refactor: remove redundant UI event handling in workflows

better handler typing
docs: word smith
2026-07-03 08:24:39 -04:00 · 2025-05-15 13:40:43 +07:00 · 2025-05-15 13:36:37 +07:00 · 2025-05-15 10:42:29 +07:00 · 2025-05-15 09:12:51 +07:00 · 2025-05-14 13:39:12 +07:00
227 changed files with 4538 additions and 17454 deletions
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Split artifacts use case to document generator and code generator
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Fix typing check issue
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+fix chromadb dependency issue
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+fix: remove dead generated ai code
@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Deprecate pro mode
@@ -64,15 +64,6 @@ jobs:
        run: pnpm run pack-install
        working-directory: packages/create-llama

-      - name: Build and store server package
-        run: |
-          pnpm run build
-          wheel_file=$(ls dist/*.whl | head -n 1)
-          mkdir -p "${{ runner.temp }}"
-          cp "$wheel_file" "${{ runner.temp }}/"
-          echo "SERVER_PACKAGE_PATH=${{ runner.temp }}/$(basename "$wheel_file")" >> $GITHUB_ENV
-        working-directory: python/llama-index-server
-
      - name: Run Playwright tests for Python
        run: pnpm run e2e:python
        env:
@@ -83,13 +74,12 @@ jobs:
          TEMPLATE_TYPE: ${{ matrix.template-types }}
          PYTHONIOENCODING: utf-8
          PYTHONLEGACYWINDOWSSTDIO: utf-8
-          SERVER_PACKAGE_PATH: ${{ env.SERVER_PACKAGE_PATH }}
        working-directory: packages/create-llama

      - uses: actions/upload-artifact@v4
        if: always()
        with:
-          name: playwright-report-python-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-${{ matrix.template-types }}
+          name: playwright-report-python-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}
          path: packages/create-llama/playwright-report/
          overwrite: true
          retention-days: 30
@@ -146,21 +136,6 @@ jobs:
        run: pnpm run pack-install
        working-directory: packages/create-llama

-      - name: Build server
-        run: pnpm run build
-        working-directory: packages/server
-
-      - name: Pack @llamaindex/server package
-        run: |
-          pnpm pack --pack-destination "${{ runner.temp }}"
-          if [ "${{ runner.os }}" == "Windows" ]; then
-            file=$(find "${{ runner.temp }}" -name "llamaindex-server-*.tgz" | head -n 1)
-            mv "$file" "${{ runner.temp }}/llamaindex-server.tgz"
-          else
-            mv ${{ runner.temp }}/llamaindex-server-*.tgz ${{ runner.temp }}/llamaindex-server.tgz
-          fi
-        working-directory: packages/server
-
      - name: Run Playwright tests for TypeScript
        run: pnpm run e2e:typescript
        env:
@@ -169,13 +144,12 @@ jobs:
          FRAMEWORK: ${{ matrix.frameworks }}
          DATASOURCE: ${{ matrix.datasources }}
          TEMPLATE_TYPE: ${{ matrix.template-types }}
-          SERVER_PACKAGE_PATH: ${{ runner.temp }}/llamaindex-server.tgz
        working-directory: packages/create-llama

      - uses: actions/upload-artifact@v4
        if: always()
        with:
-          name: playwright-report-typescript-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-node${{ matrix.node-version }}-${{ matrix.template-types }}
+          name: playwright-report-typescript-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-node${{ matrix.node-version }}
          path: packages/create-llama/playwright-report/
          overwrite: true
          retention-days: 30
@@ -16,16 +16,6 @@ jobs:

      - uses: pnpm/action-setup@v3

-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v5
-        with:
-          enable-cache: true
-
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
@@ -17,11 +17,6 @@ jobs:

      - uses: pnpm/action-setup@v3

-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
      - name: Install uv
        uses: astral-sh/setup-uv@v3

@@ -56,12 +51,8 @@ jobs:
        with:
          commit: Release ${{ steps.get-changeset-status.outputs.new-version }}
          title: Release ${{ steps.get-changeset-status.outputs.new-version }}
-          # bump versions
-          version: pnpm new-version
          # build package and call changeset publish
          publish: pnpm release
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
@@ -0,0 +1,138 @@
+name: Release llama-index-server
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "python/llama-index-server/**"
+      - ".github/workflows/release_llama_index_server.yml"
+  pull_request:
+    types:
+      - closed
+
+concurrency: ${{ github.workflow }}-${{ github.ref }}
+
+jobs:
+  release:
+    name: Create Release PR
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ./python/llama-index-server
+    if: |
+      github.event_name == 'push' && 
+      !startsWith(github.ref, 'refs/heads/release/llama-index-server-v') &&
+      !contains(github.event.head_commit.message, 'Release: llama-index-server v')
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        shell: bash
+        run: uv sync --all-extras --dev
+
+      - name: Setup Git
+        run: |
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          git config --global user.name "github-actions[bot]"
+
+      - name: Bump patch version
+        shell: bash
+        run: |
+          uvx --from=toml-cli toml set --toml-path=pyproject.toml project.version $(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version | awk -F. '{$NF = $NF + 1;}1' OFS=.)
+          git add pyproject.toml
+          git commit -m "chore(release): bump llama-index-server version to $(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)"
+
+      - name: Get current version
+        id: get_version
+        shell: bash
+        run: |
+          version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)
+          echo "current_version=${version}" >> "$GITHUB_OUTPUT"
+
+      - name: Create Release PR
+        uses: peter-evans/create-pull-request@v6
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          commit-message: "Release: llama-index-server v${{ steps.get_version.outputs.current_version }}"
+          title: "Release: llama-index-server v${{ steps.get_version.outputs.current_version }}"
+          body: |
+            This PR was automatically created to release a new version of the llama-index-server package.
+
+            Version: ${{ steps.get_version.outputs.current_version }}
+
+            Please review the changes and merge to trigger the release.
+          branch: release/llama-index-server-v${{ steps.get_version.outputs.current_version }}
+          base: main
+          labels: release, llama-index-server
+
+  publish:
+    name: Publish to PyPI
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ./python/llama-index-server
+    if: |
+      github.event_name == 'pull_request' && 
+      github.event.pull_request.merged == true && 
+      startsWith(github.event.pull_request.title, 'Release: llama-index-server') &&
+      startsWith(github.event.pull_request.head.ref, 'release/llama-index-server-v')
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        shell: bash
+        run: uv sync --all-extras
+
+      - name: Get current version
+        id: get_version
+        shell: bash
+        run: |
+          version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)
+          echo "current_version=${version}" >> "$GITHUB_OUTPUT"
+
+      - name: Build package
+        shell: bash
+        run: uv build --no-sources
+
+      - name: Publish to PyPI
+        shell: bash
+        run: uv publish --token ${{ secrets.PYPI_TOKEN }}
+
+      - name: Create GitHub Release
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: llama-index-server-v${{ steps.get_version.outputs.current_version }}
+          name: "llama-index-server v${{ steps.get_version.outputs.current_version }}"
+          body: |
+            Release of llama-index-server v${{ steps.get_version.outputs.current_version }}
+          draft: false
+          prerelease: false
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -5,7 +5,6 @@ on:

 env:
  PYTHON_VERSION: "3.9"
-  UI_TEST: "true"

 jobs:
  unit-test:
@@ -20,27 +19,20 @@ jobs:
        python-version: ["3.9"]
    steps:
      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}

      - name: Install uv
        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true

-      - name: Setup Node.js
-        uses: actions/setup-node@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
        with:
-          node-version-file: ".nvmrc"
-          cache: "pnpm"
+          python-version: ${{ matrix.python-version }}

      - name: Install dependencies
        shell: bash
-        run: pnpm install && pnpm build
+        run: uv sync --all-extras --dev

      - name: Run unit tests
        shell: bash
@@ -54,20 +46,20 @@ jobs:
        working-directory: python/llama-index-server
    steps:
      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}

      - name: Install uv
        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true

+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+
      - name: Install dependencies
-        run: pnpm install
+        shell: bash
+        run: uv sync --all-extras --dev

      - name: Run mypy
        shell: bash
@@ -81,56 +73,27 @@ jobs:
        working-directory: python/llama-index-server
    steps:
      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}

      - name: Install uv
        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true

-      - name: Setup Node.js
-        uses: actions/setup-node@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
        with:
-          node-version-file: ".nvmrc"
-          cache: "pnpm"
+          python-version: ${{ env.PYTHON_VERSION }}

-      - name: Install dependencies
-        run: pnpm install && pnpm build
-
-      - name: Build package
+      - name: Install build package
        shell: bash
-        run: uv build
-
-      - name: Get the absolute wheel file path and save it to the output
-        shell: bash
-        id: get_whl_path
-        run: |
-          WHL_FILE=$(readlink -f dist/*.whl)
-          echo "whl_file=$WHL_FILE" >> $GITHUB_OUTPUT
+        run: uv sync --all-extras

      - name: Test import
        shell: bash
-        working-directory: ${{ github.workspace }}
-        env:
-          WHL_FILE: ${{ steps.get_whl_path.outputs.whl_file }}
-        run: |
-          uv run --with $WHL_FILE python -c "from llama_index.server import LlamaIndexServer"
-
-      - name: Check frontend resources is present
-        shell: bash
-        working-directory: ${{ github.workspace }}
-        env:
-          WHL_FILE: ${{ steps.get_whl_path.outputs.whl_file }}
-        run: |
-          uv run --with $WHL_FILE python -c "from llama_index.server.chat_ui import check_ui_resources; check_ui_resources()"
+        run: uv run python -c "from llama_index.server import LlamaIndexServer"

      - name: Upload artifact
        uses: actions/upload-artifact@v4
        with:
          name: llama-index-server
-          path: dist/
+          path: python/llama-index-server/dist/
@@ -7,7 +7,6 @@ build/
 .next/
 out/
 packages/server/server/
-packages/server/project/
 **/playwright-report/
 **/test-results/

@@ -1,201 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Repository Overview
-
-Create-llama is a monorepo containing CLI tools and server frameworks for building LlamaIndex-powered applications. The repository combines TypeScript/Node.js and Python components in a unified development environment.
-
-## Architecture
-
-### Monorepo Structure
-
- **`packages/create-llama/`**: Main CLI tool for scaffolding LlamaIndex applications
- **`packages/server/`**: TypeScript/Next.js server framework (`@llamaindex/server`)
- **`python/llama-index-server/`**: Python/FastAPI server framework
- **Root**: Workspace configuration and shared development tools
-
-### Key Technologies
-
- **Package Manager**: pnpm with workspace configuration
- **Build Tools**: bunchee (TypeScript), Next.js, hatchling (Python)
- **Testing**: Playwright for e2e, pytest for Python
- **Version Management**: changesets for TypeScript packages, manual for Python
-
-## Development Commands
-
-### Root Level (Monorepo)
-
-```bash
-pnpm dev          # Start all packages in development mode
-pnpm build        # Build all packages
-pnpm lint         # ESLint across TypeScript packages
-pnpm format       # Prettier formatting
-pnpm e2e          # Run end-to-end tests
-```
-
-### Create-llama Package
-
-```bash
-cd packages/create-llama
-npm run build     # Build CLI using bash script and ncc
-npm run dev       # Watch mode development
-npm run e2e       # Playwright tests for generated projects
-npm run clean     # Clean build artifacts and template caches
-```
-
-### TypeScript Server Package
-
-```bash
-cd packages/server
-pnpm dev          # Watch mode with bunchee
-pnpm build        # Multi-step build: ESM/CJS + Next.js + static assets
-pnpm clean        # Clean all build outputs
-```
-
-### Python Server Package
-
-```bash
-cd python/llama-index-server
-uv run generate   # Index data files
-fastapi dev       # Start development server with hot reload
-pytest            # Run test suite
-```
-
-## Template System
-
-The CLI uses a sophisticated template system in `packages/create-llama/templates/`:
-
-### Organization
-
- **`types/`**: Base project structures (streaming, reflex, llamaindexserver)
- **`components/`**: Reusable components across frameworks
-  - `engines/` - Chat and agent engines
-  - `loaders/` - File, web, database loaders
-  - `providers/` - AI model configurations
-  - `vectordbs/` - Vector database integrations
-  - `use-cases/` - Workflow implementations
-
-### Development Workflow
-
- Templates support multiple frameworks (Next.js, Express, FastAPI)
- Component system allows mix-and-match functionality
- E2E tests validate generated projects work correctly
-
-## Server Framework Architecture
-
-### TypeScript Server (`@llamaindex/server`)
-
- **Core**: `LlamaIndexServer` class wrapping Next.js with workflow support
- **Frontend**: React-based chat UI with shadcn/ui components
- **API**: `/api/chat` endpoint with streaming responses
- **Build Process**: Complex multi-step build including static assets for Python integration
-
-### Python Server (`llama-index-server`)
-
- **Core**: `LlamaIndexServer` class extending FastAPI
- **Architecture**: Workflow factory pattern for stateless request handling
- **UI Generation**: AI-powered React component generation from Pydantic schemas
- **Development**: Hot reloading support with dev mode
-
-## Common Patterns
-
-### Workflow Integration
-
-Both server frameworks use factory patterns:
-
-```typescript
-// TypeScript
-const server = new LlamaIndexServer({
-  workflow: (context) => createWorkflow(context)
-});
-
-// Python
-def create_workflow(chat_request: ChatRequest) -> Workflow:
-    return MyWorkflow(chat_request.messages)
-```
-
-### Event System
-
-Structured events for UI communication:
-
- **UIEvent**: Custom components with Pydantic/Zod schemas
- **ArtifactEvent**: Code/documents for Canvas panel
- **SourceNodesEvent**: Document sources with metadata
- **AgentRunEvent**: Tool usage and progress tracking
-
-### File Handling
-
- Both servers auto-mount `data/` and `output/` directories
- LlamaCloud integration for remote file access
- Static file serving through framework-specific methods
-
-## Testing Strategy
-
-### E2E Testing
-
- Playwright tests in `packages/create-llama/e2e/`
- Tests both Python and TypeScript generated projects
- Validates CLI generation and application functionality
-
-### Unit Testing
-
- Python: pytest with comprehensive API and service tests
- TypeScript: Integrated testing through build process
-
-## Build Process
-
-### Create-llama CLI
-
-1. TypeScript compilation with bash script
-2. ncc bundling for standalone executable
-3. Template validation and caching
-
-### Server Package Build
-
-1. **prebuild**: Clean directories
-2. **build**: bunchee compilation to ESM/CJS
-3. **postbuild**: Next.js preparation and static asset generation
-4. **prepare:py-static**: Python integration assets
-
-### Release Process
-
-```bash
-pnpm release     # Build all + publish npm packages + Python release
-```
-
-## Development Environment Setup
-
-### Prerequisites
-
- Node.js >=16.14.0
- Python with uv package manager
- pnpm for package management
-
-### Common Workflow
-
-1. Clone repository and run `pnpm install`
-2. For CLI development: work in `packages/create-llama/`
-3. For server development: choose TypeScript or Python package
-4. Use `pnpm dev` for concurrent development across packages
-5. Run `pnpm e2e` to validate changes with generated projects
-
-## Special Considerations
-
-### Template Development
-
- Changes to templates require rebuilding CLI
- E2E tests validate template functionality across frameworks
- Template caching system speeds up repeated builds
-
-### Cross-package Dependencies
-
- Server package builds static assets for Python integration
- Version synchronization between TypeScript and Python packages
- Shared UI components and styling across implementations
-
-### Performance
-
- CLI uses caching for template operations
- Server frameworks support streaming responses
- Background processing for file operations and LlamaCloud integration
@@ -57,9 +57,6 @@ export default tseslint.config(
      "**/out/**",
      "**/node_modules/**",
      "**/build/**",
-      "packages/server/server/**",
-      "packages/server/project/**",
-      "packages/server/bin/**",
    ],
  },
 );
@@ -13,8 +13,7 @@
  },
  "license": "MIT",
  "workspaces": [
-    "packages/*",
-    "python/*"
+    "packages/*"
  ],
  "scripts": {
    "dev": "pnpm -r dev",
@@ -25,10 +24,8 @@
    "format:write": "prettier --ignore-unknown --write .",
    "prepare": "husky",
    "new-snapshot": "pnpm -r build && changeset version --snapshot",
-    "new-version-python": "pnpm --filter @create-llama/llama-index-server new-version",
-    "new-version": "pnpm -r build && changeset version && pnpm new-version-python",
-    "release-python": "pnpm --filter @create-llama/llama-index-server release",
-    "release": "pnpm -r build && changeset publish && pnpm release-python",
+    "new-version": "pnpm -r build && changeset version",
+    "release": "pnpm -r build && changeset publish",
    "release-snapshot": "pnpm -r build && changeset publish --tag snapshot"
  },
  "devDependencies": {
@@ -1,67 +1,5 @@
 # create-llama

-## 0.5.22
-
-### Patch Changes
-
- e2486eb: feat: support human in the loop for TS
-
-## 0.5.21
-
-### Patch Changes
-
- af9ad3c: feat: show document artifact after generating report
- a543a27: feat: bump chat-ui with inline artifact
-
-## 0.5.20
-
-### Patch Changes
-
- 3ff0a18: fix: default header padding
-
-## 0.5.19
-
-### Patch Changes
-
- 5fe9e17: support eject to fully customize next folder
- b8a1ff6: Support citation for agentic template (Python)
-
-## 0.5.18
-
-### Patch Changes
-
- 8d59ef0: Add layout_dir config to the generated python code
-
-## 0.5.17
-
-### Patch Changes
-
- eee3230: feat: support custom layout
-
-## 0.5.16
-
-### Patch Changes
-
- 6f75d4a: fix: unsupported language in code gen workflow
- d0618fa: Fix LlamaCloud generate script issue
-
-## 0.5.15
-
-### Patch Changes
-
- 527075c: Enable dev mode that allows updating code directly in the UI
-
-## 0.5.14
-
-### Patch Changes
-
- 1df8cfb: Split artifacts use case to document generator and code generator
- 1b5a519: chore: improve dev experience with nodemon
- b3eb0ba: Fix typing check issue
- 556f33c: fix chromadb dependency issue
- 2451539: fix: remove dead generated ai code
- 7a70390: Deprecate pro mode
-
 ## 0.5.13

 ### Patch Changes
@@ -1,108 +0,0 @@
-# create-llama Package
-
-## Overview
-
-The `create-llama` package is a CLI tool for creating LlamaIndex-powered applications with one command. It's designed as a project generator that scaffolds various types of RAG (Retrieval-Augmented Generation) applications using different frameworks, databases, and AI model providers.
-
-## Package Structure
-
-### Core Files
-
- **`index.ts`**: Main CLI entry point using Commander.js for argument parsing
- **`create-app.ts`**: Core application creation logic and orchestration
- **`package.json`**: Package configuration with binary entry point at `./dist/index.js`
-
-### Key Directories
-
- **`helpers/`**: Utility functions for package management, file operations, and configuration
- **`questions/`**: Interactive prompts for user configuration
- **`templates/`**: Project templates for different frameworks and use cases
- **`e2e/`**: End-to-end tests using Playwright
-
-## Core Functionality
-
-### CLI Interface
-
-The tool accepts numerous command-line options including:
-
- Framework selection (`--framework`: nextjs, express, fastapi)
- Template type (`--template`: streaming, multiagent, reflex, llamaindexserver)
- Model providers (OpenAI, Anthropic, Groq, Ollama, etc.)
- Vector databases (none, mongo, pg, pinecone, milvus, etc.)
- Data sources (files, web URLs, databases)
- Tools and observability options
-
-### Application Generation Flow
-
-1. **Project validation**: Checks project name validity and directory permissions
-2. **Interactive questioning**: Prompts user for configuration if not provided via CLI
-3. **Template installation**: Copies and configures appropriate templates
-4. **Environment setup**: Creates `.env` files with API keys and configuration
-5. **Dependencies**: Installs packages using detected/specified package manager
-6. **Post-install actions**: Can run the app, open VSCode, or install dependencies
-
-### Template System
-
-Templates are organized by:
-
- **Framework**: NextJS (frontend), Express (Node backend), FastAPI (Python backend)
- **Type**: Streaming chat, multiagent workflows, Reflex UI, LlamaIndex server
- **Components**: Engines, loaders, providers, UI components, observability
-
-### Helper Functions
-
-Key helper modules include:
-
- **Installation**: Package manager detection and dependency installation
- **Data sources**: File copying, web scraping, database connection setup
- **Providers**: Model provider configuration (OpenAI, Anthropic, etc.)
- **Tools**: Integration with external tools (Wikipedia, weather, code generation)
- **Environment**: `.env` file generation with API keys and settings
-
-## Development Commands
-
-### Build & Development
-
- `npm run build`: Build the CLI using bash script
- `npm run dev`: Watch mode development build
- `npm run clean`: Clean build artifacts and temporary files
-
-### Testing
-
- `npm run e2e`: Run all end-to-end tests
- `npm run e2e:python`: Test Python-specific templates
- `npm run e2e:typescript`: Test TypeScript-specific templates
-
-### Package Management
-
- `npm run pack-install`: Create and install local package for testing
-
-## Architecture Notes
-
-### Model Configuration
-
-The tool supports multiple AI providers with a unified `ModelConfig` interface that includes:
-
- Provider selection and API key management
- Model and embedding model specification
- Dimension configuration for embeddings
-
-### Data Source Handling
-
-Flexible data source configuration supporting:
-
- Local files and directories
- Web URLs with configurable crawling depth
- Database connections with custom queries
- Automatic file downloading and copying
-
-### Template Flexibility
-
-Templates use a component-based system allowing mix-and-match of:
-
- Different frameworks (NextJS, Express, FastAPI)
- Various vector databases
- Multiple observability tools
- Configurable tools and integrations
-
-This package serves as the foundation for rapidly prototyping and deploying LlamaIndex applications across different technology stacks and use cases.
@@ -20,7 +20,6 @@ const useCases: TemplateUseCase[] = [
  "financial_report",
  "code_generator",
  "document_generator",
-  "hitl",
 ];
 const dataSource: string = process.env.DATASOURCE
  ? process.env.DATASOURCE
@@ -1,5 +1,5 @@
 import { expect, test } from "@playwright/test";
-import { ChildProcess, execSync } from "child_process";
+import { ChildProcess } from "child_process";
 import fs from "fs";
 import path from "path";
 import type {
@@ -12,32 +12,21 @@ import { createTestDir, runCreateLlama, type AppType } from "../utils";
 const templateFramework: TemplateFramework = process.env.FRAMEWORK
  ? (process.env.FRAMEWORK as TemplateFramework)
  : "fastapi";
-const dataSource: string = process.env.DATASOURCE
-  ? (process.env.DATASOURCE as string)
-  : "--example-file";
-const llamaCloudProjectName = "create-llama";
-const llamaCloudIndexName = "e2e-test";
-
+const dataSource: string = "--example-file";
 const templateUI: TemplateUI = "shadcn";
 const templatePostInstallAction: TemplatePostInstallAction = "runApp";
 const appType: AppType = "--frontend";
 const userMessage = "Write a blog post about physical standards for letters";
-const templateUseCases = [
-  "agentic_rag",
-  "financial_report",
-  "deep_research",
-  "code_generator",
-  // "hitl",
-];
-const ejectDir = "next";
+const templateUseCases = ["financial_report", "agentic_rag", "deep_research"];

 for (const useCase of templateUseCases) {
  test.describe(`Test use case ${useCase} ${templateFramework} ${dataSource} ${templateUI} ${appType} ${templatePostInstallAction}`, async () => {
    test.skip(
-      dataSource === "--no-files" || templateFramework === "express",
+      process.platform !== "linux" ||
+        process.env.DATASOURCE === "--no-files" ||
+        templateFramework === "express",
      "The llamaindexserver template currently only works with nextjs, fastapi. We also only run on Linux to speed up tests.",
    );
-    const useLlamaParse = dataSource === "--llamacloud";
    let port: number;
    let cwd: string;
    let name: string;
@@ -59,9 +48,6 @@ for (const useCase of templateUseCases) {
        templateUI,
        appType,
        useCase,
-        llamaCloudProjectName,
-        llamaCloudIndexName,
-        useLlamaParse,
      });
      name = result.projectName;
      appProcess = result.appProcess;
@@ -112,28 +98,6 @@ for (const useCase of templateUseCases) {
      expect(response.ok()).toBeTruthy();
    });

-    test("Should successfully eject, install dependencies and build without errors", async () => {
-      test.skip(
-        templateFramework !== "nextjs" ||
-          useCase !== "code_generator" ||
-          dataSource === "--llamacloud",
-        "Eject test only applies to Next.js framework, code generator use case, and non-llamacloud",
-      );
-
-      // Run eject command
-      execSync("npm run eject", { cwd: path.join(cwd, name) });
-
-      // Verify next directory exists
-      const nextDirExists = fs.existsSync(path.join(cwd, name, ejectDir));
-      expect(nextDirExists).toBeTruthy();
-
-      // Install dependencies in next directory
-      execSync("npm install", { cwd: path.join(cwd, name, ejectDir) });
-
-      // Run build
-      execSync("npm run build", { cwd: path.join(cwd, name, ejectDir) });
-    });
-
    // clean processes
    test.afterAll(async () => {
      appProcess?.kill();
@@ -3,7 +3,6 @@ import { exec } from "child_process";
 import fs from "fs";
 import path from "path";
 import util from "util";
-import { NO_DATA_USE_CASES } from "../../helpers/constant";
 import {
  TemplateFramework,
  TemplateType,
@@ -26,7 +25,6 @@ const useCases: TemplateUseCase[] = [
  "financial_report",
  "code_generator",
  "document_generator",
-  "hitl",
 ];
 const dataSource: string = process.env.DATASOURCE
  ? process.env.DATASOURCE
@@ -85,7 +83,7 @@ test.describe("Test resolve TS dependencies", () => {
          });
        });
        // Skipping llamacloud for the use case doesn't use index.
-        if (!useCase || !NO_DATA_USE_CASES.includes(useCase)) {
+        if (useCase !== "code_generator" && useCase !== "document_generator") {
          test(`llamaParse - ${optionDescription}`, async () => {
            await runTest({
              templateType: templateType,
@@ -1,15 +1,6 @@
-import { TemplateUseCase } from "./types";
-
 export const COMMUNITY_OWNER = "run-llama";
 export const COMMUNITY_REPO = "create_llama_projects";
 export const LLAMA_PACK_OWNER = "run-llama";
 export const LLAMA_PACK_REPO = "llama_index";
 export const LLAMA_PACK_FOLDER = "llama-index-packs";
 export const LLAMA_PACK_FOLDER_PATH = `${LLAMA_PACK_OWNER}/${LLAMA_PACK_REPO}/main/${LLAMA_PACK_FOLDER}`;
-
-// these use cases don't have data folder, so no need to run generate and no need to getIndex
-export const NO_DATA_USE_CASES: TemplateUseCase[] = [
-  "code_generator",
-  "document_generator",
-  "hitl",
-];
@@ -4,7 +4,6 @@ import path from "path";
 import picocolors, { cyan } from "picocolors";

 import fsExtra from "fs-extra";
-import { NO_DATA_USE_CASES } from "./constant";
 import { writeLoadersConfig } from "./datasources";
 import { createBackendEnvFile, createFrontendEnvFile } from "./env-variables";
 import { PackageManager } from "./get-pkg-manager";
@@ -19,7 +18,6 @@ import {
  ModelConfig,
  TemplateDataSource,
  TemplateFramework,
-  TemplateUseCase,
  TemplateVectorDB,
 } from "./types";
 import { installTSTemplate } from "./typescript";
@@ -62,7 +60,6 @@ async function generateContextData(
  vectorDb?: TemplateVectorDB,
  llamaCloudKey?: string,
  useLlamaParse?: boolean,
-  useCase?: TemplateUseCase,
 ) {
  if (packageManager) {
    const runGenerate = `${cyan(
@@ -99,13 +96,7 @@ async function generateContextData(
        }
      } else {
        console.log(`Running ${runGenerate} to generate the context data.`);
-
-        const shouldRunGenerate =
-          !useCase || !NO_DATA_USE_CASES.includes(useCase);
-
-        if (shouldRunGenerate) {
-          await callPackageManager(packageManager, true, ["run", "generate"]);
-        }
+        await callPackageManager(packageManager, true, ["run", "generate"]);
        return;
      }
    }
@@ -233,7 +224,6 @@ export const installTemplate = async (
        props.vectorDb,
        props.llamaCloudKey,
        props.useLlamaParse,
-        props.useCase,
      );
    }

@@ -5,7 +5,6 @@ import { parse, stringify } from "smol-toml";
 import terminalLink from "terminal-link";
 import { isUvAvailable, tryUvSync } from "./uv";

-import { isCI } from "ci-info";
 import { assetRelocator, copy } from "./copy";
 import { templatesDir } from "./dir";
 import { Tool } from "./tools";
@@ -268,7 +267,7 @@ const getAdditionalDependencies = (
    if (observability === "traceloop") {
      dependencies.push({
        name: "traceloop-sdk",
-        version: ">=0.15.11",
+        version: ">=0.15.11,<0.16.0",
      });
    }
    if (observability === "llamatrace") {
@@ -279,19 +278,6 @@ const getAdditionalDependencies = (
    }
  }

-  // If app template is llama-index-server and CI and SERVER_PACKAGE_PATH is set,
-  // add @llamaindex/server to dependencies
-  if (
-    templateType === "llamaindexserver" &&
-    isCI &&
-    process.env.SERVER_PACKAGE_PATH
-  ) {
-    dependencies.push({
-      name: "llama-index-server",
-      version: `@file://${process.env.SERVER_PACKAGE_PATH}`,
-    });
-  }
-
  return dependencies;
 };

@@ -592,12 +578,6 @@ const installLlamaIndexServerTemplate = async ({
    cwd: path.join(templatesDir, "components", "ui", "use-cases", useCase),
  });

-  // Copy layout components to layout folder in root
-  await copy("*", path.join(root, "layout"), {
-    parents: true,
-    cwd: path.join(templatesDir, "components", "ui", "layout"),
-  });
-
  if (useLlamaParse) {
    await copy("index.py", path.join(root, "app"), {
      parents: true,
@@ -697,7 +677,6 @@ export const installPythonTemplate = async ({
    dataSources,
    tools,
    template,
-    observability,
  );

  await addDependencies(root, addOnDependencies);
@@ -59,8 +59,7 @@ export type TemplateUseCase =
  | "contract_review"
  | "agentic_rag"
  | "code_generator"
-  | "document_generator"
-  | "hitl";
+  | "document_generator";
 // Config for both file and folder
 export type FileSourceConfig =
  | {
@@ -4,7 +4,6 @@ import path from "path";
 import { bold, cyan, red, yellow } from "picocolors";
 import { assetRelocator, copy } from "../helpers/copy";
 import { callPackageManager } from "../helpers/install";
-import { NO_DATA_USE_CASES } from "./constant";
 import { templatesDir } from "./dir";
 import { PackageManager } from "./get-pkg-manager";
 import { InstallTemplateArgs, ModelProvider, TemplateVectorDB } from "./types";
@@ -43,18 +42,12 @@ const installLlamaIndexServerTemplate = async ({
    rename: assetRelocator,
  });

-  // copy workflow UI components to components folder in root
+  // copy workflow UI components to output/components folder
  await copy("*", path.join(root, "components"), {
    parents: true,
    cwd: path.join(templatesDir, "components", "ui", "use-cases", useCase),
  });

-  // copy layout components to layout folder in root
-  await copy("*", path.join(root, "layout"), {
-    parents: true,
-    cwd: path.join(templatesDir, "components", "ui", "layout"),
-  });
-
  // Override generate.ts if workflow use case doesn't use custom UI
  if (vectorDb === "llamacloud") {
    await copy("generate.ts", path.join(root, "src"), {
@@ -84,7 +77,7 @@ const installLlamaIndexServerTemplate = async ({
  }

  // Simplify use case code
-  if (useCase && NO_DATA_USE_CASES.includes(useCase)) {
+  if (useCase === "code_generator" || useCase === "document_generator") {
    // Artifact use case doesn't use index.
    // We don't need data.ts, generate.ts
    await fs.rm(path.join(root, "src", "app", "data.ts"));
@@ -394,7 +387,7 @@ const providerDependencies: {
  [key in ModelProvider]?: Record<string, string>;
 } = {
  openai: {
-    "@llamaindex/openai": "~0.4.0",
+    "@llamaindex/openai": "^0.3.7",
  },
  gemini: {
    "@llamaindex/google": "^0.2.0",
@@ -520,7 +513,7 @@ async function updatePackageJson({
  if (backend) {
    packageJson.dependencies = {
      ...packageJson.dependencies,
-      "@llamaindex/readers": "~3.1.4",
+      "@llamaindex/readers": "^3.1.3",
    };

    if (vectorDb && vectorDb in vectorDbDependencies) {
@@ -550,16 +543,6 @@ async function updatePackageJson({
    };
  }

-  // if having custom server package tgz file, use it for testing @llamaindex/server
-  const serverPackagePath = process.env.SERVER_PACKAGE_PATH;
-  if (serverPackagePath && template === "llamaindexserver") {
-    const relativePath = path.relative(process.cwd(), serverPackagePath);
-    packageJson.dependencies = {
-      ...packageJson.dependencies,
-      "@llamaindex/server": `file:${relativePath}`,
-    };
-  }
-
  await fs.writeFile(
    packageJsonFile,
    JSON.stringify(packageJson, null, 2) + os.EOL,
@@ -1,6 +1,6 @@
 {
  "name": "create-llama",
-  "version": "0.5.22",
+  "version": "0.5.13",
  "description": "Create LlamaIndex-powered apps with one command",
  "keywords": [
    "rag",
@@ -6,7 +6,7 @@ const defaults: Omit<QuestionArgs, "modelConfig"> = {
  framework: "nextjs",
  ui: "shadcn",
  frontend: false,
-  llamaCloudKey: undefined,
+  llamaCloudKey: "",
  useLlamaParse: false,
  communityProjectConfig: undefined,
  llamapack: "",
@@ -1,5 +1,4 @@
 import prompts from "prompts";
-import { NO_DATA_USE_CASES } from "../helpers/constant";
 import { EXAMPLE_10K_SEC_FILES, EXAMPLE_FILE } from "../helpers/datasources";
 import { askModelConfig } from "../helpers/providers";
 import { getTools } from "../helpers/tools";
@@ -12,8 +11,7 @@ type AppType =
  | "financial_report"
  | "deep_research"
  | "code_generator"
-  | "document_generator"
-  | "hitl";
+  | "document_generator";

 type SimpleAnswers = {
  appType: AppType;
@@ -59,12 +57,6 @@ export const askSimpleQuestions = async (
          value: "document_generator",
          description: "Build a OpenAI canvas-styled document generator.",
        },
-        {
-          title: "Human in the Loop",
-          value: "hitl",
-          description:
-            "Build a CLI command workflow that is reviewed by a human before execution",
-        },
      ],
    },
    questionHandlers,
@@ -89,8 +81,7 @@ export const askSimpleQuestions = async (
  );
  language = newLanguage;

-  const shouldAskLlamaCloud = !NO_DATA_USE_CASES.includes(appType);
-  if (shouldAskLlamaCloud) {
+  if (appType !== "code_generator" && appType !== "document_generator") {
    const { useLlamaCloud: newUseLlamaCloud } = await prompts(
      {
        type: "toggle",
@@ -179,12 +170,6 @@ const convertAnswers = async (
      tools: [],
      modelConfig: MODEL_GPT41,
    },
-    hitl: {
-      template: "llamaindexserver",
-      dataSources: [],
-      tools: [],
-      modelConfig: MODEL_GPT41,
-    },
  };

  const results = lookup[answers.appType];
@@ -12,7 +12,7 @@ dependencies = [
    "llama-index>=0.12.1",
    "llama-parse>=0.6.21,<0.7.0",
    "cachetools>=5.3.3",
-    "reflex==0.7.10",
+    "reflex>=0.6.2.post1",
 ]

 [project.scripts]
@@ -13,7 +13,7 @@ dependencies = [
    "llama-index>=0.12.1",
    "llama-parse>=0.6.21,<0.7.0",
    "cachetools>=5.3.3",
-    "reflex==0.7.10",
+    "reflex>=0.6.2.post1",
 ]

 [project.scripts]
@@ -1,40 +0,0 @@
-"use client";
-
-import { Sparkles, Star } from "lucide-react";
-
-export default function Header() {
-  return (
-    <div className="flex items-center justify-between p-2 px-4">
-      <div className="flex items-center gap-2">
-        <Sparkles className="size-4" />
-        <h1 className="font-semibold">LlamaIndex App</h1>
-      </div>
-      <div className="flex items-center justify-end gap-4">
-        <div className="flex items-center gap-2">
-          <a
-            href="https://www.llamaindex.ai/"
-            target="_blank"
-            rel="noopener noreferrer"
-            className="text-sm text-gray-600 hover:text-gray-800 dark:text-gray-400 dark:hover:text-gray-200"
-          >
-            Built by LlamaIndex
-          </a>
-          <img
-            className="h-[24px] w-[24px] rounded-sm"
-            src="/llama.png"
-            alt="Llama Logo"
-          />
-        </div>
-        <a
-          href="https://github.com/run-llama/LlamaIndexTS"
-          target="_blank"
-          rel="noopener noreferrer"
-          className="hover:bg-accent flex items-center gap-2 rounded-md border border-gray-300 px-2 py-1 text-sm"
-        >
-          <Star className="size-4" />
-          Star on GitHub
-        </a>
-      </div>
-    </div>
-  );
-}
@@ -1,95 +0,0 @@
-import { Button } from "@/components/ui/button";
-import { Card, CardContent, CardFooter } from "@/components/ui/card";
-import { JSONValue, useChatUI } from "@llamaindex/chat-ui";
-import React, { FC, useState } from "react";
-import { z } from "zod";
-
-// This schema is equivalent to the CLICommand model defined in events.py
-const CLIInputEventSchema = z.object({
-  command: z.string(),
-});
-type CLIInputEvent = z.infer<typeof CLIInputEventSchema>;
-
-const CLIHumanInput: FC<{
-  events: JSONValue[];
-}> = ({ events }) => {
-  const inputEvent = (events || [])
-    .map((ev) => {
-      const parseResult = CLIInputEventSchema.safeParse(ev);
-      return parseResult.success ? parseResult.data : null;
-    })
-    .filter((ev): ev is CLIInputEvent => ev !== null)
-    .at(-1);
-
-  const { append } = useChatUI();
-  const [confirmedValue, setConfirmedValue] = useState<boolean | null>(null);
-  const [editableCommand, setEditableCommand] = useState<string | undefined>(
-    inputEvent?.command,
-  );
-
-  // Update editableCommand if inputEvent changes (e.g. new event comes in)
-  React.useEffect(() => {
-    setEditableCommand(inputEvent?.command);
-  }, [inputEvent?.command]);
-
-  const handleConfirm = () => {
-    append({
-      content: "Yes",
-      role: "user",
-      annotations: [
-        {
-          type: "human_response",
-          data: {
-            execute: true,
-            command: editableCommand, // Use editable command
-          },
-        },
-      ],
-    });
-    setConfirmedValue(true);
-  };
-
-  const handleCancel = () => {
-    append({
-      content: "No",
-      role: "user",
-      annotations: [
-        {
-          type: "human_response",
-          data: {
-            execute: false,
-            command: inputEvent?.command,
-          },
-        },
-      ],
-    });
-    setConfirmedValue(false);
-  };
-
-  return (
-    <Card className="my-4">
-      <CardContent className="pt-6">
-        <p className="text-sm text-gray-700">
-          Do you want to execute the following command?
-        </p>
-        <input
-          disabled
-          type="text"
-          value={editableCommand || ""}
-          onChange={(e) => setEditableCommand(e.target.value)}
-          className="my-2 w-full overflow-x-auto rounded border border-gray-300 bg-gray-100 p-3 font-mono text-xs text-gray-800"
-        />
-      </CardContent>
-      {confirmedValue === null ? (
-        <CardFooter className="flex justify-end gap-2">
-          <>
-            <Button onClick={handleConfirm}>Yes</Button>
-            <Button onClick={handleCancel}>No</Button>
-          </>
-        </CardFooter>
-      ) : null}
-    </Card>
-  );
-};
-
-export default CLIHumanInput;
@@ -3,12 +3,9 @@ from typing import Optional
 from app.index import get_index
 from llama_index.core.agent.workflow import AgentWorkflow
 from llama_index.core.settings import Settings
+from llama_index.llms.openai import OpenAI
 from llama_index.server.api.models import ChatRequest
 from llama_index.server.tools.index import get_query_engine_tool
-from llama_index.server.tools.index.citation import (
-    CITATION_SYSTEM_PROMPT,
-    enable_citation,
-)


 def create_workflow(chat_request: Optional[ChatRequest] = None) -> AgentWorkflow:
@@ -17,16 +14,9 @@ def create_workflow(chat_request: Optional[ChatRequest] = None) -> AgentWorkflow
        raise RuntimeError(
            "Index not found! Please run `uv run generate` to index the data first."
        )
-    # Create a query tool with citations enabled
-    query_tool = enable_citation(get_query_engine_tool(index=index))
-
-    # Define the system prompt for the agent
-    # Append the citation system prompt to the system prompt
-    system_prompt = """You are a helpful assistant"""
-    system_prompt += CITATION_SYSTEM_PROMPT
-
+    query_tool = get_query_engine_tool(index=index)
    return AgentWorkflow.from_tools_or_functions(
        tools_or_functions=[query_tool],
-        llm=Settings.llm,
-        system_prompt=system_prompt,
+        llm=Settings.llm or OpenAI(model="gpt-4o-mini"),
+        system_prompt="You are a helpful assistant.",
    )
@@ -23,18 +23,7 @@ from llama_index.core.workflow import (
    Workflow,
    step,
 )
-from llama_index.server.api.models import (
-    ArtifactEvent,
-    ArtifactType,
-    ChatRequest,
-    SourceNodesEvent,
-    UIEvent,
-    Artifact,
-    DocumentArtifactData,
-    DocumentArtifactSource,
-)
-import time
-from llama_index.server.utils.stream import write_response_to_stream
+from llama_index.server.api.models import ChatRequest, SourceNodesEvent, UIEvent
 from pydantic import BaseModel, Field

 logger = logging.getLogger("uvicorn")
@@ -376,31 +365,8 @@ class DeepResearchWorkflow(Workflow):
            user_request=self.user_request,
            stream=self.stream,
        )
-
-        final_response = await write_response_to_stream(res, ctx)
-
-        ctx.write_event_to_stream(
-            ArtifactEvent(
-                data=Artifact(
-                    type=ArtifactType.DOCUMENT,
-                    created_at=int(time.time()),
-                    data=DocumentArtifactData(
-                        title="DeepResearch Report",
-                        content=final_response,
-                        type="markdown",
-                        sources=[
-                            DocumentArtifactSource(
-                                id=node.id_,
-                            )
-                            for node in self.context_nodes
-                        ],
-                    ),
-                ),
-            )
-        )
-
        return StopEvent(
-            result="",
+            result=res,
        )


@@ -1,109 +0,0 @@
-This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Workflows](https://docs.llamaindex.ai/en/stable/understanding/workflows/).
-
-## Getting Started
-
-First, setup the environment with uv:
-
-> **_Note:_** This step is not needed if you are using the dev-container.
-
-```shell
-uv sync
-```
-
-Then check the parameters that have been pre-configured in the `.env` file in this directory.
-Make sure you have set the `OPENAI_API_KEY` for the LLM.
-
-Then, run the development server:
-
-```shell
-uv run fastapi dev
-```
-
-Then open [http://localhost:8000](http://localhost:8000) with your browser to start the chat UI.
-
-To start the app optimized for **production**, run:
-
-```
-uv run fastapi run
-```
-
-## Configure LLM and Embedding Model
-
-You can configure [LLM model](https://docs.llamaindex.ai/en/stable/module_guides/models/llms) and [embedding model](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings) in [settings.py](app/settings.py).
-
-## Use Case
-
-This example shows how to use the LlamaIndexServer with a human in the loop. It allows you to start CLI commands that are reviewed by a human before execution.
-
-To update the workflow, you can modify the code in [`workflow.py`](app/workflow.py).
-
-You can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:
-
-```
-curl --location 'localhost:8000/api/chat' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Show me the files in the current directory" }] }'
-```
-
-## How does HITL work?
-
-### Events
-
-The human-in-the-loop approach used here is based on a simple idea: the workflow pauses and waits for a human response before proceeding to the next step.
-
-To do this, you will need to implement two custom events:
-
- [HumanInputEvent](https://github.com/run-llama/create-llama/blob/main/packages/server/src/utils/hitl/events.ts): This event is used to request input from the user.
- [HumanResponseEvent](https://github.com/run-llama/create-llama/blob/main/packages/server/src/utils/hitl/events.ts): This event is sent to the workflow to resume execution with input from the user.
-
-In this example, we have implemented these two custom events in [`events.ts`](src/app/events.ts):
-
- `cliHumanInputEvent` – to request input from the user for CLI command execution.
- `cliHumanResponseEvent` – to resume the workflow with the response from the user.
-
-```typescript
-export const cliHumanInputEvent = humanInputEvent<{
-  type: "cli_human_input";
-  data: { command: string };
-  response: typeof cliHumanResponseEvent;
-}>();
-
-export const cliHumanResponseEvent = humanResponseEvent<{
-  type: "human_response";
-  data: { execute: boolean; command: string };
-}>();
-```
-
-### UI Component
-
-HITL also needs a custom UI component, that is shown when the LlamaIndexServer receives the `cliHumanInputEvent`. The name of the component is defined in the `type` field of the `cliHumanInputEvent` - in our case, it is `cli_human_input`, which corresponds to the [cli_human_input.tsx](./components/cli_human_input.tsx) component.
-
-The custom component must use `append` to send a message with a `human_response` annotation. The data of the annotation must be in the format of the response event `cliHumanResponseEvent`, in our case, for sending to execute the command `ls -l`, we would send:
-
-```tsx
-append({
-  content: "Yes",
-  role: "user",
-  annotations: [
-    {
-      type: "human_response",
-      data: {
-        execute: true,
-        command: "ls -l", // The command to execute
-      },
-    },
-  ],
-});
-```
-
-This component displays the command to execute and the user can choose to execute or cancel the command execution.
-
-## Learn More
-
-To learn more about LlamaIndex, take a look at the following resources:
-
- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
- [Workflows Introduction](https://docs.llamaindex.ai/en/stable/understanding/workflows/) - learn about LlamaIndex workflows.
- [LlamaIndex Server](https://pypi.org/project/llama-index-server/)
-
-You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!
@@ -1,34 +0,0 @@
-from typing import Type
-
-from pydantic import BaseModel, Field
-
-from llama_index.server.models import HumanInputEvent, HumanResponseEvent
-
-
-class CLIHumanResponseEvent(HumanResponseEvent):
-    execute: bool = Field(
-        description="True if the human wants to execute the command, False otherwise."
-    )
-    command: str = Field(description="The command to execute.")
-
-
-class CLICommand(BaseModel):
-    command: str = Field(description="The command to execute.")
-
-
-# We need an event that extends from HumanInputEvent for HITL feature
-class CLIHumanInputEvent(HumanInputEvent):
-    """
-    CLIInputRequiredEvent is sent when the agent needs permission from the user to execute the CLI command or not.
-    Render this event by showing the command and a boolean button to execute the command or not.
-    """
-
-    event_type: str = (
-        "cli_human_input"  # used by UI to render with appropriate component
-    )
-    response_event_type: Type = (
-        CLIHumanResponseEvent  # used by workflow to resume with the correct event
-    )
-    data: CLICommand = Field(  # the data that sent to the UI for rendering
-        description="The command to execute.",
-    )
@@ -1,87 +0,0 @@
-import platform
-import subprocess
-from typing import Any
-
-from app.events import CLICommand, CLIHumanInputEvent, CLIHumanResponseEvent
-
-from llama_index.core.prompts import PromptTemplate
-from llama_index.core.settings import Settings
-from llama_index.core.workflow import (
-    Context,
-    StartEvent,
-    StopEvent,
-    Workflow,
-    step,
-)
-
-
-def create_workflow() -> Workflow:
-    return CLIWorkflow()
-
-
-class CLIWorkflow(Workflow):
-    """
-    A workflow has ability to execute command line tool with human in the loop for confirmation.
-    """
-
-    default_prompt = PromptTemplate(
-        template="""
-        You are a helpful assistant who can write CLI commands to execute using {cli_language}.
-        Your task is to analyze the user's request and write a CLI command to execute.
-
-        ## User Request
-        {user_request}
-
-        Don't be verbose, only respond with the CLI command without any other text.
-        """
-    )
-
-    def __init__(self, **kwargs: Any) -> None:
-        # HITL Workflow should disable timeout otherwise, we will get a timeout error from callback
-        kwargs["timeout"] = None
-        super().__init__(**kwargs)
-
-    @step
-    async def start(self, ctx: Context, ev: StartEvent) -> CLIHumanInputEvent:
-        user_msg = ev.user_msg
-        if user_msg is None:
-            raise ValueError("Missing user_msg in StartEvent")
-        await ctx.set("user_msg", user_msg)
-        # Request LLM to generate a CLI command
-        os_name = platform.system()
-        if os_name == "Linux" or os_name == "Darwin":
-            cli_language = "bash"
-        else:
-            cli_language = "cmd"
-        prompt = self.default_prompt.format(
-            user_request=user_msg, cli_language=cli_language
-        )
-        llm = Settings.llm
-        if llm is None:
-            raise ValueError("Missing LLM in Settings")
-        response = await llm.acomplete(prompt, formatted=True)
-        command = response.text.strip()
-        if command == "":
-            raise ValueError("Couldn't generate a command")
-        # Send the command to the user for confirmation
-        await ctx.set("command", command)
-        return CLIHumanInputEvent(  # type: ignore
-            data=CLICommand(command=command),
-            response_event_type=CLIHumanResponseEvent,
-        )
-
-    @step
-    async def handle_human_response(
-        self,
-        ctx: Context,
-        ev: CLIHumanResponseEvent,  # This event is sent by LlamaIndexServer when user response
-    ) -> StopEvent:
-        # If we have human response, check the confirmation and execute the command
-        if ev.execute:
-            command = ev.command or ""
-            if command == "":
-                raise ValueError("Missing command in CLIExecutionEvent")
-            res = subprocess.run(command, shell=True, capture_output=True, text=True)
-            return StopEvent(result=res.stdout or res.stderr)
-        else:
-            return StopEvent(result=None)
@@ -41,14 +41,6 @@ curl --location 'localhost:3000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "What standards for a letter exist?" }] }'
 ```

-## Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-
-```bash
-npm run eject
-```
-
 ## Learn More

 To learn more about LlamaIndex, take a look at the following resources:
@@ -8,7 +8,7 @@ First, install the dependencies:
 npm install
 ```

-Second, run the development server:
+Third, run the development server:

 ```
 npm run dev
@@ -34,7 +34,7 @@ AI-powered code generator that can help you generate app with a chat interface,

 To update the workflow, you can modify the code in [`workflow.ts`](app/workflow.ts).

-You can start by sending a request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:
+You can start by sending an request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:

 ```shell
 curl --location 'localhost:3000/api/chat' \
@@ -42,14 +42,6 @@ curl --location 'localhost:3000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "Compare the financial performance of Apple and Tesla" }] }'
 ```

-## Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-
-```bash
-npm run eject
-```
-
 ## Learn More

 To learn more about LlamaIndex, take a look at the following resources:
@@ -1,5 +1,5 @@
-import { artifactEvent, extractLastArtifact } from "@llamaindex/server";
-import { ChatMemoryBuffer, MessageContent, Settings } from "llamaindex";
+import { extractLastArtifact } from "@llamaindex/server";
+import { ChatMemoryBuffer, LLM, MessageContent, Settings } from "llamaindex";

 import {
  agentStreamEvent,
@@ -12,6 +12,12 @@ import {

 import { z } from "zod";

+export const workflowFactory = async (reqBody: any) => {
+  const workflow = createCodeArtifactWorkflow(reqBody);
+
+  return workflow;
+};
+
 export const RequirementSchema = z.object({
  next_step: z.enum(["answering", "coding"]),
  language: z.string().nullable().optional(),
@@ -52,33 +58,47 @@ const synthesizeAnswerEvent = workflowEvent<object>();

 const uiEvent = workflowEvent<UIEvent>();

-export function workflowFactory(reqBody: any) {
-  const llm = Settings.llm;
+const artifactEvent = workflowEvent<{
+  type: "artifact";
+  data: {
+    type: "code";
+    created_at: number;
+    data: {
+      language: string;
+      file_name: string;
+      code: string;
+    };
+  };
+}>();

+export function createCodeArtifactWorkflow(reqBody: any, llm?: LLM) {
+  if (!llm) {
+    llm = Settings.llm;
+  }
  const { withState, getContext } = createStatefulMiddleware(() => {
    return {
-      memory: new ChatMemoryBuffer({ llm }),
+      memory: new ChatMemoryBuffer({
+        llm,
+        chatHistory: reqBody.chatHistory,
+      }),
      lastArtifact: extractLastArtifact(reqBody),
    };
  });
  const workflow = withState(createWorkflow());

-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { userInput, chatHistory = [] } = data;
+  workflow.handle([startAgentEvent], async ({ data: { userInput } }) => {
    // Prepare chat history
    const { state } = getContext();
    // Put user input to the memory
    if (!userInput) {
      throw new Error("Missing user input to start the workflow");
    }
-    state.memory.set(chatHistory);
-    state.memory.put({ role: "user", content: userInput });
-
+    state.memory.put({
+      role: "user",
+      content: userInput,
+    });
    return planEvent.with({
      userInput: userInput,
-      context: state.lastArtifact
-        ? JSON.stringify(state.lastArtifact)
-        : undefined,
    });
  });

@@ -53,14 +53,6 @@ curl --location 'localhost:3000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "Compare the financial performance of Apple and Tesla" }] }'
 ```

-## Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-
-```bash
-npm run eject
-```
-
 ## Learn More

 To learn more about LlamaIndex, take a look at the following resources:
@@ -1,4 +1,4 @@
-import { artifactEvent, toSourceEvent } from "@llamaindex/server";
+import { toSourceEvent } from "@llamaindex/server";
 import {
  agentStreamEvent,
  createStatefulMiddleware,
@@ -339,26 +339,6 @@ export function getWorkflow(index: VectorStoreIndex | LlamaCloudIndex) {
        }),
      );
    }
-
-    // Open the generated report in Canvas
-    sendEvent(
-      artifactEvent.with({
-        type: "artifact",
-        data: {
-          type: "document",
-          created_at: Date.now(),
-          data: {
-            title: "DeepResearch Report",
-            content: response,
-            type: "markdown",
-            sources: state.contextNodes.map((node) => ({
-              id: node.node.id_,
-            })),
-          },
-        },
-      }),
-    );
-
    return stopAgentEvent.with({
      result: response,
    });
@@ -8,7 +8,7 @@ First, install the dependencies:
 npm install
 ```

-Second, run the development server:
+Third, run the development server:

 ```
 npm run dev
@@ -34,7 +34,7 @@ AI-powered document generator that can help you generate documents with a chat i

 To update the workflow, you can modify the code in [`workflow.ts`](app/workflow.ts).

-You can start by sending a request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:
+You can start by sending an request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:

 ```shell
 curl --location 'localhost:3000/api/chat' \
@@ -42,14 +42,6 @@ curl --location 'localhost:3000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "Compare the financial performance of Apple and Tesla" }] }'
 ```

-## Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-
-```bash
-npm run eject
-```
-
 ## Learn More

 To learn more about LlamaIndex, take a look at the following resources:
@@ -1,5 +1,5 @@
-import { artifactEvent, extractLastArtifact } from "@llamaindex/server";
-import { ChatMemoryBuffer, MessageContent, Settings } from "llamaindex";
+import { extractLastArtifact } from "@llamaindex/server";
+import { ChatMemoryBuffer, LLM, MessageContent, Settings } from "llamaindex";

 import {
  agentStreamEvent,
@@ -12,6 +12,12 @@ import {

 import { z } from "zod";

+export const workflowFactory = async (reqBody: any) => {
+  const workflow = createDocumentArtifactWorkflow(reqBody);
+
+  return workflow;
+};
+
 export const DocumentRequirementSchema = z.object({
  type: z.enum(["markdown", "html"]),
  title: z.string(),
@@ -55,28 +61,45 @@ const synthesizeAnswerEvent = workflowEvent<{

 const uiEvent = workflowEvent<UIEvent>();

-export function workflowFactory(reqBody: any) {
-  const llm = Settings.llm;
+const artifactEvent = workflowEvent<{
+  type: "artifact";
+  data: {
+    type: "document";
+    created_at: number;
+    data: {
+      title: string;
+      content: string;
+      type: "markdown" | "html";
+    };
+  };
+}>();

+export function createDocumentArtifactWorkflow(reqBody: any, llm?: LLM) {
+  if (!llm) {
+    llm = Settings.llm;
+  }
  const { withState, getContext } = createStatefulMiddleware(() => {
    return {
-      memory: new ChatMemoryBuffer({ llm }),
+      memory: new ChatMemoryBuffer({
+        llm,
+        chatHistory: reqBody.chatHistory,
+      }),
      lastArtifact: extractLastArtifact(reqBody),
    };
  });
  const workflow = withState(createWorkflow());

-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { userInput, chatHistory = [] } = data;
+  workflow.handle([startAgentEvent], async ({ data: { userInput } }) => {
    // Prepare chat history
    const { state } = getContext();
    // Put user input to the memory
    if (!userInput) {
      throw new Error("Missing user input to start the workflow");
    }
-    state.memory.set(chatHistory);
-    state.memory.put({ role: "user", content: userInput });
-
+    state.memory.put({
+      role: "user",
+      content: userInput,
+    });
    return planEvent.with({
      userInput,
      context: state.lastArtifact
@@ -41,14 +41,6 @@ curl --location 'localhost:3000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "Generate a financial report that compares the financial performance of Apple and Tesla" }] }'
 ```

-## Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-
-```bash
-npm run eject
-```
-
 ## Learn More

 To learn more about LlamaIndex, take a look at the following resources:
@@ -1,106 +0,0 @@
-This is a [LlamaIndex](https://www.llamaindex.ai/) project bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
-
-## Getting Started
-
-First, install the dependencies:
-
-```
-npm install
-```
-
-Second, run the development server:
-
-```
-npm run dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the chat UI.
-
-## Configure LLM and Embedding Model
-
-You can configure [LLM model](https://ts.llamaindex.ai/docs/llamaindex/modules/llms) in the [settings file](src/app/settings.ts).
-
-## Use Case
-
-This example shows how to use the LlamaIndexServer with a human in the loop. It allows you to start CLI commands that are reviewed by a human before execution.
-
-To update the workflow, you can modify the code in [`workflow.py`](app/workflow.py).
-
-You can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:
-
-```
-curl --location 'localhost:8000/api/chat' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Show me the files in the current directory" }] }'
-```
-
-## How does HITL work?
-
-### Events
-
-The human-in-the-loop approach used here is based on a simple idea: the workflow pauses and waits for a human response before proceeding to the next step.
-
-To do this, you will need to implement two custom events:
-
- [HumanInputEvent](https://github.com/run-llama/create-llama/blob/main/packages/server/src/utils/hitl/events.ts): This event is used to request input from the user.
- [HumanResponseEvent](https://github.com/run-llama/create-llama/blob/main/packages/server/src/utils/hitl/events.ts): This event is sent to the workflow to resume execution with input from the user.
-
-In this example, we have implemented these two custom events in [`events.ts`](src/app/events.ts):
-
- `cliHumanInputEvent` – to request input from the user for CLI command execution.
- `cliHumanResponseEvent` – to resume the workflow with the response from the user.
-
-```typescript
-export const cliHumanInputEvent = humanInputEvent<{
-  type: "cli_human_input";
-  data: { command: string };
-  response: typeof cliHumanResponseEvent;
-}>();
-
-export const cliHumanResponseEvent = humanResponseEvent<{
-  type: "human_response";
-  data: { execute: boolean; command: string };
-}>();
-```
-
-### UI Component
-
-HITL also needs a custom UI component, that is shown when the LlamaIndexServer receives the `cliHumanInputEvent`. The name of the component is defined in the `type` field of the `cliHumanInputEvent` - in our case, it is `cli_human_input`, which corresponds to the [cli_human_input.tsx](./components/cli_human_input.tsx) component.
-
-The custom component must use `append` to send a message with a `human_response` annotation. The data of the annotation must be in the format of the response event `cliHumanResponseEvent`, in our case, for sending to execute the command `ls -l`, we would send:
-
-```tsx
-append({
-  content: "Yes",
-  role: "user",
-  annotations: [
-    {
-      type: "human_response",
-      data: {
-        execute: true,
-        command: "ls -l", // The command to execute
-      },
-    },
-  ],
-});
-```
-
-This component displays the command to execute and the user can choose to execute or cancel the command execution.
-
-## Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-
-```bash
-npm run eject
-```
-
-## Learn More
-
-To learn more about LlamaIndex, take a look at the following resources:
-
- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
- [LlamaIndexTS Documentation](https://ts.llamaindex.ai/docs/llamaindex) - learn about LlamaIndex (Typescript features).
- [Workflows Introduction](https://ts.llamaindex.ai/docs/llamaindex/modules/workflows) - learn about LlamaIndexTS workflows.
-
-You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!
@@ -1,12 +0,0 @@
-import { humanInputEvent, humanResponseEvent } from "@llamaindex/server";
-
-export const cliHumanInputEvent = humanInputEvent<{
-  type: "cli_human_input";
-  data: { command: string };
-  response: typeof cliHumanResponseEvent;
-}>();
-
-export const cliHumanResponseEvent = humanResponseEvent<{
-  type: "human_response";
-  data: { execute: boolean; command: string };
-}>();
@@ -1,20 +0,0 @@
-import { execSync } from "child_process";
-import { tool } from "llamaindex";
-import { z } from "zod";
-
-export const cliExecutor = tool({
-  name: "cli_executor",
-  description: "This tool executes a command and returns the output.",
-  parameters: z.object({ command: z.string() }),
-  execute: async ({ command }) => {
-    try {
-      const output = execSync(command, {
-        encoding: "utf-8",
-      });
-      return output;
-    } catch (error) {
-      console.error(error);
-      return "Command failed";
-    }
-  },
-});
@@ -1,101 +0,0 @@
-import { toAgentRunEvent, writeResponseToStream } from "@llamaindex/server";
-import { chatWithTools } from "@llamaindex/tools";
-import {
-  createWorkflow,
-  getContext,
-  startAgentEvent,
-  stopAgentEvent,
-  withSnapshot,
-  workflowEvent,
-} from "@llamaindex/workflow";
-import { ChatMessage, Settings, ToolCallLLM } from "llamaindex";
-import { cliHumanInputEvent, cliHumanResponseEvent } from "./events";
-import { cliExecutor } from "./tools";
-
-const summaryEvent = workflowEvent<string>(); // simple event to summarize the result
-
-export const workflowFactory = (body: unknown) => {
-  const llm = Settings.llm as ToolCallLLM;
-
-  if (!llm.supportToolCall) {
-    throw new Error("LLM is not a ToolCallLLM");
-  }
-
-  const { messages } = body as { messages: ChatMessage[] };
-
-  const workflow = withSnapshot(createWorkflow());
-
-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { userInput, chatHistory = [] } = data;
-    if (!userInput) {
-      throw new Error("User input is required");
-    }
-
-    // in this example, we use chatWithTools to decide should perform a tool call or not
-    // if cli executor is called, emit HumanInputEvent to ask user for permission
-    const toolCallResponse = await chatWithTools(
-      llm,
-      [cliExecutor],
-      chatHistory.concat({ role: "user", content: userInput }),
-    );
-    const cliExecutorToolCall = toolCallResponse.toolCalls.find(
-      (toolCall) => toolCall.name === cliExecutor.metadata.name,
-    );
-    const command = cliExecutorToolCall?.input?.command as string;
-    if (command) {
-      return cliHumanInputEvent.with({
-        type: "cli_human_input",
-        data: { command },
-        response: cliHumanResponseEvent,
-      });
-    }
-
-    // if no tool call, just response as normal
-    return summaryEvent.with("");
-  });
-
-  // do actions after getting response from human
-  workflow.handle([cliHumanResponseEvent], async ({ data }) => {
-    const { sendEvent } = getContext();
-    const { command, execute } = data.data;
-
-    if (!execute) {
-      // stop the workflow if user reject to execute the command
-      return summaryEvent.with(`User reject to execute the command ${command}`);
-    }
-
-    sendEvent(
-      toAgentRunEvent({
-        agent: "CLI Executor",
-        text: `Execute the command "${command}" and return the result`,
-        type: "text",
-      }),
-    );
-
-    const result = (await cliExecutor.call({ command })) as string;
-
-    return summaryEvent.with(
-      `Executed the command ${command} and got the result: ${result}`,
-    );
-  });
-
-  workflow.handle([summaryEvent], async ({ data: summaryResult }) => {
-    const { sendEvent } = getContext();
-
-    const chatHistory = messages;
-    if (summaryResult) {
-      chatHistory.push({ role: "user", content: summaryResult });
-    }
-
-    const stream = await llm.chat({
-      messages: chatHistory,
-      stream: true,
-    });
-
-    const result = await writeResponseToStream(stream, sendEvent);
-
-    return stopAgentEvent.with({ result });
-  });
-
-  return workflow;
-};
@@ -12,12 +12,11 @@ from llama_index.server.services.llamacloud.generate import (
    load_to_llamacloud,
 )

-
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger()


-def generate_index():
+def generate_datasource():
    init_settings()
    logger.info("Generate index for the provided data")

@@ -28,26 +27,5 @@ def generate_index():
    load_to_llamacloud(index, logger=logger)


-def generate_ui_for_workflow():
-    """
-    Generate UI for UIEventData event in app/workflow.py
-    """
-    import asyncio
-    from llama_index.llms.openai import OpenAI
-    from main import COMPONENT_DIR
-
-    # To generate UI components for additional event types,
-    # import the corresponding data model (e.g., MyCustomEventData)
-    # and run the generate_ui_for_workflow function with the imported model.
-    # Make sure the output filename of the generated UI component matches the event type (here `ui_event`)
-    try:
-        from app.workflow import UIEventData  # type: ignore
-    except ImportError:
-        raise ImportError("Couldn't generate UI component for the current workflow.")
-    from llama_index.server.gen_ui import generate_event_component
-
-    # works also well with Claude 3.7 Sonnet or Gemini Pro 2.5
-    llm = OpenAI(model="gpt-4.1")
-    code = asyncio.run(generate_event_component(event_cls=UIEventData, llm=llm))
-    with open(f"{COMPONENT_DIR}/ui_event.jsx", "w") as f:
-        f.write(code)
+if __name__ == "__main__":
+    generate_datasource()
@@ -1,5 +1,3 @@
-import { OpenAI } from "@llamaindex/openai";
-import { generateEventComponent } from "@llamaindex/server";
 import * as dotenv from "dotenv";
 import "dotenv/config";
 import * as fs from "fs/promises";
@@ -90,7 +88,7 @@ async function loadAndIndex() {
  console.log(`Successfully uploaded documents to LlamaCloud!`);
 }

-async function generateDatasource() {
+(async () => {
  try {
    checkRequiredEnvVars();
    initSettings();
@@ -99,39 +97,4 @@ async function generateDatasource() {
  } catch (error) {
    console.error("Error generating storage.", error);
  }
-}
-
-async function generateUi() {
-  // Also works well with Claude 3.5 Sonnet and Google Gemini 2.5 Pro
-  const llm = new OpenAI({ model: "gpt-4.1" });
-
-  const workflowModule = await import("./app/workflow");
-  const UIEventSchema = (workflowModule as any).UIEventSchema;
-  if (!UIEventSchema) {
-    throw new Error(
-      "To generate the UI, you must define a UIEventSchema in your workflow.",
-    );
-  }
-
-  const generatedCode = await generateEventComponent(UIEventSchema, llm);
-  // Write the generated code to components/ui_event.ts
-  await fs.writeFile("components/ui_event.jsx", generatedCode);
-}
-
-(async () => {
-  const args = process.argv.slice(2);
-  const command = args[0];
-
-  initSettings();
-
-  if (command === "datasource") {
-    await generateDatasource();
-  } else if (command === "ui") {
-    await generateUi();
-  } else {
-    console.error(
-      'Invalid command. Please use "datasource" or "ui". Running "datasource" by default.',
-    );
-    await generateDatasource(); // Default behavior or could throw an error
-  }
 })();
@@ -1,12 +1,8 @@
-import os
-
 from llama_index.core import Settings
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.llms.openai import OpenAI


 def init_settings():
-    if os.getenv("OPENAI_API_KEY") is None:
-        raise RuntimeError("OPENAI_API_KEY is missing in environment variables")
-    Settings.llm = OpenAI(model="gpt-4.1")
-    Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-large")
+    Settings.llm = OpenAI(model="gpt-4o-mini")
+    Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
@@ -16,11 +16,9 @@ def create_app():
        workflow_factory=create_workflow,  # A factory function that creates a new workflow for each request
        ui_config=UIConfig(
            component_dir=COMPONENT_DIR,
-            dev_mode=True,  # Please disable this in production
-            layout_dir="layout",
+            app_title="Chat App",
        ),
        logger=logger,
-        env="dev",
    )
    # You can also add custom FastAPI routes to app
    app.add_api_route("/api/health", lambda: {"message": "OK"}, status_code=200)
@@ -12,7 +12,7 @@ dependencies = [
    "pydantic<2.10",
    "aiostream>=0.5.2,<0.6.0",
    "llama-index-core>=0.12.28,<0.13.0",
-    "llama-index-server>=0.1.17,<0.2.0",
+    "llama-index-server>=0.1.15,<0.2.0",
 ]

 [project.optional-dependencies]
@@ -46,9 +46,6 @@ disable_error_code = [ "return-value", "assignment" ]
 module = "app.*"
 ignore_missing_imports = false

-[tool.hatch.metadata]
-allow-direct-references = true
-
 [build-system]
 requires = [ "hatchling>=1.24" ]
-build-backend = "hatchling.build"
+build-backend = "hatchling.build"
@@ -1,6 +0,0 @@
-{
-  "watch": ["src/**/*.ts"],
-  "exec": "nodemon --exec tsx src/index.ts",
-  "ext": "js ts",
-  "ignore": ["src/app/workflow_*.ts"]
-}
@@ -5,23 +5,21 @@
    "generate": "tsx src/generate.ts datasource",
    "generate:datasource": "tsx src/generate.ts datasource",
    "generate:ui": "tsx src/generate.ts ui",
-    "dev": "nodemon",
-    "start": "tsx src/index.ts",
-    "eject": "llamaindex-server eject"
+    "dev": "tsx watch src/index.ts",
+    "start": "tsx src/index.ts"
  },
  "dependencies": {
-    "@llamaindex/openai": "~0.4.0",
-    "@llamaindex/server": "~0.2.1",
-    "@llamaindex/workflow": "~1.1.8",
-    "@llamaindex/tools": "~0.0.11",
-    "llamaindex": "~0.11.0",
+    "@llamaindex/openai": "^0.3.7",
+    "@llamaindex/server": "^0.2.0",
+    "@llamaindex/workflow": "^1.1.2",
+    "@llamaindex/tools": "^0.0.10",
+    "llamaindex": "^0.10.6",
    "dotenv": "^16.4.7",
    "zod": "^3.23.8"
  },
  "devDependencies": {
    "@types/node": "^20.10.3",
-    "tsx": "4.7.2",
-    "typescript": "^5.3.2",
-    "nodemon": "^3.1.10"
+    "tsx": "^4.7.2",
+    "typescript": "^5.3.2"
  }
 }
@@ -1,10 +1,14 @@
 import "dotenv/config";
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
+import {
+  OpenAI,
+  storageContextFromDefaults,
+  VectorStoreIndex,
+} from "llamaindex";
 import { initSettings } from "./app/settings";
 import fs from "fs";
 import { generateEventComponent } from "@llamaindex/server";
-import { OpenAI } from "@llamaindex/openai";
+import { UIEventSchema } from "./app/workflow";

 async function generateDatasource() {
  console.log(`Generating storage context...`);
@@ -26,14 +30,6 @@ async function generateUi() {
  // Also works well with Claude 3.5 Sonnet and Google Gemini 2.5 Pro
  const llm = new OpenAI({ model: "gpt-4.1" });

-  const workflowModule = await import("./app/workflow");
-  const UIEventSchema = (workflowModule as any).UIEventSchema;
-  if (!UIEventSchema) {
-    throw new Error(
-      "To generate the UI, you must define a UIEventSchema in your workflow.",
-    );
-  }
-
  // You can also generate for other workflow events
  const generatedCode = await generateEventComponent(UIEventSchema, llm);
  // Write the generated code to components/ui_event.ts
@@ -8,7 +8,7 @@ initSettings();
 new LlamaIndexServer({
  workflow: workflowFactory,
  uiConfig: {
+    appTitle: "LlamaIndex App",
    componentsDir: "components",
-    devMode: true,
  },
 }).start();
@@ -1,8 +1 @@
-# server contains Nextjs frontend code (not compiled)
-server/
-
-# the ejected nextjs project
-project/
-
-# temp is the copy of next folder but without API folder, used to build frontend static files
-temp/
+server/
@@ -1,61 +1,5 @@
 # @llamaindex/server

-## 0.2.8
-
-### Patch Changes
-
- e2486eb: feat: support human in the loop for TS
-
-## 0.2.7
-
-### Patch Changes
-
- af9ad3c: feat: show document artifact after generating report
- a543a27: feat: bump chat-ui with inline artifact
- 1ff6eaf: Add support for chat upload file
-
-## 0.2.6
-
-### Patch Changes
-
- 3ff0a18: fix: default header padding
- df10474: fix: missing cursor pointer for button
- 087c961: Support zod and chat-ui hooks for custom components
-
-## 0.2.5
-
-### Patch Changes
-
- 058b376: Fix generate script for ejected project
-
-## 0.2.4
-
-### Patch Changes
-
- 5fe9e17: support eject to fully customize next folder
- b8a1ff6: Bump version: chat-ui@0.4.6
-
-## 0.2.3
-
-### Patch Changes
-
- eee3230: feat: support custom layout
- 0bc5a0d: Add suggestNextQuestions config
- 3acec88: chore: bump chat-ui
-
-## 0.2.2
-
-### Patch Changes
-
- 25fba43: refactor: migrate to Nextjs Route Handler
- 6f75d4a: fix: unsupported language in code gen workflow
-
-## 0.2.1
-
-### Patch Changes
-
- f072308: feat: add dev mode UI
-
 ## 0.2.0

 ### Minor Changes
@@ -1,160 +0,0 @@
-# @llamaindex/server Package
-
-This package provides a Next.js-based server framework for running LlamaIndex workflows with both API endpoints and a chat UI interface.
-
-## Overview
-
-The `@llamaindex/server` package (`src/`) allows you to quickly launch LlamaIndex Workflows and Agent Workflows as an API server with an optional sophisticated chat UI. It combines a backend API server with a frontend React interface built on Next.js.
-
-## Key Components
-
-### Core Server (src/server.ts)
-
- **LlamaIndexServer class**: Main server implementation that wraps Next.js
- Handles workflow factory initialization and UI configuration
- Manages custom components and layout directories
- Creates HTTP server with custom routing for chat API
- Automatically configures client-side config in `public/config.js`
-
-### Chat Handler (src/handlers/chat.ts)
-
- **handleChat function**: Processes POST requests to `/api/chat`
- Converts AI SDK messages to LlamaIndex format
- Manages workflow execution with abort signals
- Streams responses back to client with optional question suggestions
- Handles errors and validation
-
-### Workflow Management (src/utils/workflow.ts)
-
- **runWorkflow function**: Executes workflows with proper event handling
- Transforms workflow events (tool calls, source nodes) into UI-friendly formats
- Downloads LlamaCloud files automatically in background
- Processes agent events and source annotations
-
-### Event System (src/events.ts)
-
- **Source Events**: For displaying document/file sources with metadata
- **Agent Events**: For showing agent tool usage and progress
- **Artifact Events**: For structured data like code/documents sent to Canvas UI
- Helper functions for converting LlamaIndex data to UI events
-
-### UI Generation (src/utils/gen-ui.ts)
-
- **generateEventComponent function**: Uses LLM to auto-generate React components
- Creates workflow for UI planning, aggregation, and code generation
- Validates generated components against supported dependencies
- Supports shadcn/ui, lucide-react, tailwind CSS, and LlamaIndex chat-ui
-
-### Types (src/types.ts)
-
- **WorkflowFactory**: Function signature for creating workflow instances
- **UIConfig**: Configuration options for chat interface
- **LlamaIndexServerOptions**: Main server configuration interface
-
-## Next.js Frontend
-
-The `next/` directory contains the React frontend:
-
-### API Routes
-
- `/api/chat/route.ts`: Main chat endpoint (delegates to handleChat)
- `/api/components/route.ts`: Serves custom UI components
- `/api/layout/route.ts`: Serves custom layout components
- `/api/files/[...slug]/route.ts`: File serving for data/output folders
-
-### UI Components
-
- Chat interface with message history, streaming responses, and canvas panel
- Extensible component system for custom workflow events
- Custom layout support for headers/footers
- Built with shadcn/ui components and Tailwind CSS
-
-## Build Process
-
-### Development
-
-```bash
-pnpm dev  # Watch mode with bunchee
-```
-
-### Production Build
-
-```bash
-pnpm build  # Multi-step build process
-```
-
-The build process:
-
-1. **prebuild**: Cleans dist, server, and temp directories
-2. **build**: Compiles source with bunchee to ESM/CJS
-3. **postbuild**: Prepares TypeScript server and Python static assets
-4. **prepare:ts-server**: Copies Next.js app, builds CSS, compiles API routes
-5. **prepare:py-static**: Creates static build for Python integration
-
-## Key Features
-
-### Workflow Integration
-
- Factory pattern for creating workflow instances per request
- Supports Agent Workflows with startAgentEvent/stopAgentEvent contract
- Automatic event transformation and streaming
- Built-in tool call and source node handling
-
-### UI Extensibility
-
- AI-generated components based on Zod schemas
- Custom layout sections (header/footer)
- Canvas panel for artifacts (documents, code)
- Event aggregation and real-time updates
-
-### File Handling
-
- Automatic mounting of `data/` and `output/` folders
- LlamaCloud file downloads in background
- Static asset serving through Next.js
-
-### Development Features
-
- Hot reload support for workflow code (beta)
- Dev mode panel for live code editing
- TypeScript support throughout
- Comprehensive error handling
-
-## Configuration
-
-Server configuration through `LlamaIndexServerOptions`:
-
- `workflow`: Factory function for creating workflow instances
- `uiConfig.starterQuestions`: Predefined questions for chat interface
- `uiConfig.componentsDir`: Directory for custom event components
- `uiConfig.layoutDir`: Directory for custom layout components
- `uiConfig.llamaCloudIndexSelector`: Enable LlamaCloud integration
- `uiConfig.devMode`: Enable live code editing
- `suggestNextQuestions`: Auto-suggest follow-up questions
-
-## Dependencies
-
-### Runtime Dependencies
-
- Next.js 15+ for server framework
- React 19+ for UI components
- LlamaIndex workflow engine
- Radix UI components (shadcn/ui)
- AI SDK for streaming responses
-
-### Development Dependencies
-
- Bunchee for bundling
- TypeScript for type safety
- Tailwind CSS for styling
- PostCSS for CSS processing
-
-## Usage Patterns
-
-1. **Basic Setup**: Create workflow factory, configure UI, start server
-2. **Custom Events**: Define Zod schemas, generate UI components with LLM
-3. **File Integration**: Use data/output folders for document processing
-4. **Development**: Use dev mode for iterative workflow development
-5. **Production**: Build static assets for deployment with Python backend
-
-The package serves as a complete solution for deploying LlamaIndex workflows with professional chat interfaces and extensible UI components.
@@ -4,11 +4,10 @@ LlamaIndexServer is a Next.js-based application that allows you to quickly launc

 ## Features

- Add a sophisticated chatbot UI to your LlamaIndex workflow
- Edit code and document artifacts in an OpenAI Canvas-style UI
- Extendable UI components for events and headers
+- Serving a workflow as a chatbot
 - Built on Next.js for high performance and easy API development
- Human-in-the-loop (HITL) support, check out the [Human-in-the-loop](https://github.com/run-llama/create-llama/blob/main/packages/server/examples/hitl/README.md) documentation for more details.
+- Optional built-in chat UI with extendable UI components
+- Prebuilt development code

 ## Installation

@@ -22,22 +21,19 @@ Create an `index.ts` file and add the following code:

 ```ts
 import { LlamaIndexServer } from "@llamaindex/server";
-import { openai } from "@llamaindex/openai";
-import { agent } from "@llamaindex/workflow";
 import { wiki } from "@llamaindex/tools"; // or any other tool

-const createWorkflow = () => agent({ tools: [wiki()], llm: openai("gpt-4o") });
+const createWorkflow = () => agent({ tools: [wiki()] });

 new LlamaIndexServer({
  workflow: createWorkflow,
  uiConfig: {
+    appTitle: "LlamaIndex App",
    starterQuestions: ["Who is the first president of the United States?"],
  },
 }).start();
 ```

-The `createWorkflow` function is a factory function that creates an [Agent Workflow](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/agent_workflow) with a tool that retrieves information from Wikipedia in this case. For more details, read about the [Workflow factory contract](#workflow-factory-contract).
-
 ## Running the Server

 In the same directory as `index.ts`, run the following command to start the server:
@@ -58,86 +54,16 @@ curl -X POST "http://localhost:3000/api/chat" -H "Content-Type: application/json

 The `LlamaIndexServer` accepts the following configuration options:

- `workflow`: A callable function that creates a workflow instance for each request. See [Workflow factory contract](#workflow-factory-contract) for more details.
+- `workflow`: A callable function that creates a workflow instance for each request
 - `uiConfig`: An object to configure the chat UI containing the following properties:
+  - `appTitle`: The title of the application (default: `"LlamaIndex App"`)
  - `starterQuestions`: List of starter questions for the chat UI (default: `[]`)
-  - `enableFileUpload`: Whether to enable file upload in the chat UI (default: `false`). See [Upload file example](./examples/private-file/README.md) for more details.
  - `componentsDir`: The directory for custom UI components rendering events emitted by the workflow. The default is undefined, which does not render custom UI components.
-  - `layoutDir`: The directory for custom layout sections. The default value is `layout`. See [Custom Layout](#custom-layout) for more details.
  - `llamaCloudIndexSelector`: Whether to show the LlamaCloud index selector in the chat UI (requires `LLAMA_CLOUD_API_KEY` to be set in the environment variables) (default: `false`)
-  - `dev_mode`: When enabled, you can update workflow code in the UI and see the changes immediately. It's currently in beta and only supports updating workflow code at `app/src/workflow.ts`. Please start server in dev mode (`npm run dev`) to use see this reload feature enabled.
- `suggestNextQuestions`: Whether to suggest next questions after the assistant's response (default: `true`). You can change the prompt for the next questions by setting the `NEXT_QUESTION_PROMPT` environment variable.

 LlamaIndexServer accepts all the configuration options from Nextjs Custom Server such as `port`, `hostname`, `dev`, etc.
 See all Nextjs Custom Server options [here](https://nextjs.org/docs/app/building-your-application/configuring/custom-server).

-## Workflow factory contract
-
-The `workflow` provided will be called for each chat request to initialize a new workflow instance. For advanced use cases, you can define workflowFactory with a chatBody which include list of UI messages in the request body.
-
-```typescript
-import { type Message } from "ai";
-import { agent } from "@llamaindex/workflow";
-
-const workflowFactory = (chatBody: { messages: Message[] }) => {
-  ...
-};
-```
-
-The contract of the generated workflow must be the same as for the [Agent Workflow](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/agent_workflow). This means that the workflow must handle a `startAgentEvent` event, which is the entry point of the workflow and contains the following information in it's `data` property:
-
-```typescript
-{
-  userInput: MessageContent;
-  chatHistory?: ChatMessage[] | undefined;
-};
-```
-
-The `userInput` is the latest user message and the `chatHistory` is the list of messages exchanged between the user and the workflow so far.
-
-Furthermore, the workflow must stop with a `stopAgentEvent` event to mark the end of the workflow. In between, the workflow can emit [UI events](##AI-generated-UI-Components) to render custom UI components and [Artifact events](##Sending-Artifacts-to-the-UI) to send structured data like generated documents or code snippets to the UI.
-
-```ts
-import {
-  createStatefulMiddleware,
-  createWorkflow,
-  startAgentEvent,
-} from "@llamaindex/workflow";
-import { ChatMemoryBuffer, type ChatMessage, Settings } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { wiki } from "@llamaindex/tools";
-
-Settings.llm = openai("gpt-4o");
-
-export const workflowFactory = async () => {
-  const workflow = createWorkflow();
-
-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { state, sendEvent } = getContext();
-    const messages = data.chatHistory;
-
-    const toolCallResponse = await chatWithTools(
-      Settings.llm,
-      [wiki()],
-      messages,
-    );
-
-    // using result from tool call and use `sendEvent` to emit the next event...
-  });
-
-  // define more workflow handling logic here...
-
-  // Finally stop with a `stopAgentEvent` event to mark the end of the workflow.
-  // return stopAgentEvent.with({
-  //   result: "This is the end!",
-  // });
-
-  return workflow;
-};
-```
-
-To generate sophisticated examples of workflows, you best use the [create-llama](https://github.com/run-llama/create-llama) project.
-
 ## AI-generated UI Components

 The LlamaIndex server provides support for rendering workflow events using custom UI components, allowing you to extend and customize the chat interface.
@@ -197,28 +123,6 @@ Feel free to modify the generated code to match your needs. If you're not satisf

 > Note that `generateEventComponent` is generating JSX code, but you can also provide a TSX file.

-## Custom Layout
-
-LlamaIndex Server supports custom layout for header and footer. To use custom layout, you need to initialize the LlamaIndex server with the `layoutDir` that contains your custom layout files.
-
-```ts
-new LlamaIndexServer({
-  workflow: createWorkflow,
-  uiConfig: {
-    layoutDir: "layout",
-  },
-}).start();
-```
-
-```
-layout/
-  header.tsx
-  footer.tsx
-```
-
-We currently support custom header and footer for the chat interface. The syntax for these files is the same as events components in components directory.
-Note that by default, we are still rendering the default LlamaIndex Header. It's also the fallback when having errors rendering the custom header. Example layout files will be generated in the `layout` directory of your project when creating a new project with `create-llama`.
-
 ### Server Setup

 To use the generated UI components, you need to initialize the LlamaIndex server with the `componentsDir` that contains your custom UI components:
@@ -227,71 +131,12 @@ To use the generated UI components, you need to initialize the LlamaIndex server
 new LlamaIndexServer({
  workflow: createWorkflow,
  uiConfig: {
+    appTitle: "LlamaIndex App",
    componentsDir: "components",
  },
 }).start();
 ```

-## Sending Artifacts to the UI
-
-In addition to UI events for custom components, LlamaIndex Server supports a special `ArtifactEvent` to send structured data like generated documents or code snippets to the UI. These artifacts are displayed in a dedicated "Canvas" panel in the chat interface.
-
-### Artifact Event Structure
-
-To send an artifact, your workflow needs to emit an event with `type: "artifact"`. The `data` payload of this event should include:
-
- `type`: A string indicating the type of artifact (e.g., `"document"`, `"code"`).
- `created_at`: A timestamp (e.g., `Date.now()`) indicating when the artifact was created.
- `data`: An object containing the specific details of the artifact. The structure of this object depends on the artifact `type`.
-
-### Defining and Sending an ArtifactEvent
-
-First, define your artifact event using `workflowEvent` from `@llamaindex/workflow`:
-
-```typescript
-import { workflowEvent } from "@llamaindex/workflow";
-
-// Example for a document artifact
-const artifactEvent = workflowEvent<{
-  type: "artifact"; // Must be "artifact"
-  data: {
-    type: "document"; // Custom type for your artifact (e.g., "document", "code")
-    created_at: number;
-    data: {
-      // Specific data for the document artifact type
-      title: string;
-      content: string;
-      type: "markdown" | "html"; // document format
-    };
-  };
-}>();
-```
-
-Then, within your workflow logic, use `sendEvent` (obtained from `getContext()`) to emit the event:
-
-```typescript
-// Assuming 'sendEvent' is available in your workflow handler
-// and 'documentDetails' contains the content for the artifact.
-
-sendEvent(
-  artifactEvent.with({
-    type: "artifact", // This top-level type must be "artifact"
-    data: {
-      type: "document", // This is your specific artifact type
-      created_at: Date.now(),
-      data: {
-        title: "My Generated Document",
-        content: "# Hello World
-This is a markdown document.",
-        type: "markdown",
-      },
-    },
-  }),
-);
-```
-
-This will send the artifact to the LlamaIndex Server UI, where it will be rendered in the [ChatCanvasPanel](/packages/server/next/app/components/ui/chat/canvas/panel.tsx) by a renderer depending on the artifact type. For type `document` this is using the [DocumentArtifactViewer](https://github.com/run-llama/chat-ui/blob/bacb75fc6edceacf742fba18632404a2483b5a81/packages/chat-ui/src/chat/canvas/artifacts/document.tsx#L17).
-
 ## Default Endpoints and Features

 ### Chat Endpoint
@@ -311,23 +156,6 @@ The server always provides a chat interface at the root path (`/`) with:
 - The server automatically mounts the `data` and `output` folders at `{server_url}{api_prefix}/files/data` (default: `/api/files/data`) and `{server_url}{api_prefix}/files/output` (default: `/api/files/output`) respectively.
 - Your workflows can use both folders to store and access files. By convention, the `data` folder is used for documents that are ingested, and the `output` folder is used for documents generated by the workflow.

-### Eject Mode
-
-If you want to fully customize the server UI and routes, you can use `npm eject`. It will create a normal Next.js project with the same functionality as @llamaindex/server.
-By default, the ejected project will be in the `next` directory in the current working directory. You can change the output directory by providing custom path after `eject` command:
-
-```bash
-npm eject <path-to-output-directory>
-```
-
-How eject works:
-
-1. Init nextjs project with eslint, prettier, postcss, tailwindcss, shadcn components, etc.
-2. Copy your workflow definition and setting files in src/app/\* to the ejected project in app/api/chat
-3. Copy your components, data, output, storage folders to the ejected project
-4. Copy your current .env file to the ejected project
-5. Clean up files that are no longer needed and update imports
-
 ## API Reference

 - [LlamaIndexServer](https://ts.llamaindex.ai/docs/api/classes/LlamaIndexServer)
@@ -1,172 +0,0 @@
-#!/usr/bin/env node
-
-const fs = require("fs").promises;
-const path = require("path");
-
-// Resolve the project directory in node_modules/@llamaindex/server/project
-// This is the template that used to construct the nextjs project
-const projectDir = path.resolve(__dirname, "../project");
-
-// Resolve the src directory that contains workflow & setting files
-const srcDir = path.join(process.cwd(), "src");
-const srcAppDir = path.join(srcDir, "app");
-const generateFile = path.join(srcDir, "generate.ts");
-const envFile = path.join(process.cwd(), ".env");
-
-// The environment variables that are used as LlamaIndexServer configs
-const SERVER_CONFIG_VARS = [
-  {
-    key: "OPENAI_API_KEY",
-    defaultValue: "<your-openai-api-key>",
-    description: "OpenAI API key",
-  },
-  {
-    key: "SUGGEST_NEXT_QUESTIONS",
-    defaultValue: "true",
-    description: "Whether to suggest next questions (`suggestNextQuestions`)",
-  },
-  {
-    key: "COMPONENTS_DIR",
-    defaultValue: "components",
-    description: "Directory for custom components (`componentsDir`)",
-  },
-  {
-    key: "WORKFLOW_FILE_PATH",
-    defaultValue: "app/api/chat/app/workflow.ts",
-    description: "The path to the workflow file (will be updated in dev mode)",
-  },
-  {
-    key: "NEXT_PUBLIC_USE_COMPONENTS_DIR",
-    defaultValue: "true",
-    description: "Whether to enable components directory feature on frontend",
-  },
-  {
-    key: "NEXT_PUBLIC_DEV_MODE",
-    defaultValue: "true",
-    description: "Whether to enable dev mode (`devMode`)",
-  },
-  {
-    key: "NEXT_PUBLIC_STARTER_QUESTIONS",
-    defaultValue: '["Summarize the document", "What are the key points?"]',
-    description:
-      "Initial questions to display in the chat (`starterQuestions`)",
-  },
-  {
-    key: "NEXT_PUBLIC_SHOW_LLAMACLOUD_SELECTOR",
-    defaultValue: "false",
-    description:
-      "Whether to show LlamaCloud selector for frontend (`llamaCloudIndexSelector`)",
-  },
-];
-
-async function eject() {
-  try {
-    // validate required directories (nextjs project template, src directory, src/app directory)
-    const requiredDirs = [projectDir, srcDir, srcAppDir];
-    for (const dir of requiredDirs) {
-      const exists = await fs
-        .access(dir)
-        .then(() => true)
-        .catch(() => false);
-      if (!exists) {
-        console.error("Error: directory does not exist at", dir);
-        process.exit(1);
-      }
-    }
-
-    // Get destination directory from command line arguments (pnpm eject <path>)
-    const args = process.argv;
-    const outputIndex = args.indexOf("eject");
-    const destDir =
-      outputIndex !== -1 && args[outputIndex + 1]
-        ? path.resolve(args[outputIndex + 1]) // Use provided path after eject
-        : path.join(process.cwd(), "next"); // Default to "next" folder in the current working directory
-
-    // remove destination directory if it exists
-    await fs.rm(destDir, { recursive: true, force: true });
-
-    // create destination directory
-    await fs.mkdir(destDir, { recursive: true });
-
-    // Copy the nextjs project template to the destination directory
-    await fs.cp(projectDir, destDir, { recursive: true });
-
-    // copy src/app/* to destDir/app/api/chat
-    const chatRouteDir = path.join(destDir, "app", "api", "chat");
-    await fs.cp(srcAppDir, path.join(chatRouteDir, "app"), { recursive: true });
-
-    // nextjs project doesn't depend on @llamaindex/server anymore, we need to update the imports in workflow file
-    const workflowFile = path.join(chatRouteDir, "app", "workflow.ts");
-    let workflowContent = await fs.readFile(workflowFile, "utf-8");
-    workflowContent = workflowContent.replace("@llamaindex/server", "../utils");
-    await fs.writeFile(workflowFile, workflowContent);
-
-    // copy generate.ts if it exists
-    const genFilePath = path.join(chatRouteDir, "generate.ts");
-    const genFileExists = await copy(generateFile, genFilePath);
-    if (genFileExists) {
-      // update the import @llamaindex/server in generate.ts
-      let genContent = await fs.readFile(genFilePath, "utf-8");
-      genContent = genContent.replace("@llamaindex/server", "./utils");
-      await fs.writeFile(genFilePath, genContent);
-    }
-
-    // copy folders in root directory if exists
-    const rootFolders = ["components", "data", "output", "storage"];
-    for (const folder of rootFolders) {
-      await copy(path.join(process.cwd(), folder), path.join(destDir, folder));
-    }
-
-    // copy .env if it exists or create a new one
-    const envFileExists = await copy(envFile, path.join(destDir, ".env"));
-    if (!envFileExists) {
-      await fs.writeFile(path.join(destDir, ".env"), "");
-    }
-
-    // update .env file with more server configs
-    let envFileContent = await fs.readFile(path.join(destDir, ".env"), "utf-8");
-    for (const envVar of SERVER_CONFIG_VARS) {
-      const { key, defaultValue, description } = envVar;
-      if (!envFileContent.includes(key)) {
-        // if the key is not exists in the env file, add it
-        envFileContent += `\n# ${description}\n${key}=${defaultValue}\n`;
-      }
-    }
-    await fs.writeFile(path.join(destDir, ".env"), envFileContent);
-
-    // rename gitignore -> .gitignore
-    await fs.rename(
-      path.join(destDir, "gitignore"),
-      path.join(destDir, ".gitignore"),
-    );
-
-    // user can customize layout directory in nextjs project, remove layout api
-    await fs.rm(path.join(destDir, "app", "api", "layout"), {
-      recursive: true,
-      force: true,
-    });
-
-    // remove no-needed files
-    await fs.unlink(path.join(destDir, "public", "config.js"));
-    await fs.unlink(path.join(destDir, "next-build.config.ts"));
-
-    console.log("Successfully ejected @llamaindex/server to", destDir);
-  } catch (error) {
-    console.error("Error during eject:", error.message);
-    process.exit(1);
-  }
-}
-
-// copy src to dest if src exists, return true if src exists
-async function copy(src, dest) {
-  const srcExists = await fs
-    .access(src)
-    .then(() => true)
-    .catch(() => false);
-  if (srcExists) {
-    await fs.cp(src, dest, { recursive: true });
-  }
-  return srcExists;
-}
-
-eject();
@@ -1,186 +0,0 @@
-# LlamaIndex Server Examples
-
-This package contains practical examples demonstrating how to use the `@llamaindex/server` package to build chat applications with LlamaIndex workflows.
-
-## Package Overview
-
-The examples package is a collection of standalone TypeScript applications that showcase different features and capabilities of the LlamaIndex Server framework. Each example can be run independently to demonstrate specific functionality.
-
-## Key Features Demonstrated
-
-### 1. Simple Workflow (`simple-workflow/calculator.ts`)
-
- **Purpose**: Basic agent workflow with tool integration
- **Features**: Calculator agent with add tool, starter questions
- **Key Concepts**: Tool definition with Zod schemas, basic server setup
-
-### 2. Agentic RAG (`agentic-rag/index.ts`)
-
- **Purpose**: Retrieval-Augmented Generation with document querying
- **Features**: Vector store index, document ingestion, query engine tool, automatic question suggestions
- **Key Concepts**: RAG implementation, source node inclusion, embedding models
-
-### 3. Custom Layout (`custom-layout/index.ts` + `layout/header.tsx`)
-
- **Purpose**: Custom UI components and layout customization
- **Features**: Weather agent with custom header layout, branded interface
- **Key Concepts**: Layout directory configuration, React component integration
-
-### 4. Development Mode (`devmode/index.ts` + `src/app/workflow.ts`)
-
- **Purpose**: Live development and hot reloading capabilities
- **Features**: Dev mode panel, workflow file hot reloading, separate workflow file structure
- **Key Concepts**: Development workflow, file watching, modular architecture
-
-## Development Scripts
-
-```bash
-# Type checking
-pnpm typecheck
-
-# Run development server (defaults to simple-workflow/calculator.ts)
-pnpm dev
-
-# Run specific examples
-npx nodemon --exec tsx agentic-rag/index.ts
-npx nodemon --exec tsx custom-layout/index.ts
-npx nodemon --exec tsx devmode/index.ts --ignore src/app/workflow_*.ts  # Dev mode with file watching
-```
-
-## Environment Setup
-
-All examples require OpenAI API access:
-
-```bash
-export OPENAI_API_KEY=your_openai_api_key
-```
-
-## Dependencies
-
-### Core Dependencies
-
- `@llamaindex/server`: Main server framework (workspace dependency)
- `@llamaindex/workflow`: Workflow engine for agent creation
- `@llamaindex/openai`: OpenAI LLM and embedding integrations
- `@llamaindex/tools`: Tool utilities
- `@llamaindex/readers`: Document readers
- `llamaindex`: Core LlamaIndex library
- `zod`: Schema validation for tools
-
-### Development Dependencies
-
- `tsx`: TypeScript execution for development
- `nodemon`: File watching and auto-restart
- `typescript`: TypeScript compiler
-
-## Architecture Patterns
-
-### Workflow Factory Pattern
-
-All examples use the workflow factory pattern:
-
-```typescript
-const workflowFactory = () => agent({ tools: [...] });
-// or
-const workflowFactory = async () => { /* setup logic */ return agent({ tools: [...] }); };
-```
-
-### Server Configuration
-
-Standard server setup pattern:
-
-```typescript
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  uiConfig: {
-    /* UI configuration */
-  },
-  port: 3000,
-}).start();
-```
-
-### Tool Definition Pattern
-
-Consistent tool creation with Zod schemas:
-
-```typescript
-tool({
-  name: "tool_name",
-  description: "Tool description",
-  parameters: z.object({
-    /* parameters */
-  }),
-  execute: (params) => {
-    /* implementation */
-  },
-});
-```
-
-## Example-Specific Features
-
-### Simple Workflow
-
- Basic arithmetic operations
- Minimal setup for learning
- Demonstrates core workflow concepts
-
-### Agentic RAG
-
- Document indexing with embeddings
- Vector similarity search
- Source node tracking for citations
- Auto-generated follow-up questions
-
-### Custom Layout
-
- Custom React components in `layout/` directory
- Branded header with navigation
- Layout directory configuration (`layoutDir: "layout"`)
-
-### Dev Mode
-
- Live code editing in browser
- Hot reloading of workflow files
- Separate workflow file organization
- Development panel UI
-
-## TypeScript Configuration
-
- Target: ES2022 with bundler module resolution
- Strict type checking enabled
- Excludes: `node_modules`, `dist`, `custom-layout/layout` (runtime components)
- Output: `dist/` directory
-
-## Development Workflow
-
-1. **Choose Example**: Select appropriate example for your use case
-2. **Environment Setup**: Configure OpenAI API key
-3. **Run Development Server**: Use `pnpm dev` or specific nodemon commands
-4. **Access UI**: Open browser at `http://localhost:3000`
-5. **Iterate**: Modify code and see changes in real-time
-
-## Common Patterns
-
-### Agent Creation
-
-All examples use the `agent()` function from `@llamaindex/workflow` with tool arrays.
-
-### UI Configuration
-
- `starterQuestions`: Predefined questions for user guidance
- `layoutDir`: Custom layout components directory
- `devMode`: Enable development features
- `suggestNextQuestions`: Auto-generate follow-up questions
-
-### Error Handling
-
-Examples demonstrate proper async/await patterns and error handling for LLM operations.
-
-## Integration Points
-
- **LlamaIndex Core**: Document processing, indexing, querying
- **OpenAI**: LLM and embedding model integration
- **React/Next.js**: Frontend UI components and server-side rendering
- **TypeScript**: Type safety throughout the application stack
-
-This examples package serves as a comprehensive reference for building production-ready chat applications with LlamaIndex workflows.
@@ -1,38 +1,12 @@
 # LlamaIndex Server Examples

-This directory provides example projects demonstrating how to use the LlamaIndex Server.
+This directory contains examples of how to use the LlamaIndex Server.

-## How to Run the Examples
+## Running the examples

-1. **Install dependencies**
+```bash
+export OPENAI_API_KEY=your_openai_api_key
+npx tsx simple-workflow/calculator.ts
+```

-   In the root of this directory, run:
-
-   ```bash
-   pnpm install
-   ```
-
-2. **Set your OpenAI API key**
-
-   Export your OpenAI API key as an environment variable:
-
-   ```bash
-   export OPENAI_API_KEY=your_openai_api_key
-   ```
-
-3. **Start an example**
-
-   Replace `<example>` with the name of the example you want to run (e.g., `private-file`):
-
-   ```bash
-   pnpm nodemon --exec tsx <example>/index.ts
-   ```
-
-4. **Open the application in your browser**
-
-   Visit [http://localhost:3000](http://localhost:3000) to interact with the running example.
-
-## Notes
-
- Make sure you have [pnpm](https://pnpm.io/) installed.
- Each example may have its own specific instructions or requirements; check the individual example's index.ts for details.
+## Open browser at http://localhost:3000
@@ -1,7 +1,12 @@
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import { LlamaIndexServer } from "@llamaindex/server";
 import { agent } from "@llamaindex/workflow";
-import { Document, Settings, VectorStoreIndex } from "llamaindex";
+import {
+  Document,
+  OpenAI,
+  OpenAIEmbedding,
+  Settings,
+  VectorStoreIndex,
+} from "llamaindex";

 Settings.llm = new OpenAI({
  model: "gpt-4o-mini",
@@ -30,9 +35,9 @@ export const workflowFactory = async () => {

 new LlamaIndexServer({
  workflow: workflowFactory,
-  suggestNextQuestions: true,
  uiConfig: {
+    appTitle: "LlamaIndex App",
    starterQuestions: ["What is the color of the dog?"],
  },
-  port: 3000,
+  port: 4100,
 }).start();
@@ -1,22 +0,0 @@
-This example demonstrates how to use the code generation workflow.
-
-```ts
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  uiConfig: {
-    starterQuestions: [
-      "Generate a calculator app",
-      "Create a simple todo list app",
-    ],
-    componentsDir: "components",
-  },
-  port: 3000,
-}).start();
-```
-
-Export OpenAI API key and start the server in dev mode.
-
-```bash
-export OPENAI_API_KEY=<your-openai-api-key>
-npx nodemon --exec tsx index.ts
-```
@@ -1,132 +0,0 @@
-import { Badge } from "@/components/ui/badge";
-import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
-import { Progress } from "@/components/ui/progress";
-import { Skeleton } from "@/components/ui/skeleton";
-import { cn } from "@/lib/utils";
-import { Markdown } from "@llamaindex/chat-ui/widgets";
-import { ListChecks, Loader2, Wand2 } from "lucide-react";
-import { useEffect, useState } from "react";
-
-const STAGE_META = {
-  plan: {
-    icon: ListChecks,
-    badgeText: "Step 1/2: Planning",
-    gradient: "from-blue-100 via-blue-50 to-white",
-    progress: 33,
-    iconBg: "bg-blue-100 text-blue-600",
-    badge: "bg-blue-100 text-blue-700",
-  },
-  generate: {
-    icon: Wand2,
-    badgeText: "Step 2/2: Generating",
-    gradient: "from-violet-100 via-violet-50 to-white",
-    progress: 66,
-    iconBg: "bg-violet-100 text-violet-600",
-    badge: "bg-violet-100 text-violet-700",
-  },
-};
-
-function ArtifactWorkflowCard({ event }) {
-  const [visible, setVisible] = useState(event?.state !== "completed");
-  const [fade, setFade] = useState(false);
-
-  useEffect(() => {
-    if (event?.state === "completed") {
-      setVisible(false);
-    } else {
-      setVisible(true);
-      setFade(false);
-    }
-  }, [event?.state]);
-
-  if (!event || !visible) return null;
-
-  const { state, requirement } = event;
-  const meta = STAGE_META[state];
-
-  if (!meta) return null;
-
-  return (
-    <div className="flex min-h-[180px] w-full items-center justify-center py-2">
-      <Card
-        className={cn(
-          "w-full rounded-xl shadow-md transition-all duration-500",
-          "border-0",
-          fade && "pointer-events-none opacity-0",
-          `bg-gradient-to-br ${meta.gradient}`,
-        )}
-        style={{
-          boxShadow:
-            "0 2px 12px 0 rgba(80, 80, 120, 0.08), 0 1px 3px 0 rgba(80, 80, 120, 0.04)",
-        }}
-      >
-        <CardHeader className="flex flex-row items-center gap-2 px-3 pb-1 pt-2">
-          <div
-            className={cn(
-              "flex items-center justify-center rounded-full p-1",
-              meta.iconBg,
-            )}
-          >
-            <meta.icon className="h-5 w-5" />
-          </div>
-          <CardTitle className="flex items-center gap-2 text-base font-semibold">
-            <Badge className={cn("ml-1", meta.badge, "px-2 py-0.5 text-xs")}>
-              {meta.badgeText}
-            </Badge>
-          </CardTitle>
-        </CardHeader>
-        <CardContent className="px-3 py-1">
-          {state === "plan" && (
-            <div className="flex flex-col items-center gap-2 py-2">
-              <Loader2 className="mb-1 h-6 w-6 animate-spin text-blue-400" />
-              <div className="text-center text-sm font-medium text-blue-900">
-                Analyzing your request...
-              </div>
-              <Skeleton className="mt-1 h-3 w-1/2 rounded-full" />
-            </div>
-          )}
-          {state === "generate" && (
-            <div className="flex flex-col gap-2 py-2">
-              <div className="flex items-center gap-1">
-                <Loader2 className="h-4 w-4 animate-spin text-violet-400" />
-                <span className="text-sm font-medium text-violet-900">
-                  Working on the requirement:
-                </span>
-              </div>
-              <div className="max-h-24 overflow-auto rounded-lg border border-violet-200 bg-violet-50 px-2 py-1 text-xs">
-                {requirement ? (
-                  <Markdown content={requirement} />
-                ) : (
-                  <span className="italic text-violet-400">
-                    No requirements available yet.
-                  </span>
-                )}
-              </div>
-            </div>
-          )}
-        </CardContent>
-        <div className="px-3 pb-2 pt-1">
-          <Progress
-            value={meta.progress}
-            className={cn(
-              "h-1 rounded-full bg-gray-200",
-              state === "plan" && "bg-blue-200",
-              state === "generate" && "bg-violet-200",
-            )}
-          />
-        </div>
-      </Card>
-    </div>
-  );
-}
-
-export default function Component({ events }) {
-  const aggregateEvents = () => {
-    if (!events || events.length === 0) return null;
-    return events[events.length - 1];
-  };
-
-  const event = aggregateEvents();
-
-  return <ArtifactWorkflowCard event={event} />;
-}
@@ -1,20 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import { LlamaIndexServer } from "@llamaindex/server";
-import { Settings } from "llamaindex";
-import { workflowFactory } from "./src/app/workflow";
-
-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  uiConfig: {
-    starterQuestions: [
-      "Generate a calculator app",
-      "Create a simple todo list app",
-    ],
-    componentsDir: "components",
-  },
-  port: 3000,
-}).start();
@@ -1,337 +0,0 @@
-import { artifactEvent, extractLastArtifact } from "@llamaindex/server";
-import { ChatMemoryBuffer, MessageContent, Settings } from "llamaindex";
-
-import {
-  agentStreamEvent,
-  createStatefulMiddleware,
-  createWorkflow,
-  startAgentEvent,
-  stopAgentEvent,
-  workflowEvent,
-} from "@llamaindex/workflow";
-
-import { z } from "zod";
-
-export const RequirementSchema = z.object({
-  next_step: z.enum(["answering", "coding"]),
-  language: z.string().nullable().optional(),
-  file_name: z.string().nullable().optional(),
-  requirement: z.string(),
-});
-
-export type Requirement = z.infer<typeof RequirementSchema>;
-
-export const UIEventSchema = z.object({
-  type: z.literal("ui_event"),
-  data: z.object({
-    state: z
-      .enum(["plan", "generate", "completed"])
-      .describe(
-        "The current state of the workflow: 'plan', 'generate', or 'completed'.",
-      ),
-    requirement: z
-      .string()
-      .optional()
-      .describe(
-        "An optional requirement creating or updating a code, if applicable.",
-      ),
-  }),
-});
-
-export type UIEvent = z.infer<typeof UIEventSchema>;
-const planEvent = workflowEvent<{
-  userInput: MessageContent;
-  context?: string | undefined;
-}>();
-
-const generateArtifactEvent = workflowEvent<{
-  requirement: Requirement;
-}>();
-
-const synthesizeAnswerEvent = workflowEvent<object>();
-
-const uiEvent = workflowEvent<UIEvent>();
-
-export function workflowFactory(reqBody: unknown) {
-  const llm = Settings.llm;
-
-  const { withState, getContext } = createStatefulMiddleware(() => {
-    return {
-      memory: new ChatMemoryBuffer({ llm }),
-      lastArtifact: extractLastArtifact(reqBody),
-    };
-  });
-  const workflow = withState(createWorkflow());
-
-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { userInput, chatHistory = [] } = data;
-    // Prepare chat history
-    const { state } = getContext();
-    // Put user input to the memory
-    if (!userInput) {
-      throw new Error("Missing user input to start the workflow");
-    }
-    state.memory.set(chatHistory);
-    state.memory.put({ role: "user", content: userInput });
-
-    return planEvent.with({
-      userInput: userInput,
-      context: state.lastArtifact
-        ? JSON.stringify(state.lastArtifact)
-        : undefined,
-    });
-  });
-
-  workflow.handle([planEvent], async ({ data: planData }) => {
-    const { sendEvent } = getContext();
-    const { state } = getContext();
-    sendEvent(
-      uiEvent.with({
-        type: "ui_event",
-        data: {
-          state: "plan",
-        },
-      }),
-    );
-    const user_msg = planData.userInput;
-    const context = planData.context
-      ? `## The context is: \n${planData.context}\n`
-      : "";
-    const prompt = `
-You are a product analyst responsible for analyzing the user's request and providing the next step for code or document generation.
-You are helping user with their code artifact. To update the code, you need to plan a coding step.
-
-Follow these instructions:
-1. Carefully analyze the conversation history and the user's request to determine what has been done and what the next step should be.
-2. The next step must be one of the following two options:
-    - "coding": To make the changes to the current code.
-    - "answering": If you don't need to update the current code or need clarification from the user.
-Important: Avoid telling the user to update the code themselves, you are the one who will update the code (by planning a coding step).
-3. If the next step is "coding", you may specify the language ("typescript" or "python") and file_name if known, otherwise set them to null. 
-4. The requirement must be provided clearly what is the user request and what need to be done for the next step in details
-    as precise and specific as possible, don't be stingy with in the requirement.
-5. If the next step is "answering", set language and file_name to null, and the requirement should describe what to answer or explain to the user.
-6. Be concise; only return the requirements for the next step.
-7. The requirements must be in the following format:
-    \`\`\`json
-    {
-        "next_step": "answering" | "coding",
-        "language": "typescript" | "python" | null,
-        "file_name": string | null,
-        "requirement": string
-    }
-    \`\`\`
-
-## Example 1:
-User request: Create a calculator app.
-You should return:
-\`\`\`json
-{
-    "next_step": "coding",
-    "language": "typescript",
-    "file_name": "calculator.tsx",
-    "requirement": "Generate code for a calculator app that has a simple UI with a display and button layout. The display should show the current input and the result. The buttons should include basic operators, numbers, clear, and equals. The calculation should work correctly."
-}
-\`\`\`
-
-## Example 2:
-User request: Explain how the game loop works.
-Context: You have already generated the code for a snake game.
-You should return:
-\`\`\`json
-{
-    "next_step": "answering",
-    "language": null,
-    "file_name": null,
-    "requirement": "The user is asking about the game loop. Explain how the game loop works."
-}
-\`\`\`
-
-${context}
-
-Now, plan the user's next step for this request:
-${user_msg}
-`;
-
-    const response = await llm.complete({
-      prompt,
-    });
-    // parse the response to Requirement
-    // 1. use regex to find the json block
-    const jsonBlock = response.text.match(/```json\s*([\s\S]*?)\s*```/);
-    if (!jsonBlock) {
-      throw new Error("No JSON block found in the response.");
-    }
-    const requirement = RequirementSchema.parse(JSON.parse(jsonBlock[1]));
-    state.memory.put({
-      role: "assistant",
-      content: `The plan for next step: \n${response.text}`,
-    });
-
-    if (requirement.next_step === "coding") {
-      return generateArtifactEvent.with({
-        requirement,
-      });
-    } else {
-      return synthesizeAnswerEvent.with({});
-    }
-  });
-
-  workflow.handle([generateArtifactEvent], async ({ data: planData }) => {
-    const { sendEvent } = getContext();
-    const { state } = getContext();
-
-    sendEvent(
-      uiEvent.with({
-        type: "ui_event",
-        data: {
-          state: "generate",
-          requirement: planData.requirement.requirement,
-        },
-      }),
-    );
-
-    const previousArtifact = state.lastArtifact
-      ? JSON.stringify(state.lastArtifact)
-      : "There is no previous artifact";
-    const requirementText = planData.requirement.requirement;
-
-    const prompt = `
-        You are a skilled developer who can help user with coding.
-        You are given a task to generate or update a code for a given requirement.
-
-        ## Follow these instructions:
-        **1. Carefully read the user's requirements.** 
-           If any details are ambiguous or missing, make reasonable assumptions and clearly reflect those in your output.
-           If the previous code is provided:
-           + Carefully analyze the code with the request to make the right changes.
-           + Avoid making a lot of changes from the previous code if the request is not to write the code from scratch again.
-        **2. For code requests:**
-           - If the user does not specify a framework or language, default to a React component using the Next.js framework.
-           - For Next.js, use Shadcn UI components, Typescript, @types/node, @types/react, @types/react-dom, PostCSS, and TailwindCSS.
-           The import pattern should be:
-           \`\`\`typescript
-           import { ComponentName } from "@/components/ui/component-name"
-           import { Markdown } from "@llamaindex/chat-ui"
-           import { cn } from "@/lib/utils"
-           \`\`\`
-           - Ensure the code is idiomatic, production-ready, and includes necessary imports.
-           - Only generate code relevant to the user's request—do not add extra boilerplate.
-        **3. Don't be verbose on response**
-           - No other text or comments only return the code which wrapped by \`\`\`language\`\`\` block.
-           - If the user's request is to update the code, only return the updated code.
-        **4. Only the following languages are allowed: "typescript", "python".**
-        **5. If there is no code to update, return the reason without any code block.**
-           
-        ## Example:
-        \`\`\`typescript
-        import React from "react";
-        import { Button } from "@/components/ui/button";
-        import { cn } from "@/lib/utils";
-
-        export default function MyComponent() {
-        return (
-           <div className="flex flex-col items-center justify-center h-screen">
-              <Button>Click me</Button>
-           </div>
-        );
-        }
-        \`\`\`
-
-        The previous code is:
-        {previousArtifact}
-
-        Now, i have to generate the code for the following requirement:
-        {requirement}
-      `
-      .replace("{previousArtifact}", previousArtifact)
-      .replace("{requirement}", requirementText);
-
-    const response = await llm.complete({
-      prompt,
-    });
-
-    // Extract the code from the response
-    const codeMatch = response.text.match(/```(\w+)([\s\S]*)```/);
-    if (!codeMatch) {
-      return synthesizeAnswerEvent.with({});
-    }
-
-    const code = codeMatch[2].trim();
-
-    // Put the generated code to the memory
-    state.memory.put({
-      role: "assistant",
-      content: `Updated the code: \n${response.text}`,
-    });
-
-    // To show the Canvas panel for the artifact
-    sendEvent(
-      artifactEvent.with({
-        type: "artifact",
-        data: {
-          type: "code",
-          created_at: Date.now(),
-          data: {
-            language: planData.requirement.language || "",
-            file_name: planData.requirement.file_name || "",
-            code,
-          },
-        },
-      }),
-    );
-
-    return synthesizeAnswerEvent.with({});
-  });
-
-  workflow.handle([synthesizeAnswerEvent], async () => {
-    const { sendEvent } = getContext();
-    const { state } = getContext();
-
-    const chatHistory = await state.memory.getMessages();
-    const messages = [
-      ...chatHistory,
-      {
-        role: "system" as const,
-        content: `
-        You are a helpful assistant who is responsible for explaining the work to the user.
-        Based on the conversation history, provide an answer to the user's question. 
-        The user has access to the code so avoid mentioning the whole code again in your response.
-      `,
-      },
-    ];
-
-    const responseStream = await llm.chat({
-      messages,
-      stream: true,
-    });
-
-    sendEvent(
-      uiEvent.with({
-        type: "ui_event",
-        data: {
-          state: "completed",
-        },
-      }),
-    );
-
-    let response = "";
-    for await (const chunk of responseStream) {
-      response += chunk.delta;
-      sendEvent(
-        agentStreamEvent.with({
-          delta: chunk.delta,
-          response: "",
-          currentAgentName: "assistant",
-          raw: chunk,
-        }),
-      );
-    }
-
-    return stopAgentEvent.with({
-      result: response,
-    });
-  });
-
-  return workflow;
-}
@@ -1,32 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import { LlamaIndexServer } from "@llamaindex/server";
-import { agent } from "@llamaindex/workflow";
-import { Settings, tool } from "llamaindex";
-import { z } from "zod";
-
-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
-const weatherAgent = agent({
-  tools: [
-    tool({
-      name: "weather",
-      description: "Get the weather in a given city",
-      parameters: z.object({ city: z.string() }),
-      execute: ({ city }) => `The weather in ${city} is sunny`,
-    }),
-  ],
-});
-
-new LlamaIndexServer({
-  workflow: () => weatherAgent,
-  uiConfig: {
-    starterQuestions: [
-      "What is the weather in Tokyo?",
-      "What is the weather in Ho Chi Minh City?",
-    ],
-    layoutDir: "layout",
-  },
-  port: 3000,
-}).start();
@@ -1,40 +0,0 @@
-"use client";
-
-import { Sparkles, Star } from "lucide-react";
-
-export default function Header() {
-  return (
-    <div className="flex items-center justify-between p-2 px-4">
-      <div className="flex items-center gap-2">
-        <Sparkles className="size-4" />
-        <h1 className="font-semibold">LlamaIndex App</h1>
-      </div>
-      <div className="flex items-center justify-end gap-4">
-        <div className="flex items-center gap-2">
-          <a
-            href="https://www.llamaindex.ai/"
-            target="_blank"
-            rel="noopener noreferrer"
-            className="text-sm text-gray-600 hover:text-gray-800 dark:text-gray-400 dark:hover:text-gray-200"
-          >
-            Built by LlamaIndex
-          </a>
-          <img
-            className="h-[24px] w-[24px] rounded-sm"
-            src="/llama.png"
-            alt="Llama Logo"
-          />
-        </div>
-        <a
-          href="https://github.com/run-llama/LlamaIndexTS"
-          target="_blank"
-          rel="noopener noreferrer"
-          className="hover:bg-accent flex items-center gap-2 rounded-md border border-gray-300 px-2 py-1 text-sm"
-        >
-          <Star className="size-4" />
-          Star on GitHub
-        </a>
-      </div>
-    </div>
-  );
-}
@@ -1,20 +0,0 @@
-This example shows how to use the dev mode of the server.
-
-First, we need to set `devMode` to `true` in the `uiConfig` of the server.
-
-```ts
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  uiConfig: {
-    devMode: true,
-  },
-  port: 3000,
-}).start();
-```
-
-Export OpenAI API key and start the server in dev mode.
-
-```bash
-export OPENAI_API_KEY=<your-openai-api-key>
-npx nodemon --exec tsx index.ts --ignore src/app/workflow_*.ts
-```
@@ -1,20 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import { LlamaIndexServer } from "@llamaindex/server";
-import { Settings } from "llamaindex";
-import { workflowFactory } from "./src/app/workflow";
-
-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  uiConfig: {
-    devMode: true,
-    starterQuestions: [
-      "What is the weather in Tokyo?",
-      "What is the weather in New York?",
-    ],
-  },
-  port: 3000,
-}).start();
@@ -1,16 +0,0 @@
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { z } from "zod";
-
-export const workflowFactory = async () => {
-  return agent({
-    tools: [
-      tool({
-        name: "weather",
-        description: "Get the weather in a specific city",
-        parameters: z.object({ city: z.string() }),
-        execute: ({ city }) => `The weather in ${city} is sunny`,
-      }),
-    ],
-  });
-};
@@ -1,172 +0,0 @@
-# Human in the Loop
-
-This example shows how to use the LlamaIndexServer with a human in the loop. It allows you to start CLI commands that are reviewed by a human before execution.
-
-## Getting Started
-
-### Environment Setup
-
-Export your OpenAI API key:
-
-```bash
-export OPENAI_API_KEY=<your-openai-api-key>
-```
-
-### Starting the Server
-
-Run the server in development mode:
-
-```bash
-npx nodemon --exec tsx index.ts --ignore output/*
-```
-
-### Access the Application
-
-Open your browser and go to:
-
-```
-http://localhost:3000
-```
-
-You will see the LlamaIndexServer UI, where you can interact with the HITL agent. Try "List all files in the current directory" and see how the agent pauses and waits for a human response before executing the command.
-
-## How does HITL work?
-
-### Events
-
-The human-in-the-loop approach used here is based on a simple idea: the workflow pauses and waits for a human response before proceeding to the next step.
-
-To do this, you will need to implement two custom events:
-
- [HumanInputEvent](https://github.com/run-llama/create-llama/blob/main/packages/server/src/utils/hitl/events.ts): This event is used to request input from the user.
- [HumanResponseEvent](https://github.com/run-llama/create-llama/blob/main/packages/server/src/utils/hitl/events.ts): This event is sent to the workflow to resume execution with input from the user.
-
-In this example, we have implemented these two custom events in [`events.ts`](src/app/events.ts):
-
- `cliHumanInputEvent` – to request input from the user for CLI command execution.
- `cliHumanResponseEvent` – to resume the workflow with the response from the user.
-
-```typescript
-export const cliHumanInputEvent = humanInputEvent<{
-  type: "cli_human_input";
-  data: { command: string };
-  response: typeof cliHumanResponseEvent;
-}>();
-
-export const cliHumanResponseEvent = humanResponseEvent<{
-  type: "human_response";
-  data: { execute: boolean; command: string };
-}>();
-```
-
-### UI Component
-
-HITL also needs a custom UI component, that is shown when the LlamaIndexServer receives the `cliHumanInputEvent`. The name of the component is defined in the `type` field of the `cliHumanInputEvent` - in our case, it is `cli_human_input`, which corresponds to the [cli_human_input.tsx](./components/cli_human_input.tsx) component.
-
-The custom component must use `append` to send a message with a `human_response` annotation. The data of the annotation must be in the format of the response event `cliHumanResponseEvent`, in our case, for sending to execute the command `ls -l`, we would send:
-
-```tsx
-append({
-  content: "Yes",
-  role: "user",
-  annotations: [
-    {
-      type: "human_response",
-      data: {
-        execute: true,
-        command: "ls -l", // The command to execute
-      },
-    },
-  ],
-});
-```
-
-This component displays the command to execute and the user can choose to execute or cancel the command execution.
-
-### Workflow Implementation
-
-The workflow is implemented in [`workflow.ts`](src/app/workflow.ts) using LlamaIndex workflows. The workflow handles three main steps:
-
-1. **Initial Request Handling**: When a user input is received, the workflow uses `chatWithTools` to determine if a CLI command should be executed. If so, it emits a `cliHumanInputEvent` to request user permission.
-
-```typescript
-workflow.handle([startAgentEvent], async ({ data }) => {
-  const { userInput, chatHistory = [] } = data;
-
-  const toolCallResponse = await chatWithTools(
-    llm,
-    [cliExecutor],
-    chatHistory.concat({ role: "user", content: userInput }),
-  );
-
-  const cliExecutorToolCall = toolCallResponse.toolCalls.find(
-    (toolCall) => toolCall.name === cliExecutor.metadata.name,
-  );
-
-  const command = cliExecutorToolCall?.input?.command as string;
-  if (command) {
-    return cliHumanInputEvent.with({
-      type: "cli_human_input",
-      data: { command },
-      response: cliHumanResponseEvent,
-    });
-  }
-
-  return summaryEvent.with("");
-});
-```
-
-2. **Human Response Handling**: After receiving human input, the workflow either executes the command or cancels based on the user's choice.
-
-```typescript
-workflow.handle([cliHumanResponseEvent], async ({ data }) => {
-  const { command, execute } = data.data;
-
-  if (!execute) {
-    return summaryEvent.with(`User reject to execute the command ${command}`);
-  }
-
-  const result = (await cliExecutor.call({ command })) as string;
-
-  return summaryEvent.with(
-    `Executed the command ${command} and got the result: ${result}`,
-  );
-});
-```
-
-3. **Final Response**: The workflow generates a final response based on the execution result and streams it back to the user.
-
-### Tools
-
-The CLI executor tool is defined in [`tools.ts`](src/app/tools.ts):
-
-```typescript
-export const cliExecutor = tool({
-  name: "cli_executor",
-  description: "This tool executes a command and returns the output.",
-  parameters: z.object({ command: z.string() }),
-  execute: async ({ command }) => {
-    try {
-      const output = execSync(command, {
-        encoding: "utf-8",
-      });
-      return output;
-    } catch (error) {
-      console.error(error);
-      return "Command failed";
-    }
-  },
-});
-```
-
-## Architecture
-
-The HITL implementation consists of:
-
-1. **Workflow Factory** (`workflow.ts`): Creates and configures the workflow with event handlers
-2. **Events** (`events.ts`): Defines typed events for human input and response
-3. **Tools** (`tools.ts`): Implements the CLI executor tool
-4. **UI Component** (`components/cli_human_input.tsx`): Provides the user interface for human approval
-5. **Server Entry** (`index.ts`): Configures and starts the LlamaIndexServer
-
-This architecture ensures that dangerous operations like CLI command execution require explicit human approval before proceeding.
@@ -1,95 +0,0 @@
-import { Button } from "@/components/ui/button";
-import { Card, CardContent, CardFooter } from "@/components/ui/card";
-import { JSONValue, useChatUI } from "@llamaindex/chat-ui";
-import React, { FC, useState } from "react";
-import { z } from "zod";
-
-// This schema is equivalent to the CLICommand model defined in events.py
-const CLIInputEventSchema = z.object({
-  command: z.string(),
-});
-type CLIInputEvent = z.infer<typeof CLIInputEventSchema>;
-
-const CLIHumanInput: FC<{
-  events: JSONValue[];
-}> = ({ events }) => {
-  const inputEvent = (events || [])
-    .map((ev) => {
-      const parseResult = CLIInputEventSchema.safeParse(ev);
-      return parseResult.success ? parseResult.data : null;
-    })
-    .filter((ev): ev is CLIInputEvent => ev !== null)
-    .at(-1);
-
-  const { append } = useChatUI();
-  const [confirmedValue, setConfirmedValue] = useState<boolean | null>(null);
-  const [editableCommand, setEditableCommand] = useState<string | undefined>(
-    inputEvent?.command,
-  );
-
-  // Update editableCommand if inputEvent changes (e.g. new event comes in)
-  React.useEffect(() => {
-    setEditableCommand(inputEvent?.command);
-  }, [inputEvent?.command]);
-
-  const handleConfirm = () => {
-    append({
-      content: "Yes",
-      role: "user",
-      annotations: [
-        {
-          type: "human_response",
-          data: {
-            execute: true,
-            command: editableCommand, // Use editable command
-          },
-        },
-      ],
-    });
-    setConfirmedValue(true);
-  };
-
-  const handleCancel = () => {
-    append({
-      content: "No",
-      role: "user",
-      annotations: [
-        {
-          type: "human_response",
-          data: {
-            execute: false,
-            command: inputEvent?.command,
-          },
-        },
-      ],
-    });
-    setConfirmedValue(false);
-  };
-
-  return (
-    <Card className="my-4">
-      <CardContent className="pt-6">
-        <p className="text-sm text-gray-700">
-          Do you want to execute the following command?
-        </p>
-        <input
-          disabled
-          type="text"
-          value={editableCommand || ""}
-          onChange={(e) => setEditableCommand(e.target.value)}
-          className="my-2 w-full overflow-x-auto rounded border border-gray-300 bg-gray-100 p-3 font-mono text-xs text-gray-800"
-        />
-      </CardContent>
-      {confirmedValue === null ? (
-        <CardFooter className="flex justify-end gap-2">
-          <>
-            <Button onClick={handleConfirm}>Yes</Button>
-            <Button onClick={handleCancel}>No</Button>
-          </>
-        </CardFooter>
-      ) : null}
-    </Card>
-  );
-};
-
-export default CLIHumanInput;
@@ -1,20 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import { LlamaIndexServer } from "@llamaindex/server";
-import { Settings } from "llamaindex";
-import { workflowFactory } from "./src/app/workflow";
-
-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  uiConfig: {
-    starterQuestions: [
-      "Check status of git in the current directory",
-      "List all files in the current directory",
-    ],
-    componentsDir: "components",
-  },
-  port: 3000,
-}).start();
@@ -1,12 +0,0 @@
-import { humanInputEvent, humanResponseEvent } from "@llamaindex/server";
-
-export const cliHumanInputEvent = humanInputEvent<{
-  type: "cli_human_input";
-  data: { command: string };
-  response: typeof cliHumanResponseEvent;
-}>();
-
-export const cliHumanResponseEvent = humanResponseEvent<{
-  type: "human_response";
-  data: { execute: boolean; command: string };
-}>();
@@ -1,20 +0,0 @@
-import { execSync } from "child_process";
-import { tool } from "llamaindex";
-import { z } from "zod";
-
-export const cliExecutor = tool({
-  name: "cli_executor",
-  description: "This tool executes a command and returns the output.",
-  parameters: z.object({ command: z.string() }),
-  execute: async ({ command }) => {
-    try {
-      const output = execSync(command, {
-        encoding: "utf-8",
-      });
-      return output;
-    } catch (error) {
-      console.error(error);
-      return "Command failed";
-    }
-  },
-});
@@ -1,106 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import { toAgentRunEvent, writeResponseToStream } from "@llamaindex/server";
-import { chatWithTools } from "@llamaindex/tools";
-import {
-  createWorkflow,
-  getContext,
-  startAgentEvent,
-  stopAgentEvent,
-  withSnapshot,
-  workflowEvent,
-} from "@llamaindex/workflow";
-import { ChatMessage, Settings, ToolCallLLM } from "llamaindex";
-import { cliHumanInputEvent, cliHumanResponseEvent } from "./events";
-import { cliExecutor } from "./tools";
-
-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
-const summaryEvent = workflowEvent<string>(); // simple event to summarize the result
-
-export const workflowFactory = (body: unknown) => {
-  const llm = Settings.llm as ToolCallLLM;
-
-  if (!llm.supportToolCall) {
-    throw new Error("LLM is not a ToolCallLLM");
-  }
-
-  const { messages } = body as { messages: ChatMessage[] };
-
-  const workflow = withSnapshot(createWorkflow());
-
-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { userInput, chatHistory = [] } = data;
-    if (!userInput) {
-      throw new Error("User input is required");
-    }
-
-    // in this example, we use chatWithTools to decide should perform a tool call or not
-    // if cli executor is called, emit HumanInputEvent to ask user for permission
-    const toolCallResponse = await chatWithTools(
-      llm,
-      [cliExecutor],
-      chatHistory.concat({ role: "user", content: userInput }),
-    );
-    const cliExecutorToolCall = toolCallResponse.toolCalls.find(
-      (toolCall) => toolCall.name === cliExecutor.metadata.name,
-    );
-    const command = cliExecutorToolCall?.input?.command as string;
-    if (command) {
-      return cliHumanInputEvent.with({
-        type: "cli_human_input",
-        data: { command },
-        response: cliHumanResponseEvent,
-      });
-    }
-
-    // if no tool call, just response as normal
-    return summaryEvent.with("");
-  });
-
-  // do actions after getting response from human
-  workflow.handle([cliHumanResponseEvent], async ({ data }) => {
-    const { sendEvent } = getContext();
-    const { command, execute } = data.data;
-
-    if (!execute) {
-      // stop the workflow if user reject to execute the command
-      return summaryEvent.with(`User reject to execute the command ${command}`);
-    }
-
-    sendEvent(
-      toAgentRunEvent({
-        agent: "CLI Executor",
-        text: `Execute the command "${command}" and return the result`,
-        type: "text",
-      }),
-    );
-
-    const result = (await cliExecutor.call({ command })) as string;
-
-    return summaryEvent.with(
-      `Executed the command ${command} and got the result: ${result}`,
-    );
-  });
-
-  workflow.handle([summaryEvent], async ({ data: summaryResult }) => {
-    const { sendEvent } = getContext();
-
-    const chatHistory = messages;
-    if (summaryResult) {
-      chatHistory.push({ role: "user", content: summaryResult });
-    }
-
-    const stream = await llm.chat({
-      messages: chatHistory,
-      stream: true,
-    });
-
-    const result = await writeResponseToStream(stream, sendEvent);
-
-    return stopAgentEvent.with({ result });
-  });
-
-  return workflow;
-};
@@ -4,21 +4,21 @@
  "private": true,
  "scripts": {
    "typecheck": "tsc --noEmit",
-    "dev": "nodemon --exec tsx simple-workflow/calculator.ts"
+    "dev": "tsx simple-workflow/calculator.ts"
  },
  "dependencies": {
-    "@llamaindex/openai": "~0.4.0",
-    "@llamaindex/readers": "~3.1.4",
+    "@llamaindex/openai": "^0.2.0",
+    "@llamaindex/readers": "^3.0.0",
    "@llamaindex/server": "workspace:*",
-    "@llamaindex/tools": "~0.0.11",
+    "@llamaindex/tools": "0.0.4",
+    "@llamaindex/workflow": "1.1.0",
    "dotenv": "^16.4.7",
-    "llamaindex": "~0.11.0",
-    "zod": "^3.24.2"
+    "llamaindex": "0.10.2",
+    "zod": "^3.23.8"
  },
  "devDependencies": {
    "@types/node": "^20.10.3",
-    "nodemon": "^3.1.10",
-    "tsx": "4.7.2",
+    "tsx": "^4.7.2",
    "typescript": "^5.3.2"
  }
 }
@@ -1,68 +0,0 @@
-# Upload File Example
-
-This example shows how to use the uploaded file (private file) from the user in the workflow.
-
-## Prerequisites
-
-Please follow the setup instructions in the [examples README](../README.md).
-
-You will also need:
-
- An OpenAI API key
- The `enableFileUpload` option in the `uiConfig` is set to `true`.
-
-```typescript
-new LlamaIndexServer({
-  // ... other options
-  uiConfig: { enableFileUpload: true },
-}).start();
-```
-
-## How to get the uploaded files in your workflow:
-
-In LlamaIndexServer, the uploaded file is included in chat message annotations. You can easily get the uploaded files from chat messages using the [extractFileAttachments](https://github.com/llamaindex/llamaindex/blob/main/packages/server/src/utils/events.ts) function.
-
-```typescript
-import { type Message } from "ai";
-import { extractFileAttachments } from "@llamaindex/server";
-
-async function workflowFactory(reqBody: { messages: Message[] }) {
-  const attachments = extractFileAttachments(reqBody.messages);
-  // ...
-}
-```
-
-### AgentWorkflow
-
-If you are using AgentWorkflow, to provide file access to the agent, you can create a tool to read the file content. We recommend to use the `fileId` as the parameter of the tool instead of the `filePath` to avoid showing internal file path to the user. You can use the `getStoredFilePath` helper function to get the file path from the file id.
-
-```typescript
-import { getStoredFilePath, extractFileAttachments } from "@llamaindex/server";
-
-const readFileTool = tool(
-  ({ fileId }) => {
-    // Get the file path from the file id
-    const filePath = getStoredFilePath({ id: fileId });
-    return fsPromises.readFile(filePath, "utf8");
-  },
-  {
-    name: "read_file",
-    description: `Use this tool with the file id to read the file content. The available file are: [${attachments.map((file) => file.id).join(", ")}]`,
-    parameters: z.object({
-      fileId: z.string(),
-    }),
-  },
-);
-```
-
-**Tip:** You can either put the attachments file information to the tool description or agent's system prompt.
-
-Check: [agent-workflow.ts](./agent-workflow.ts) for the full example.
-
-### Custom Workflow
-
-In custom workflow, instead of defining a tool, you can use the helper functions (`extractFileAttachments` and `getStoredFilePath`) to work with file attachments in your workflow.
-
-Check: [custom-workflow.ts](./custom-workflow.ts) for the full example.
-
-> To run custom workflow example, update the `index.ts` file to use the `workflowFactory` from `custom-workflow.ts` instead of `agent-workflow.ts`.
@@ -1,39 +0,0 @@
-import { extractFileAttachments, getStoredFilePath } from "@llamaindex/server";
-import { agent } from "@llamaindex/workflow";
-import { type Message } from "ai";
-import { tool } from "llamaindex";
-import { promises as fsPromises } from "node:fs";
-import { z } from "zod";
-
-export const workflowFactory = async (reqBody: { messages: Message[] }) => {
-  const { messages } = reqBody;
-  // Extract the files from the messages
-  const files = extractFileAttachments(messages);
-  const fileIds = files.map((file) => file.id);
-
-  // Define a tool to read the file content using the id
-  const readFileTool = tool(
-    ({ fileId }) => {
-      if (!fileIds.includes(fileId)) {
-        throw new Error(`File with id ${fileId} not found`);
-      }
-
-      const filePath = getStoredFilePath({ id: fileId });
-      return fsPromises.readFile(filePath, "utf8");
-    },
-    {
-      name: "read_file",
-      description: `Use this tool with the id of the file to read the file content. Here are the available file ids: [${fileIds.join(", ")}]`,
-      parameters: z.object({
-        fileId: z.string(),
-      }),
-    },
-  );
-  return agent({
-    tools: [readFileTool],
-    systemPrompt: `
-      You are a helpful assistant that can help the user with their file.
-      You can use the read_file tool to read the file content.
-    `,
-  });
-};
@@ -1,98 +0,0 @@
-import { extractFileAttachments } from "@llamaindex/server";
-import { ChatMemoryBuffer, MessageContent, Settings } from "llamaindex";
-
-import {
-  agentStreamEvent,
-  createStatefulMiddleware,
-  createWorkflow,
-  startAgentEvent,
-  stopAgentEvent,
-  workflowEvent,
-} from "@llamaindex/workflow";
-import { Message } from "ai";
-import { promises as fsPromises } from "node:fs";
-
-const fileHelperEvent = workflowEvent<{
-  userInput: MessageContent;
-  fileContent: string;
-}>();
-
-/**
- * This is an simple workflow to demonstrate how to use uploaded files in the workflow.
- */
-export function workflowFactory(reqBody: { messages: Message[] }) {
-  const llm = Settings.llm;
-
-  // First, extract the uploaded file from the messages
-  const attachments = extractFileAttachments(reqBody.messages);
-
-  if (attachments.length === 0) {
-    throw new Error("Please upload a file to start");
-  }
-
-  // Then, add the uploaded file info to the workflow state
-  const { withState, getContext } = createStatefulMiddleware(() => {
-    return {
-      memory: new ChatMemoryBuffer({ llm }),
-      uploadedFile: attachments[attachments.length - 1],
-    };
-  });
-  const workflow = withState(createWorkflow());
-
-  // Handle the start of the workflow: read the file content
-  workflow.handle([startAgentEvent], async ({ data }) => {
-    const { userInput } = data;
-    // Prepare chat history
-    const { state } = getContext();
-    if (!userInput) {
-      throw new Error("Missing user input to start the workflow");
-    }
-    state.memory.put({ role: "user", content: userInput });
-
-    // Read file content
-    const fileContent = await fsPromises.readFile(
-      state.uploadedFile.path,
-      "utf8",
-    );
-
-    return fileHelperEvent.with({
-      userInput,
-      fileContent,
-    });
-  });
-
-  // Use LLM to help the user with the file content
-  workflow.handle([fileHelperEvent], async ({ data }) => {
-    const { sendEvent } = getContext();
-
-    const prompt = `
-You are a helpful assistant that can help the user with their file.
-
-Here is the provided file content:
-${data.fileContent}
-
-Now, let help the user with this request:
-${data.userInput}
-`;
-
-    const response = await llm.complete({
-      prompt,
-      stream: true,
-    });
-
-    // Stream the response
-    for await (const chunk of response) {
-      sendEvent(
-        agentStreamEvent.with({
-          delta: chunk.text,
-          response: chunk.text,
-          currentAgentName: "agent",
-          raw: chunk.raw,
-        }),
-      );
-    }
-    sendEvent(stopAgentEvent.with({ result: "" }));
-  });
-
-  return workflow;
-}
@@ -1,23 +0,0 @@
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
-import { LlamaIndexServer } from "@llamaindex/server";
-import { Settings } from "llamaindex";
-import { workflowFactory } from "./agent-workflow";
-// Uncomment this to use a custom workflow
-// import { workflowFactory } from "./custom-workflow";
-
-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
-Settings.embedModel = new OpenAIEmbedding({
-  model: "text-embedding-3-small",
-});
-
-new LlamaIndexServer({
-  workflow: workflowFactory,
-  suggestNextQuestions: false,
-  uiConfig: {
-    enableFileUpload: true,
-  },
-  port: 3000,
-}).start();
@@ -1,13 +1,8 @@
-import { OpenAI } from "@llamaindex/openai";
 import { LlamaIndexServer } from "@llamaindex/server";
 import { agent } from "@llamaindex/workflow";
-import { Settings, tool } from "llamaindex";
+import { tool } from "llamaindex";
 import { z } from "zod";

-Settings.llm = new OpenAI({
-  model: "gpt-4o-mini",
-});
-
 const calculatorAgent = agent({
  tools: [
    tool({
@@ -22,7 +17,8 @@ const calculatorAgent = agent({
 new LlamaIndexServer({
  workflow: () => calculatorAgent,
  uiConfig: {
+    appTitle: "Calculator",
    starterQuestions: ["1 + 1", "2 + 2"],
  },
-  port: 3000,
+  port: 4000,
 }).start();
@@ -10,5 +10,5 @@
    "outDir": "dist"
  },
  "include": ["**/*"],
-  "exclude": ["node_modules", "dist", "custom-layout/layout", "hitl/components"]
+  "exclude": ["node_modules", "dist"]
 }
@@ -26,7 +26,6 @@ yarn-error.log*

 # local env files
 .env*.local
-.env

 # vercel
 .vercel
@@ -36,6 +35,5 @@ yarn-error.log*
 next-env.d.ts

 output/
-storage/

 !lib/
@@ -1,45 +0,0 @@
-This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Next.js](https://nextjs.org/) that is ejected from [`llamaindex-server`](https://github.com/run-llama/create-llama/tree/main/packages/server) via `npm eject` command.
-
-## Quick Start
-
-As this is a Next.js project, you can use the following commands to start the development server:
-
-```bash
-npm install
-npm run dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-## Useful Commands
-
- Generate Datasource (in case you're having a `./data` folder): `npm run generate`
- Typecheck: `npm run typecheck`
- Lint: `npm run lint`
- Format: `npm run format`
- Build & Start: `npm run build && npm run start`
-
-## Deployment
-
-The project can be deployed to any platform that supports Next.js like Vercel.
-
-## Configuration
-
-Your original [`llamaindex-server`](https://github.com/run-llama/create-llama/tree/main/packages/server#configuration-options) configurations have been migrated to a [`.env`](.env) file.
-
-Changing the `.env` file will change the behavior of the application, e.g. for changing the initial questions to display in the chat, you can do:
-
-```
-NEXT_PUBLIC_STARTER_QUESTIONS=['What is the capital of France?']
-```
-
-Alternatively, you can also change the file referencing `process.env.NEXT_PUBLIC_STARTER_QUESTIONS` directly in the source code.
-
-## Learn More
-
-To learn more about LlamaIndex, take a look at the following resources:
-
- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
- [LlamaIndexTS Documentation](https://ts.llamaindex.ai) - learn about LlamaIndex (Typescript features).
-
-You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!
@@ -1,32 +0,0 @@
-import { getEnv } from "@llamaindex/env";
-import { LLamaCloudFileService } from "llamaindex";
-import { NextRequest, NextResponse } from "next/server";
-
-export async function GET(request: NextRequest): Promise<NextResponse> {
-  if (!getEnv("LLAMA_CLOUD_API_KEY")) {
-    return NextResponse.json(
-      {
-        error: "env variable LLAMA_CLOUD_API_KEY is required to use LlamaCloud",
-      },
-      { status: 500 },
-    );
-  }
-
-  try {
-    const config = {
-      projects: await LLamaCloudFileService.getAllProjectsWithPipelines(),
-      pipeline: {
-        pipeline: getEnv("LLAMA_CLOUD_INDEX_NAME"),
-        project: getEnv("LLAMA_CLOUD_PROJECT_NAME"),
-      },
-    };
-    return NextResponse.json(config, { status: 200 });
-  } catch (error) {
-    return NextResponse.json(
-      {
-        error: "Failed to fetch LlamaCloud configuration",
-      },
-      { status: 500 },
-    );
-  }
-}
@@ -1,97 +0,0 @@
-import { type Message } from "ai";
-import { type MessageType } from "llamaindex";
-import { NextRequest, NextResponse } from "next/server";
-
-// import chat utils
-import {
-  getHumanResponsesFromMessage,
-  pauseForHumanInput,
-  processWorkflowStream,
-  runWorkflow,
-  sendSuggestedQuestionsEvent,
-  toDataStream,
-} from "./utils";
-
-// import workflow factory and settings from local file
-import { stopAgentEvent } from "@llamaindex/workflow";
-import { initSettings } from "./app/settings";
-import { workflowFactory } from "./app/workflow";
-
-initSettings();
-
-export async function POST(req: NextRequest) {
-  try {
-    const reqBody = await req.json();
-    const suggestNextQuestions = process.env.SUGGEST_NEXT_QUESTIONS === "true";
-
-    const { messages, id: requestId } = reqBody as {
-      messages: Message[];
-      id?: string;
-    };
-    const chatHistory = messages.map((message) => ({
-      role: message.role as MessageType,
-      content: message.content,
-    }));
-
-    const lastMessage = messages[messages.length - 1];
-    if (lastMessage?.role !== "user") {
-      return NextResponse.json(
-        {
-          detail: "Messages cannot be empty and last message must be from user",
-        },
-        { status: 400 },
-      );
-    }
-
-    const abortController = new AbortController();
-    req.signal.addEventListener("abort", () =>
-      abortController.abort("Connection closed"),
-    );
-
-    const context = await runWorkflow({
-      workflow: await workflowFactory(reqBody),
-      input: { userInput: lastMessage.content, chatHistory },
-      human: {
-        snapshotId: requestId, // use requestId to restore snapshot
-        responses: getHumanResponsesFromMessage(lastMessage),
-      },
-    });
-
-    const stream = processWorkflowStream(context.stream).until(
-      (event) =>
-        abortController.signal.aborted || stopAgentEvent.include(event),
-    );
-
-    const dataStream = toDataStream(stream, {
-      callbacks: {
-        onPauseForHumanInput: async (responseEvent) => {
-          await pauseForHumanInput(context, responseEvent, requestId); // use requestId to save snapshot
-        },
-        onFinal: async (completion, dataStreamWriter) => {
-          chatHistory.push({
-            role: "assistant" as MessageType,
-            content: completion,
-          });
-          if (suggestNextQuestions) {
-            await sendSuggestedQuestionsEvent(dataStreamWriter, chatHistory);
-          }
-        },
-      },
-    });
-    return new Response(dataStream, {
-      status: 200,
-      headers: {
-        "Content-Type": "text/plain; charset=utf-8",
-        "X-Vercel-AI-Data-Stream": "v1",
-      },
-    });
-  } catch (error) {
-    console.error("Chat handler error:", error);
-    return NextResponse.json(
-      {
-        detail: (error as Error).message || "Internal server error",
-      },
-      { status: 500 },
-    );
-  }
-}
@@ -1,9 +0,0 @@
-import { NextRequest } from "next/server";
-import { handleComponentRoute } from "../shared/component-handler";
-
-export async function GET(request: NextRequest) {
-  const params = request.nextUrl.searchParams;
-  const directory =
-    params.get("componentsDir") || process.env.COMPONENTS_DIR || "components";
-  return handleComponentRoute(directory);
-}
@@ -1,97 +0,0 @@
-import { exec } from "child_process";
-import fs from "fs";
-import { NextRequest, NextResponse } from "next/server";
-import path from "path";
-import { promisify } from "util";
-
-const DEFAULT_WORKFLOW_FILE_PATH =
-  process.env.WORKFLOW_FILE_PATH || "src/app/workflow.ts";
-
-export async function GET(request: NextRequest) {
-  const filePath = DEFAULT_WORKFLOW_FILE_PATH;
-
-  const fileExists = await promisify(fs.exists)(DEFAULT_WORKFLOW_FILE_PATH);
-  if (!fileExists) {
-    return NextResponse.json(
-      {
-        detail: `Dev mode is currently in beta. It only supports updating workflow file at ${filePath}`,
-      },
-      { status: 404 },
-    );
-  }
-
-  const content = await promisify(fs.readFile)(filePath, "utf-8");
-  const last_modified = fs.statSync(filePath).mtime.getTime();
-
-  return NextResponse.json(
-    { content, file_path: filePath, last_modified },
-    { status: 200 },
-  );
-}
-
-export async function PUT(request: NextRequest) {
-  const filePath = DEFAULT_WORKFLOW_FILE_PATH;
-  const { content } = await request.json();
-
-  const fileExists = await promisify(fs.exists)(filePath);
-  if (!fileExists) {
-    return NextResponse.json(
-      {
-        detail: `Dev mode is currently in beta. It only supports updating workflow file at ${DEFAULT_WORKFLOW_FILE_PATH}`,
-      },
-      { status: 404 },
-    );
-  }
-
-  try {
-    const resolvedFilePath = path.resolve(DEFAULT_WORKFLOW_FILE_PATH);
-    const result = await validateTypeScriptFile(resolvedFilePath, content);
-
-    if (!result.isValid) {
-      return NextResponse.json(
-        {
-          detail: result.errors.join("\n"),
-        },
-        { status: 400 },
-      );
-    }
-
-    await promisify(fs.writeFile)(filePath, content);
-    return NextResponse.json({ content }, { status: 200 });
-  } catch (error) {
-    console.error("Error updating workflow file:", error);
-    return NextResponse.json(
-      { error: "Failed to update workflow file" },
-      { status: 500 },
-    );
-  }
-}
-
-// use typescript package to validate the file syntax and imports
-async function validateTypeScriptFile(filePath: string, content: string) {
-  // Update workflow file directly will cause the server restart immediately.
-  // So we create a temporary file with the same content in the same directory as the workflow file
-  // This file will be used to validate the file syntax and imports. It will be deleted after validation.
-  const tempFilePath = path.join(
-    path.dirname(filePath),
-    `workflow_${Date.now()}.ts`,
-  );
-  fs.writeFileSync(tempFilePath, content);
-
-  const errors = [];
-  try {
-    const tscCommand = `npx tsc ${tempFilePath} --noEmit --skipLibCheck true`;
-    await promisify(exec)(tscCommand);
-  } catch (error) {
-    const errorMessage = (error as { stdout: string })?.stdout;
-    errors.push(errorMessage);
-  } finally {
-    // Clean up temporary file
-    if (fs.existsSync(tempFilePath)) fs.unlinkSync(tempFilePath);
-  }
-
-  return {
-    isValid: errors.length === 0,
-    errors: errors,
-  };
-}
@@ -1,24 +0,0 @@
-import fs from "fs";
-import { NextRequest, NextResponse } from "next/server";
-import { promisify } from "util";
-
-export async function GET(
-  request: NextRequest,
-  { params }: { params: Promise<{ slug: string[] }> },
-) {
-  const filePath = (await params).slug.join("/");
-
-  if (!filePath.startsWith("output") && !filePath.startsWith("data")) {
-    return NextResponse.json({ error: "No permission" }, { status: 400 });
-  }
-
-  const decodedFilePath = decodeURIComponent(filePath);
-  const fileExists = await promisify(fs.exists)(decodedFilePath);
-
-  if (fileExists) {
-    const fileBuffer = await promisify(fs.readFile)(decodedFilePath);
-    return new NextResponse(fileBuffer);
-  } else {
-    return NextResponse.json({ error: "File not found" }, { status: 404 });
-  }
-}
@@ -1,57 +0,0 @@
-import crypto from "node:crypto";
-import fs from "node:fs";
-import path from "node:path";
-
-import { type ServerFile } from "@llamaindex/server";
-
-export const UPLOADED_FOLDER = "output/uploaded";
-
-export async function storeFile(
-  name: string,
-  fileBuffer: Buffer,
-): Promise<ServerFile> {
-  const parts = name.split(".");
-  const fileName = parts[0];
-  const fileExt = parts[1];
-  if (!fileName) {
-    throw new Error("File name is required");
-  }
-  if (!fileExt) {
-    throw new Error("File extension is required");
-  }
-
-  const id = crypto.randomUUID();
-  const fileId = `${sanitizeFileName(fileName)}_${id}.${fileExt}`;
-  const filepath = path.join(UPLOADED_FOLDER, fileId);
-  const fileUrl = await saveFile(filepath, fileBuffer);
-  return {
-    id: fileId,
-    size: fileBuffer.length,
-    type: fileExt,
-    url: fileUrl,
-    path: filepath,
-  };
-}
-
-// Save document to file server and return the file url
-async function saveFile(filepath: string, content: string | Buffer) {
-  if (path.isAbsolute(filepath)) {
-    throw new Error("Absolute file paths are not allowed.");
-  }
-
-  const dirPath = path.dirname(filepath);
-  await fs.promises.mkdir(dirPath, { recursive: true });
-
-  if (typeof content === "string") {
-    await fs.promises.writeFile(filepath, content, "utf-8");
-  } else {
-    await fs.promises.writeFile(filepath, content);
-  }
-
-  const fileurl = `/api/files/${filepath}`;
-  return fileurl;
-}
-
-function sanitizeFileName(fileName: string) {
-  return fileName.replace(/[^a-zA-Z0-9_-]/g, "_");
-}
@@ -1,49 +0,0 @@
-import { type FileAnnotation } from "@llamaindex/server";
-import { NextRequest, NextResponse } from "next/server";
-import { storeFile } from "./helpers";
-
-export async function POST(request: NextRequest) {
-  try {
-    const {
-      name,
-      base64,
-    }: {
-      name: string;
-      base64: string;
-    } = await request.json();
-    if (!base64 || !name) {
-      return NextResponse.json(
-        { error: "base64 and name is required in the request body" },
-        { status: 400 },
-      );
-    }
-
-    const parts = base64.split(",");
-    if (parts.length !== 2) {
-      return NextResponse.json(
-        { error: "Invalid base64 format" },
-        { status: 400 },
-      );
-    }
-
-    const [header, content] = parts;
-    if (!header || !content) {
-      return NextResponse.json(
-        { error: "Invalid base64 format" },
-        { status: 400 },
-      );
-    }
-
-    const fileBuffer = Buffer.from(content, "base64");
-
-    const file = await storeFile(name, fileBuffer);
-
-    return NextResponse.json(file as FileAnnotation);
-  } catch (error) {
-    console.error("[Upload API]", error);
-    return NextResponse.json(
-      { error: (error as Error).message },
-      { status: 500 },
-    );
-  }
-}
@@ -1,10 +0,0 @@
-import { NextRequest } from "next/server";
-import { handleComponentRoute } from "../shared/component-handler";
-
-const LAYOUT_TYPES = ["header", "footer"] as const;
-
-export async function GET(request: NextRequest) {
-  const params = request.nextUrl.searchParams;
-  const directory = params.get("layoutDir") || "layout";
-  return handleComponentRoute(directory, LAYOUT_TYPES);
-}
@@ -1,80 +0,0 @@
-import fs from "fs";
-import { NextResponse } from "next/server";
-import path from "path";
-import { promisify } from "util";
-
-const VALID_EXTENSIONS = [".tsx", ".jsx"];
-
-export type Item = {
-  type: string;
-  filename: string;
-  code: string;
-};
-
-function filterDuplicateFiles(files: string[]): string[] {
-  const fileMap = new Map<string, string>();
-
-  for (const file of files) {
-    const type = path.basename(file, path.extname(file));
-
-    if (fileMap.has(type)) {
-      const existingFile = fileMap.get(type)!;
-      // Prefer .tsx files
-      if (file.endsWith(".tsx") && !existingFile.endsWith(".tsx")) {
-        console.warn(`Preferring ${file} over ${existingFile}`);
-        fileMap.set(type, file);
-      }
-    } else {
-      fileMap.set(type, file);
-    }
-  }
-  return Array.from(fileMap.values());
-}
-
-export async function handleComponentRoute(
-  directory: string,
-  itemTypes?: readonly string[],
-): Promise<NextResponse> {
-  try {
-    const exists = await promisify(fs.exists)(directory);
-    if (!exists) {
-      return NextResponse.json(
-        { error: `Directory not found at ${directory}` },
-        { status: 404 },
-      );
-    }
-
-    const filesInDir = await promisify(fs.readdir)(directory);
-    const validFiles = filesInDir.filter((file) =>
-      VALID_EXTENSIONS.includes(path.extname(file)),
-    );
-    let filesToProcess = filterDuplicateFiles(validFiles);
-
-    if (itemTypes?.length) {
-      // Specific item types provided (e.g., for layouts "header", "footer")
-      filesToProcess = filesToProcess.filter((file) =>
-        itemTypes.includes(path.basename(file, path.extname(file))),
-      );
-    }
-
-    const items: Item[] = await Promise.all(
-      filesToProcess.map(async (file) => {
-        const filePath = path.join(directory, file);
-        const content = await promisify(fs.readFile)(filePath, "utf-8");
-        return {
-          type: path.basename(file, path.extname(file)),
-          code: content,
-          filename: file,
-        };
-      }),
-    );
-
-    return NextResponse.json(items, { status: 200 });
-  } catch (error) {
-    console.error(`Error reading directory ${directory}:`, error);
-    return NextResponse.json(
-      { error: `Failed to read directory ${directory}` },
-      { status: 500 },
-    );
-  }
-}
@@ -60,12 +60,12 @@ function Calendar({
        ...classNames,
      }}
      components={{
-        Chevron: ({ ...props }) =>
-          props.orientation === "left" ? (
-            <ChevronLeft {...props} className="h-4 w-4" />
-          ) : (
-            <ChevronRight {...props} className="h-4 w-4" />
-          ),
+        IconLeft: ({ className, ...props }) => (
+          <ChevronLeft className={cn("size-4", className)} {...props} />
+        ),
+        IconRight: ({ className, ...props }) => (
+          <ChevronRight className={cn("size-4", className)} {...props} />
+        ),
      }}
      {...props}
    />
@@ -0,0 +1,55 @@
+"use client";
+
+import { Sparkles, Star } from "lucide-react";
+import { Button } from "../button";
+import { getConfig } from "../lib/utils";
+
+export function ChatHeader() {
+  return (
+    <div className="flex items-center justify-between px-4 pt-2">
+      <ChatAppTitle />
+      <LlamaIndexLinks />
+    </div>
+  );
+}
+
+function ChatAppTitle() {
+  return (
+    <div className="flex items-center gap-2">
+      <Sparkles className="size-4" />
+      <h1 className="font-semibold">{getConfig("APP_TITLE")}</h1>
+    </div>
+  );
+}
+
+function LlamaIndexLinks() {
+  return (
+    <div className="flex items-center justify-end gap-4">
+      <div className="flex items-center gap-2">
+        <a
+          href="https://www.llamaindex.ai/"
+          target="_blank"
+          rel="noopener noreferrer"
+          className="text-sm text-gray-600 hover:text-gray-800 dark:text-gray-400 dark:hover:text-gray-200"
+        >
+          Built by LlamaIndex
+        </a>
+        <img
+          className="h-[24px] w-[24px] rounded-sm"
+          src="/llama.png"
+          alt="Llama Logo"
+        />
+      </div>
+      <a
+        href="https://github.com/run-llama/LlamaIndexTS"
+        target="_blank"
+        rel="noopener noreferrer"
+      >
+        <Button variant="outline" size="sm">
+          <Star className="mr-2 size-4" />
+          Star on GitHub
+        </Button>
+      </a>
+    </div>
+  );
+}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
leehuwuj	66ee88719e	refactor: remove redundant UI event handling in workflows	2025-05-15 13:40:43 +07:00
leehuwuj	2cbad524ce	better handler typing	2025-05-15 13:36:37 +07:00
Marcus Schiesser	fd0df23906	docs: word smith	2025-05-15 10:42:29 +07:00
leehuwuj	511f0da60e	fix: improve type handling and clean up UI event component - Removed unnecessary string conversion for userInput in code_generator and deep_research workflows. - Updated userRequest type to MessageContent for better type safety. - Cleaned up the UI event component by removing redundant indicatorClassName logic.	2025-05-15 09:12:51 +07:00
leehuwuj	6062b7b68e	Merge remote-tracking branch 'origin/main' into lee/separate-artifacts	2025-05-14 13:39:12 +07:00
leehuwuj	9871920dc0	fix typing	2025-05-14 13:31:26 +07:00
leehuwuj	e51d28cd37	fix package	2025-05-14 11:36:03 +07:00
leehuwuj	e55f93fa34	bump openai	2025-05-14 11:04:54 +07:00
leehuwuj	d0ae22eb90	fix typing	2025-05-14 10:41:07 +07:00
leehuwuj	808b392949	fix package version	2025-05-14 10:07:09 +07:00
leehuwuj	ecd0a9865b	add changeset	2025-05-14 09:56:57 +07:00
leehuwuj	960cdeb326	split artifacts use case to code generator and document generator	2025-05-14 09:56:19 +07:00