },
      },
    ),

  // Refresh methods
  refreshBenchmarks: () => mutate(FETCH_BENCHMARKS_KEY),
  refreshBenchmarkDetail: (id) => mutate([FETCH_BENCHMARK_DETAIL_KEY, id]),

  // CREATE — refresh list after creation
  createBenchmark: async (params) => {
    set({ isCreatingBenchmark: true }, false, 'createBenchmark/start');
    try {
      const result = await agentEvalService.createBenchmark(params);
      await get().refreshBenchmarks();
      return result;
    } finally {
      set({ isCreatingBenchmark: false }, false, 'createBenchmark/end');
    }
  },

  // UPDATE — optimistic update + refresh
  updateBenchmark: async (params) => {
    const { id } = params;

    // 1. Optimistic update
    get().internal_dispatchBenchmarkDetail({
      type: 'updateBenchmarkDetail',
      id,
      value: params,
    });
    // 2. Set loading
    get().internal_updateBenchmarkDetailLoading(id, true);

    try {
      // 3. Call service
      await agentEvalService.updateBenchmark(params);
      // 4. Refresh from server
      await get().refreshBenchmarks();
      await get().refreshBenchmarkDetail(id);
    } finally {
      get().internal_updateBenchmarkDetailLoading(id, false);
    }
  },

  // DELETE — optimistic update + refresh
  deleteBenchmark: async (id) => {
    get().internal_dispatchBenchmarkDetail({ type: 'deleteBenchmarkDetail', id });
    get().internal_updateBenchmarkDetailLoading(id, true);

    try {
      await agentEvalService.deleteBenchmark(id);
      await get().refreshBenchmarks();
    } finally {
      get().internal_updateBenchmarkDetailLoading(id, false);
    }
  },

  // Internal — dispatch to reducer (for detail map)
  internal_dispatchBenchmarkDetail: (payload) => {
    const currentMap = get().benchmarkDetailMap;
    const nextMap = benchmarkDetailReducer(currentMap, payload);

    // Skip set when nothing changed — avoids unnecessary re-renders
    if (isEqual(nextMap, currentMap)) return;
    set({ benchmarkDetailMap: nextMap }, false, `dispatchBenchmarkDetail/${payload.type}`);
  },

  // Internal — update loading state for specific detail
  internal_updateBenchmarkDetailLoading: (id, loading) => {
    set(
      (state) => ({
        loadingBenchmarkDetailIds: loading
          ? [...state.loadingBenchmarkDetailIds, id]
          : state.loadingBenchmarkDetailIds.filter((i) => i !== id),
      }),
      false,
      'updateBenchmarkDetailLoading',
    );
  },
});

Store Guidelines

  1. SWR keys as constants at top of file
  2. useClientDataSWR for all data fetching (never useEffect)
  3. onSuccess/onData callback updates store state
  4. Refresh methods use mutate() to invalidate cache
  5. Loading states in initialState, updated in onSuccess/onData
  6. Mutations call service, then refresh relevant cache

Async Failure Boundary Contract

Every read hook returns an SWR response that includes error and mutate; the surface must consume them. A success-only init flag (!isInit, !map[id], data ?? []) is not enough: when the request fails, that flag often never flips, so the UI paints a permanent skeleton, a fake empty state, a false NotFound, or a confident zero-value metric.

Use the shared UI primitives:

  • AsyncBoundary for normal loading / error / empty / data surfaces.
  • AsyncError for custom layouts, detail pages, inline load-more failures, or metrics.

Core precedence for first-load failures:

const { data, error, isLoading, mutate } = useFetchXxx();

return (
  <AsyncBoundary
    data={data}
    empty={<EmptyState />}
    error={error}
    isEmpty={!error && data?.length === 0}
    isLoading={isLoading}
    onRetry={() => {
      void mutate();
    }}
  >
    <List items={data ?? []} />
  </AsyncBoundary>
);

Rules:

  • Check error before empty / NotFound / zero defaults. Error is not a kind of empty.
  • Keep already-loaded content on background revalidation failures; only replace the surface when there is no settled data to preserve.
  • For detail maps, don't put the error branch after if (!map[id]) return <Skeleton/>. First-load failures never populate the map, making that error branch unreachable.
  • For infinite scroll / load-more, persist a per-bucket loadMoreError and render an inline Retry row. Do not let an IntersectionObserver silently retry while the error is still unresolved.
  • For merged fetched + static lists, branch on the fetched slice's error before merging. Static fallback rows can make a failed fetch look like a plausible partial catalog.

Layer 3: Component Usage

Fetching List Data

// ✅ CORRECT
const BenchmarkList = () => {
  // 1. Get the hook from store
  const useFetchBenchmarks = useEvalStore((s) => s.useFetchBenchmarks);

  // 2. Get list data
  const benchmarks = useEvalStore((s) => s.benchmarkList);
  const isInit = useEvalStore((s) => s.benchmarkListInit);

  // 3. Call the hook (SWR handles the data fetching)
  useFetchBenchmarks();

  // 4. Use the data
  if (!isInit) return <Loading />;
  return (
    <div>
      <h2>Total: {benchmarks.length}</h2>
      {benchmarks.map((b) => (
        <BenchmarkCard key={b.id} {...b} />
      ))}
    </div>
  );
};

Fetching Detail Data

// ✅ CORRECT
const BenchmarkDetail = () => {
  const { benchmarkId } = useParams<{ benchmarkId: string }>();

  const useFetchBenchmarkDetail = useEvalStore((s) => s.useFetchBenchmarkDetail);

  // Detail from map
  const benchmark = useEvalStore((s) =>
    benchmarkId ? s.benchmarkDetailMap[benchmarkId] : undefined,
  );

  // Per-item loading
  const isLoading = useEvalStore((s) =>
    benchmarkId ? s.loadingBenchmarkDetailIds.includes(benchmarkId) : false,
  );

  useFetchBenchmarkDetail(benchmarkId);

  if (!benchmark) return <Loading />;
  return (
    <div>
      <h1>{benchmark.name}</h1>
      <p>{benchmark.description}</p>
      {isLoading && <Spinner />}
    </div>
  );
};

Using Selectors (Recommended)

// src/store/eval/slices/benchmark/selectors.ts
export const benchmarkSelectors = {
  getBenchmarkDetail: (id: string) => (s: EvalStore) => s.benchmarkDetailMap[id],
  isLoadingBenchmarkDetail: (id: string) => (s: EvalStore) =>
    s.loadingBenchmarkDetailIds.includes(id),
};

// Component with selectors
const BenchmarkDetail = () => {
  const { benchmarkId } = useParams();
  const useFetchBenchmarkDetail = useEvalStore((s) => s.useFetchBenchmarkDetail);
  const benchmark = useEvalStore(benchmarkSelectors.getBenchmarkDetail(benchmarkId!));

  useFetchBenchmarkDetail(benchmarkId);

  return <div>{benchmark && <h1>{benchmark.name}</h1>}</div>;
};

Anti-pattern

// ❌ WRONG — Don't use useEffect for data fetching
const BenchmarkList = () => {
  const [data, setData] = useState([]);
  const [loading, setLoading] = useState(false);

  useEffect(() => {
    setLoading(true);
    lambdaClient.agentEval.listBenchmarks
      .query()
      .then(setData)
      .finally(() => setLoading(false));
  }, []);

  return <div>...</div>;
};

Mutations in Components

// Create — global mutation flag drives form loading
const CreateBenchmarkModal = () => {
  const createBenchmark = useEvalStore((s) => s.createBenchmark);
  const isCreating = useEvalStore((s) => s.isCreatingBenchmark);

  const handleSubmit = async (values) => {
    try {
      // Optimistic update + refresh happen inside createBenchmark
      await createBenchmark(values);
      message.success('Created successfully');
      onClose();
    } catch (error) {
      message.error('Failed to create');
    }
  };

  return (
    <Form onSubmit={handleSubmit} loading={isCreating}>
      ...
    </Form>
  );
};

// Update / delete — per-item loading so only the row being mutated spins
const BenchmarkItem = ({ id }: { id: string }) => {
  const updateBenchmark = useEvalStore((s) => s.updateBenchmark);
  const deleteBenchmark = useEvalStore((s) => s.deleteBenchmark);
  const isLoading = useEvalStore(benchmarkSelectors.isLoadingBenchmarkDetail(id));

  const handleUpdate = async (data) => {
    await updateBenchmark({ id, ...data });
  };

  const handleDelete = async () => {
    await deleteBenchmark(id);
  };

  return (
    <div>
      {isLoading && <Spinner />}
      <button onClick={handleUpdate}>Update</button>
      <button onClick={handleDelete}>Delete</button>
    </div>
  );
};

Why two patterns: create has no id yet, so a single isCreatingXxx flag is enough. Update/delete target a specific row, so global flags would freeze unrelated rows — keep per-item state in loadingXxxIds.


Need a fuller worked example?

The canonical Benchmark example above is the one to copy for a flat list + detail map. If you need to maintain a list keyed by a parent id (e.g. datasetMap[benchmarkId] because the same shape appears under multiple parents), read references/walkthrough.md — it walks through the full 6 steps (service → reducer → slice → store wiring → selectors → component) for that variant.


Common Patterns

Pattern 1: Pagination

Cache key array must include every parameter that should trigger a refetch.

useFetchTestCases: (params: { datasetId: string; limit: number; offset: number }) =>
  useClientDataSWR(
    params.datasetId ? [FETCH_TEST_CASES_KEY, params.datasetId, params.limit, params.offset] : null,
    () => agentEvalService.listTestCases(params),
    {
      onSuccess: (data) =>
        set({
          testCaseList: data.data,
          testCaseTotal: data.total,
          isLoadingTestCases: false,
        }),
    },
  );

Pattern 2: Dependent Fetching

Both hooks run in parallel — SWR dedupes, no manual sequencing needed.

const BenchmarkDetail = () => {
  const { benchmarkId } = useParams();
  const useFetchBenchmarkDetail = useEvalStore((s) => s.useFetchBenchmarkDetail);
  const useFetchDatasets = useEvalStore((s) => s.useFetchDatasets);

  useFetchBenchmarkDetail(benchmarkId);
  useFetchDatasets(benchmarkId);

  return <div>...</div>;
};

Pattern 3: Conditional Fetching

Pass undefined to disable the hook entirely.

// only fetch when modal is open AND id present
useFetchDatasetDetail(open && datasetId ? datasetId : undefined);

Pattern 4: Cross-domain Refresh

deleteBenchmark: async (id) => {
  await agentEvalService.deleteBenchmark(id);
  await get().refreshBenchmarks();
  await get().refreshDatasets(id); // related cache invalidated too
};

Migration Guide: useEffect → Store SWR

Before (❌ Wrong)

const TestCaseList = ({ datasetId }: Props) => {
  const [data, setData] = useState<any[]>([]);
  const [loading, setLoading] = useState(false);

  useEffect(() => {
    setLoading(true);
    lambdaClient.agentEval.listTestCases
      .query({ datasetId })
      .then((r) => setData(r.data))
      .finally(() => setLoading(false));
  }, [datasetId]);

  return <Table data={data} loading={loading} />;
};

After (✅ Correct)

// 1. Add service method
class AgentEvalService {
  async listTestCases(params: { datasetId: string }) {
    return lambdaClient.agentEval.listTestCases.query(params);
  }
}

// 2. Add store slice hook
export const createTestCaseSlice: StateCreator<...> = (set) => ({
  useFetchTestCases: (params) =>
    useClientDataSWR(
      params.datasetId ? [FETCH_TEST_CASES_KEY, params.datasetId] : null,
      () => agentEvalService.listTestCases(params),
      {
        onSuccess: (data) =>
          set({ testCaseList: data.data, isLoadingTestCases: false }),
      },
    ),
});

// 3. Component reads from store
const TestCaseList = ({ datasetId }: Props) => {
  const useFetchTestCases = useEvalStore((s) => s.useFetchTestCases);
  const data = useEvalStore((s) => s.testCaseList);
  const loading = useEvalStore((s) => s.isLoadingTestCases);

  useFetchTestCases({ datasetId });

  return <Table data={data} loading={loading} />;
};

Troubleshooting

SymptomCheck
Data never loadsHook called? Key not null/undefined? Network tab shows request?
Stale data after mutationDid refreshXxx run? Cache key matches what the hook uses?
Loading state stuck trueonSuccess writes loading=false? Promise rejected silently?
Detail map missing an entryReducer dispatch ran? isEqual short-circuited on stale data?

Summary Checklist

When adding new data fetching:

Step 1: Types & State

See store-data-structures for details.

  • Define types in @lobechat/types: Detail type + List item type
  • State structure: xxxList: XxxListItem[], xxxDetailMap: Record<string, Xxx>, loadingXxxDetailIds: string[]
  • Reducer if optimistic updates are needed

Step 2: Service Layer

  • Create service in src/services/xxxService.ts
  • Methods: listXxx(), getXxx(id), createXxx(), updateXxx(), deleteXxx()

Step 3: Store Actions

  • initialState.ts with state structure
  • action.ts with:
    • useFetchXxxList(), useFetchXxxDetail(id) — SWR hooks
    • refreshXxxList(), refreshXxxDetail(id) — cache invalidation
    • CRUD methods calling service
    • internal_dispatch, internal_updateLoading if using reducer
  • selectors.ts (optional but recommended)
  • Integrate slice into main store + initialState

Step 4: Component Usage

  • Use store hooks (NOT useEffect)
  • Destructure and consume error / mutate; wrap first-load surfaces in AsyncBoundary or render AsyncError before empty / NotFound / zero defaults
  • List pages: access xxxList array
  • Detail pages: access xxxDetailMap[id]
  • Use loading states for UI feedback
  • Infinite-scroll failures persist as a visible tail Retry row, not a silent catch

Mental model: Types → Service → Reducer → Slice → Component 🎯


Related Skills

  • store-data-structures — How to structure List and Detail data in stores
  • zustand — General Zustand patterns and best practices

Frequently asked questions about LobeHub Data Fetching Architecture

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