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run-locally

Run and test the TypeScript LangChain agent locally. Use when: (1) User wants to test locally, (2) User says 'run locally', 'test agent', 'start server', or 'dev mode', (3) Debugging issues.

90

Quality

87%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Run Locally

Start Development Servers

Start both agent and UI servers:

npm run dev

This starts:

  • Agent server on port 5001 (provides /invocations)
  • UI server on port 3001 (provides /api/chat and React frontend)
  • Hot-reload enabled for both

Or start individually:

# Terminal 1: Agent only
npm run dev:agent

# Terminal 2: UI only
npm run dev:ui

Servers will be available at:

  • Agent: http://localhost:5001/invocations
  • UI frontend: http://localhost:3000
  • UI backend: http://localhost:3001/api/chat

Start Production Build

# Build first
npm run build

# Then start
npm start

Testing the Agent

1. Test /invocations Endpoint (Responses API)

curl -X POST http://localhost:5001/invocations \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      {"role": "user", "content": "What is the weather in San Francisco?"}
    ],
    "stream": true
  }'

Expected response (Server-Sent Events):

data: {"type":"response.output_item.added","item":{"type":"message",...}}
data: {"type":"response.output_text.delta","delta":"The weather..."}
...
data: {"type":"response.completed"}
data: [DONE]

2. Test /api/chat Endpoint (useChat Format)

Requires both servers running (npm run dev)

curl -X POST http://localhost:3001/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "message": {
      "role": "user",
      "parts": [{"type": "text", "text": "Calculate 15 * 32"}]
    },
    "selectedChatModel": "chat-model"
  }'

Expected response (AI SDK format):

data: {"type":"text-delta","delta":"Let me calculate..."}
data: {"type":"tool-call",...}
...
data: [DONE]

3. Test UI Frontend

Open browser: http://localhost:3000

Should see chat interface with:

  • Message input
  • Send button
  • Chat history
  • Tool call indicators

Environment Variables

Make sure .env is configured (see quickstart skill):

# Required
DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
DATABRICKS_TOKEN=dapi...
DATABRICKS_MODEL=databricks-claude-sonnet-4-5
MLFLOW_TRACKING_URI=databricks
MLFLOW_EXPERIMENT_ID=123

# Optional
PORT=8000
TEMPERATURE=0.1
MAX_TOKENS=2000
ENABLE_SQL_MCP=false

View MLflow Traces

See MLflow Tracing Guide for viewing traces in your workspace.

Development Tips

Watch Mode

npm run dev uses tsx watch which:

  • Auto-restarts on file changes
  • Preserves type checking
  • Fast compilation

TypeScript Compilation

Manual compilation:

npm run build

Output in dist/ directory.

Debugging

Add console.log() statements and view in terminal:

console.log("Tool invoked:", toolName);
console.log("Result:", result);

For deeper debugging, use VS Code debugger:

  1. Set breakpoints in .ts files
  2. Press F5 or use Run > Start Debugging
  3. Select "Node.js" as runtime

Testing Tools

Test Basic Tools

# Weather tool
curl -X POST http://localhost:5001/invocations \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": "What is the weather in Tokyo?"}], "stream": false}'

# Calculator tool
curl -X POST http://localhost:5001/invocations \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": "Calculate 123 * 456"}], "stream": false}'

# Time tool
curl -X POST http://localhost:5001/invocations \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": "What time is it in London?"}], "stream": false}'

Test MCP Tools

MCP tools are configured in src/mcp-servers.ts. See add-tools skill for details.

Example test:

curl -X POST http://localhost:5001/invocations \
  -H "Content-Type: application/json" \
  -d '{"input": [{"role": "user", "content": "Query my database"}], "stream": false}'

Running Tests

Unit Tests (No Server Required)

Pure tests with no dependencies:

npm run test:unit

Runs tests/agent.test.ts - tests agent initialization, tool usage, multi-turn conversations.

Integration Tests (Requires Local Servers)

Tests that need local servers running:

# Terminal 1: Start servers
npm run dev

# Terminal 2: Run tests
npm run test:integration

Tests: /invocations, /api/chat, streaming, error handling.

E2E Tests (Requires Deployed App)

Tests that need a deployed Databricks app:

# 1. Deploy app
npm run build
databricks bundle deploy --profile your-profile
databricks bundle run agent_langchain_ts --profile your-profile

# 2. Set APP_URL
export APP_URL=$(databricks apps get agent-lc-ts-dev --profile your-profile --output json | jq -r '.url')

# 3. Run E2E tests
npm run test:e2e

See tests/e2e/README.md for detailed setup instructions.

All Non-E2E Tests

npm run test:all

Runs unit + integration tests (not E2E).

Troubleshooting

See Troubleshooting Guide for common issues.

Quick Fixes

Port already in use:

lsof -ti:5001 | xargs kill -9  # Agent
lsof -ti:3001 | xargs kill -9  # UI backend
lsof -ti:3000 | xargs kill -9  # UI frontend

Authentication failed:

Verify credentials:

databricks auth profiles
databricks auth env --host
databricks auth env --token

Re-run quickstart:

npm run quickstart

"Module not found"

Install dependencies:

npm install

"MLflow traces not appearing"

Check:

  1. MLFLOW_EXPERIMENT_ID is set in .env
  2. Experiment exists: databricks experiments get --experiment-id $MLFLOW_EXPERIMENT_ID
  3. Server logs show "MLflow tracing initialized"

Create experiment if missing:

databricks experiments create \
  --experiment-name "/Users/$(databricks current-user me --output json | jq -r .userName)/agent-langchain-ts"

"Tool not working"

Check tool invocation in response intermediateSteps:

curl -s http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "What is 2+2?"}]}' | jq '.intermediateSteps'

Should show tool name and observation.

Performance Monitoring

Monitor server logs for:

  • Request timing
  • Tool execution time
  • Error rates
  • Token usage

Add logging in src/server.ts:

console.log(`Request completed in ${duration}ms`);
Repository
databricks/app-templates
Commit

dfeb4ac

Last updated
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