improve SSE rendering in demos

2026-06-17 15:25:17 +02:00 · 2026-01-15 03:29:36 -08:00 · 2026-01-15 03:29:36 -08:00 · ce2b6d9acf
commit ce2b6d9acf
parent 611c082a7f
9 changed files with 817 additions and 746 deletions
--- a/demos/use_cases/langchain/README.md
+++ b/demos/use_cases/langchain/README.md
@ -1,141 +1,25 @@
 # Travel Booking Agent Demo (LangChain-first)

-A lightweight LangChain-powered multi-agent travel booking system that runs two small agents behind Plano’s router: a weather agent and a flight agent. Each agent is implemented with LangChain tool-calling out of the box and kept minimal so you can read, tweak, and extend quickly.
+A lightweight **LangChain-powered** multi-agent travel booking system that runs two agents behind Plano's router: a weather agent and a flight agent. Each agent is implemented with LangChain's tool-calling capabilities for a clean, modular design.

 ## Overview

-This demo consists of two LangChain agents that work together seamlessly:
+This demo showcases how to integrate LangChain agents with Plano:

- **Weather Agent** - Real-time weather conditions and multi-day forecasts for any city worldwide
- **Flight Agent** - Live flight information between airports with real-time tracking
+- **Weather Agent** - Uses `@tool` decorator to fetch real-time weather from Open-Meteo API
+- **Flight Agent** - Uses `@tool` decorator to search flights via FlightAware API

-Both agents are plain LangChain tool-callers. Plano routes traffic based on intent and forwards to the right LangChain agent. Everything runs in Docker for quick start.
-
-## Features
-
- **Lightweight code**: Minimal prompts + tools you can read in one pass
- **Intelligent routing**: Plano auto-routes to weather vs flight
- **Real-time data**: Weather (Open-Meteo) + flights (FlightAware)
- **Multi-day forecasts**: Up to 16 days for weather
-
-## Prerequisites
-
- Docker and Docker Compose
- [Plano CLI](https://docs.planoai.dev) installed
- OpenAI API key
-
-## Quick Start
-
-### 1. Set Environment Variables
-
-Create a `.env` file or export environment variables:
-
-```bash
-export AEROAPI_KEY="your-flightaware-api-key"  # Optional, demo key included
-```
-
-### 2. Start All Agents with Docker
-
-```bash
-chmod +x start_agents.sh
-./start_agents.sh
-```
-
-Or directly:
-
-```bash
-docker compose up --build
-```
-
-This starts:
- Weather Agent on port 10510
- Flight Agent on port 10520
- Open WebUI on port 8080
-
-### 3. Start Plano Orchestrator
-
-In a new terminal:
-
-```bash
-cd /path/to/travel_agents
-planoai up config.yaml
-# Or if installed with uv: uvx planoai up config.yaml
-```
-
-The gateway will start on port 8001 and route requests to the appropriate agents.
-
-### 4. Test the System
-
-**Option 1**: Use Open WebUI at http://localhost:8080
-
-**Option 2**: Send requests directly to Plano Orchestrator:
-
-```bash
-curl http://localhost:8001/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "gpt-4o",
-    "messages": [
-      {"role": "user", "content": "What is the weather like in Paris?"}
-    ]
-  }'
-```
-
-## Example Conversations
-
-### Weather Query
-```
-User: What's the weather in Istanbul?
-Assistant: [Weather Agent provides current conditions and forecast]
-```
-
-### Flight Search
-```
-User: What flights go from London to Seattle?
-Assistant: [Flight Agent shows available flights with schedules and status]
-```
-
-### Multi-Agent Conversation
-```
-User: What's the weather in Istanbul?
-Assistant: [Weather information]
-
-User: Do they fly out from Seattle?
-Assistant: [Flight information from Istanbul to Seattle]
-```
-
-The system understands context and pronouns, automatically routing to the right agent.
-
-### Multi-Intent Queries
-```
-User: What's the weather in Seattle, and do any flights go direct to New York?
-Assistant: [Both weather_agent and flight_agent respond simultaneously]
-  - Weather Agent: [Weather information for Seattle]
-  - Flight Agent: [Flight information from Seattle to New York]
-```
-
-The orchestrator can select multiple agents simultaneously for queries containing multiple intents.
-
-## Agent Details (LangChain)
-
-### Weather Agent
- **Port**: 10510
- **API**: Open-Meteo (free, no API key)
- **LangChain**: Tool to fetch weather; LLM summarizes with provided data
- **Capabilities**: Current weather, multi-day forecasts, temperature, conditions, sunrise/sunset
-
-### Flight Agent
- **Port**: 10520
- **API**: FlightAware AeroAPI
- **LangChain**: Tool resolves cities → IATA and fetches flights
- **Capabilities**: Real-time flight status, schedules, delays, gates, terminals, live tracking
+Both agents use LangChain's `create_tool_calling_agent` and `AgentExecutor` for:
+- Automatic tool selection and execution
+- Streaming responses via `astream_events`
+- OpenAI-compatible API endpoints

 ## Architecture

 ```
    User Request
         ↓
-    Plano (8001)
+    Plano Gateway (8001)
     [Orchestrator]
         |
    ┌────┴────┐
@ -143,70 +27,226 @@ The orchestrator can select multiple agents simultaneously for queries containin
 Weather    Flight
 Agent      Agent
 (10510)    (10520)
-[Docker]   [Docker]
+   │          │
+   └──────────┴─── LangChain Tools ───→ External APIs
 ```

-
-
 Each agent:
-1. Extracts intent using GPT-4o-mini (with OpenTelemetry tracing)
-2. Fetches real-time data from APIs
-3. Generates response using GPT-4o
-4. Streams response back to user
+1. Receives OpenAI-compatible chat requests
+2. Uses LangChain's agent executor with tools
+3. Tools fetch data from external APIs (Open-Meteo, FlightAware)
+4. Streams responses back in OpenAI format

-Both agents run as Docker containers and communicate with Plano via `host.docker.internal`.
+## LangChain Implementation Details
+
+### Weather Agent Tools
+
+```python
+@tool
+async def get_weather(city: str, days: int = 1) -> str:
+    """Get weather information for a city."""
+    # Geocode city → fetch weather from Open-Meteo
+    ...
+```
+
+### Flight Agent Tools
+
+```python
+@tool
+async def resolve_airport_code(city: str) -> str:
+    """Convert city name to IATA airport code."""
+    ...
+
+@tool
+async def search_flights(origin_code: str, destination_code: str, travel_date: str = None) -> str:
+    """Search flights between two airports."""
+    # Query FlightAware AeroAPI
+    ...
+```
+
+### Agent Setup
+
+```python
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain_openai import ChatOpenAI
+
+llm = ChatOpenAI(
+    model="openai/gpt-4o",
+    base_url=LLM_GATEWAY_ENDPOINT,  # Plano gateway
+    api_key="EMPTY",
+    streaming=True,
+)
+
+agent = create_tool_calling_agent(llm, tools, prompt)
+executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+```
+
+## Prerequisites
+
+- Docker and Docker Compose
+- [Plano CLI](https://docs.planoai.dev) installed
+- OpenAI API key
+- (Optional) FlightAware AeroAPI key for live flight data
+
+## Quick Start
+
+### 1. Set Environment Variables
+
+```bash
+export OPENAI_API_KEY="your-openai-api-key"
+export AEROAPI_KEY="your-flightaware-api-key"  # Optional for flight agent
+```
+
+### 2. Start All Services with Docker Compose
+
+```bash
+docker compose up --build
+```
+
+This starts:
+- Plano Gateway on port 8001 (and 12000 for LLM proxy)
+- Weather Agent on port 10510
+- Flight Agent on port 10520
+- Open WebUI on port 8080
+- Jaeger tracing on port 16686
+
+### 3. Test the System
+
+**Option 1**: Use Open WebUI at http://localhost:8080
+
+**Option 2**: Send requests directly:
+
+```bash
+# Weather query
+curl http://localhost:8001/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gpt-4o",
+    "messages": [{"role": "user", "content": "What is the weather like in Paris?"}]
+  }'
+
+# Flight query
+curl http://localhost:8001/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gpt-4o",
+    "messages": [{"role": "user", "content": "Find flights from Seattle to New York"}]
+  }'
+```
+
+## Example Conversations
+
+### Weather Query
+```
+User: What's the 5-day forecast for Tokyo?
+Assistant: [Weather Agent uses get_weather tool → presents forecast]
+```
+
+### Flight Search
+```
+User: What flights go from London to Seattle tomorrow?
+Assistant: [Flight Agent uses resolve_airport_code → search_flights → presents results]
+```
+
+### Multi-Agent (via Plano routing)
+```
+User: What's the weather in Seattle, and any flights to New York?
+Assistant: [Plano routes to both agents → combined response]
+```
+
+## Local Development
+
+### Run agents locally (without Docker)
+
+```bash
+# Install dependencies
+cd demos/use_cases/langchain
+uv sync
+
+# Start weather agent
+uv run python src/travel_agents/weather_agent.py
+
+# In another terminal, start flight agent
+uv run python src/travel_agents/flight_agent.py
+```
+
+### Using the CLI
+
+```bash
+# Start weather agent
+uv run travel_agents weather --port 10510
+
+# Start flight agent
+uv run travel_agents flight --port 10520
+```

 ## Project Structure

 ```
-travel_agents/
-├── config.yaml          # Plano configuration
-├── docker-compose.yaml       # Docker services orchestration
-├── Dockerfile               # Multi-agent container image
-├── start_agents.sh          # Quick start script
-├── pyproject.toml           # Python dependencies
+langchain/
+├── config.yaml              # Plano gateway configuration
+├── docker-compose.yaml      # Docker services orchestration
+├── Dockerfile               # Container image
+├── pyproject.toml           # Python dependencies (LangChain, FastAPI, etc.)
+├── README.md                # This file
 └── src/
    └── travel_agents/
-        ├── __init__.py      # CLI entry point
-        ├── weather_agent.py # Weather forecast agent (multi-day support)
-        └── flight_agent.py  # Flight information agent
+        ├── __init__.py      # CLI entry points
+        ├── weather_agent.py # Weather agent with get_weather tool
+        └── flight_agent.py  # Flight agent with search_flights tool
 ```

-## Configuration Files
+## Configuration

 ### config.yaml

-Defines the two agents, their descriptions, and routing configuration. The agent router uses these descriptions to intelligently route requests.
+Defines agent descriptions for Plano's intelligent routing:

-### docker-compose.yaml
+```yaml
+agents:
+  - id: weather_agent
+    url: http://host.docker.internal:10510
+  - id: flight_agent
+    url: http://host.docker.internal:10520

-Orchestrates the deployment of:
- Weather Agent (builds from Dockerfile)
- Flight Agent (builds from Dockerfile)
- Open WebUI (for testing)
- Jaeger (for distributed tracing)
+listeners:
+  - type: agent
+    name: travel_booking_service
+    port: 8001
+    router: plano_orchestrator_v1
+    agents:
+      - id: weather_agent
+        description: |
+          WeatherAgent provides real-time weather and forecasts...
+      - id: flight_agent
+        description: |
+          FlightAgent provides live flight information...
+```

 ## Troubleshooting

-**Docker containers won't start**
- Verify Docker and Docker Compose are installed
- Check that ports 10510, 10520, 8080 are available
- Review container logs: `docker compose logs weather-agent` or `docker compose logs flight-agent`
+**Agents not responding**
+- Check container logs: `docker compose logs weather-agent`
+- Verify Plano is running: `curl http://localhost:8001/health`

-**Plano won't start**
- Verify Plano is installed: `plano --version`
- Ensure you're in the travel_agents directory
- Check config.yaml is valid
+**LangChain agent errors**
+- Check that `LLM_GATEWAY_ENDPOINT` is correctly set
+- Verify OpenAI API key is valid

-**No response from agents**
- Verify all containers are running: `docker compose ps`
- Check that Plano is running on port 8001
- Review agent logs: `docker compose logs -f`
- Verify `host.docker.internal` resolves correctly (should point to host machine)
+**Flight API returning mock data**
+- Set `AEROAPI_KEY` for live FlightAware data
+- Without the key, the agent returns sample flight data

 ## API Endpoints

-All agents expose OpenAI-compatible chat completion endpoints:
+All agents expose OpenAI-compatible endpoints:

- `POST /v1/chat/completions` - Chat completion endpoint
- `GET /health` - Health check endpoint
+- `POST /v1/chat/completions` - Chat completion (streaming)
+- `GET /health` - Health check
+
+## Key Dependencies
+
+- `langchain>=0.3.13` - Agent framework
+- `langchain-openai>=0.2.14` - OpenAI integration via Plano
+- `fastapi>=0.115.0` - Web framework
+- `httpx>=0.24.0` - Async HTTP client for API calls
--- a/demos/use_cases/langchain/config.yaml
+++ b/demos/use_cases/langchain/config.yaml
@ -8,7 +8,7 @@ agents:

 model_providers:
  - model: openai/gpt-4o
-    access_key: $OPENAI_API_KEY
+    access_key: $OPENAI_API_KEY # smaller, faster, cheaper model for extracting entities like location
    default: true
  - model: openai/gpt-4o-mini
    access_key: $OPENAI_API_KEY # smaller, faster, cheaper model for extracting entities like location
--- a/demos/use_cases/langchain/docker-compose.yaml
+++ b/demos/use_cases/langchain/docker-compose.yaml
@ -36,7 +36,7 @@ services:
      - "10520:10520"
    environment:
      - LLM_GATEWAY_ENDPOINT=http://host.docker.internal:12000/v1
-      - AEROAPI_KEY=${AEROAPI_KEY:? AEROAPI_KEY environment variable is required but not set}
+      - AEROAPI_KEY=${AEROAPI_KEY:-}  # Optional: mock data returned if not set
    command: ["uv", "run", "python", "src/travel_agents/flight_agent.py"]
    extra_hosts:
      - "host.docker.internal:host-gateway"
--- a/demos/use_cases/langchain/gateway_test.rest
+++ b/demos/use_cases/langchain/gateway_test.rest
@ -0,0 +1,35 @@
+### Weather via Plano (8001)
+POST http://localhost:8001/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "openai/gpt-4o-mini",
+  "stream": true,
+  "messages": [
+    { "role": "user", "content": "What's the weather in Lahore?" }
+  ]
+}
+
+### Flight via Plano (8001)
+POST http://localhost:8001/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "openai/gpt-4o-mini",
+  "stream": false,
+  "messages": [
+    { "role": "user", "content": "Find flights from Seattle to Atlanta." }
+  ]
+}
+
+### Multi-intent via Plano (8001)
+POST http://localhost:8001/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "openai/gpt-4o-mini",
+  "stream": false,
+  "messages": [
+    { "role": "user", "content": "What's the weather in Tokyo this weekend, and any direct flights to Sydney?" }
+  ]
+}
--- a/demos/use_cases/langchain/pyproject.toml
+++ b/demos/use_cases/langchain/pyproject.toml
@ -1,19 +1,26 @@
 [project]
 name = "travel-agents"
 version = "0.1.0"
-description = "Travel Booking Agents - Weather, Flight, and Currency"
+description = "Travel Booking Agents (LangChain-first) - Weather and Flight agents powered by LangChain"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-    "click>=8.2.1",
-    "pydantic>=2.11.7",
+    # Web framework
    "fastapi>=0.115.0",
    "uvicorn>=0.30.0",
-    "openai>=1.0.0",
+    # HTTP client
    "httpx>=0.24.0",
+    # CLI
+    "click>=8.2.1",
+    "pydantic>=2.11.7",
+    # LangChain ecosystem (v1.0+)
+    "langchain>=1.0.0",
+    "langchain-openai>=0.3.0",
+    "langchain-core>=1.0.0",
+    # OpenAI (for direct API calls if needed)
+    "openai>=1.0.0",
+    # Telemetry
    "opentelemetry-api>=1.20.0",
-    "langchain>=0.3.13",
-    "langchain-openai>=0.2.14",
 ]

 [project.scripts]
--- a/demos/use_cases/langchain/src/travel_agents/init.py
+++ b/demos/use_cases/langchain/src/travel_agents/init.py
@ -0,0 +1,60 @@
+"""
+Travel Agents - LangChain Demo
+
+LangChain-powered travel agents that integrate with Plano gateway.
+Each agent uses LangChain's tool-calling capabilities for a clean, modular design.
+
+Agents:
+- Weather Agent (port 10510): Real-time weather and forecasts
+- Flight Agent (port 10520): Flight search and information
+
+Usage:
+    # Start weather agent
+    python -m travel_agents.weather_agent
+
+    # Start flight agent
+    python -m travel_agents.flight_agent
+
+    # Or use the CLI
+    travel_agents weather --port 10510
+    travel_agents flight --port 10520
+"""
+
+import click
+
+
+@click.group()
+def cli():
+    """Travel Agents - LangChain demo for Plano integration."""
+    pass
+
+
+@cli.command()
+@click.option("--host", default="0.0.0.0", help="Host to bind to")
+@click.option("--port", default=10510, help="Port to listen on")
+def weather(host: str, port: int):
+    """Start the Weather Agent (LangChain)."""
+    from travel_agents.weather_agent import start_server
+
+    click.echo(f"🌤️ Starting Weather Agent on {host}:{port}")
+    start_server(host=host, port=port)
+
+
+@cli.command()
+@click.option("--host", default="0.0.0.0", help="Host to bind to")
+@click.option("--port", default=10520, help="Port to listen on")
+def flight(host: str, port: int):
+    """Start the Flight Agent (LangChain)."""
+    from travel_agents.flight_agent import start_server
+
+    click.echo(f"✈️ Starting Flight Agent on {host}:{port}")
+    start_server(host=host, port=port)
+
+
+def main():
+    """Entry point for the CLI."""
+    cli()
+
+
+if __name__ == "__main__":
+    main()
--- a/demos/use_cases/langchain/src/travel_agents/flight_agent.py
+++ b/demos/use_cases/langchain/src/travel_agents/flight_agent.py
@ -1,6 +1,7 @@
 import json
+
 from fastapi import FastAPI, Request
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from openai import AsyncOpenAI
 import os
 import logging
@ -14,8 +15,7 @@ from opentelemetry.propagate import extract, inject
 from pydantic import BaseModel, Field
 from langchain_openai import ChatOpenAI
 from langchain_core.tools import tool
-from langchain.prompts import ChatPromptTemplate
-from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain.agents import create_agent

 logging.basicConfig(
    level=logging.INFO,
@ -67,61 +67,61 @@ Your task:

 Multi-agent context: If the conversation includes information from other sources, incorporate it naturally into your response."""

-ROUTE_EXTRACTION_PROMPT = """Extract flight route and travel date. Support direct AND multi-leg flights.

-Rules:
-1. Patterns: "flight from X to Y", "X to Y to Z", "fly from X through Y to Z"
-2. For multi-leg (e.g., "Seattle to Dubai to Lahore"), extract ALL cities in order
-3. Extract dates: "tomorrow", "next week", "December 25", "12/25", "on Monday"
-4. Use conversation context for missing details
+def build_flight_agent(
+    request: Request,
+    request_body: dict,
+    streaming: bool,
+):
+    ctx = extract(request.headers)
+    extra_headers = {"x-envoy-max-retries": "3"}
+    request_id = request.headers.get("x-request-id")
+    if request_id:
+        extra_headers["x-request-id"] = request_id
+    inject(extra_headers, context=ctx)

-Output format: {"cities": ["City1", "City2", ...], "date": "YYYY-MM-DD" or null}
+    @tool("search_flights", args_schema=FlightSearchInput)
+    async def search_flights(
+        origin_city: str, destination_city: str, travel_date: Optional[str] = None
+    ):
+        """Search for flights between two cities. Supports optional travel date."""
+        origin_code = await resolve_airport_code(origin_city, request)
+        dest_code = await resolve_airport_code(destination_city, request)

-Examples:
- "Flight from Seattle to Atlanta tomorrow" → {"cities": ["Seattle", "Atlanta"], "date": "2026-01-07"}
- "Seattle to Dubai to Lahore" → {"cities": ["Seattle", "Dubai", "Lahore"], "date": null}
- "Flights from LA through Chicago to NYC" → {"cities": ["LA", "Chicago", "NYC"], "date": null}
+        if not origin_code or not dest_code:
+            return {
+                "error": "Could not resolve airport codes for provided cities.",
+                "origin_city": origin_city,
+                "destination_city": destination_city,
+            }

-Today is January 6, 2026. Extract flight route:"""
+        flight_data = await fetch_flights(origin_code, dest_code, travel_date)
+        return {
+            "origin_city": origin_city,
+            "destination_city": destination_city,
+            "origin_code": origin_code,
+            "destination_code": dest_code,
+            "travel_date": travel_date or datetime.now().strftime("%Y-%m-%d"),
+            "flights": flight_data.get("flights", []),
+            "count": flight_data.get("count", 0),
+            "error": flight_data.get("error"),
+        }

+    llm = ChatOpenAI(
+        model=FLIGHT_MODEL,
+        api_key="EMPTY",
+        base_url=LLM_GATEWAY_ENDPOINT,
+        temperature=request_body.get("temperature", 0.7),
+        max_tokens=request_body.get("max_tokens", 1000),
+        streaming=streaming,
+        default_headers=extra_headers,
+    )

-async def extract_flight_route(messages: list, request: Request) -> dict:
-    try:
-        ctx = extract(request.headers)
-        extra_headers = {}
-        inject(extra_headers, context=ctx)
-
-        response = await openai_client.chat.completions.create(
-            model=EXTRACTION_MODEL,
-            messages=[
-                {"role": "system", "content": ROUTE_EXTRACTION_PROMPT},
-                *[
-                    {"role": m.get("role"), "content": m.get("content")}
-                    for m in messages[-5:]
-                ],
-            ],
-            temperature=0.1,
-            max_tokens=100,
-            extra_headers=extra_headers or None,
-        )
-
-        result = response.choices[0].message.content.strip()
-        if "```json" in result:
-            result = result.split("```json")[1].split("```")[0].strip()
-        elif "```" in result:
-            result = result.split("```")[1].split("```")[0].strip()
-
-        route = json.loads(result)
-        cities = route.get("cities", [])
-
-        if not cities and (route.get("origin") or route.get("destination")):
-            cities = [c for c in [route.get("origin"), route.get("destination")] if c]
-
-        return {"cities": cities, "date": route.get("date")}
-
-    except Exception as e:
-        logger.error(f"Error extracting flight route: {e}")
-        return {"cities": [], "date": None}
+    return create_agent(
+        model=llm,
+        tools=[search_flights],
+        system_prompt=SYSTEM_PROMPT,
+    )


 async def resolve_airport_code(city_name: str, request: Request) -> Optional[str]:
@ -247,130 +247,112 @@ async def fetch_flights(
        }


-def build_flight_context(cities: list, airport_codes: list, legs_data: list) -> str:
-    if len(cities) == 2:
-        leg = legs_data[0]
-        flight_data = {
-            "flights": leg["flights"],
-            "count": len(leg["flights"]),
-            "origin_code": leg["origin_code"],
-            "destination_code": leg["dest_code"],
-        }
-        if leg["flights"]:
-            return f"""
-Flight search results from {leg['origin']} ({leg['origin_code']}) to {leg['destination']} ({leg['dest_code']}):
-
-Flight data in JSON format:
-{json.dumps(flight_data, indent=2)}
-
-Present these {len(leg['flights'])} flight(s) to the user clearly."""
-        else:
-            error = leg.get("error") or "No direct flights found"
-            return f"""
-Flight search from {leg['origin']} ({leg['origin_code']}) to {leg['destination']} ({leg['dest_code']}):
-
-Result: {error}
-
-Let the user know and suggest alternatives if appropriate."""
-
-    route_str = " → ".join(
-        [f"{city} ({code})" for city, code in zip(cities, airport_codes)]
-    )
-    context = f"\nMulti-leg flight search: {route_str}\n\n"
-
-    for leg in legs_data:
-        context += f"**Leg {leg['leg']}: {leg['origin']} ({leg['origin_code']}) → {leg['destination']} ({leg['dest_code']})**\n"
-        if leg["flights"]:
-            leg_data = {"flights": leg["flights"], "count": len(leg["flights"])}
-            context += f"Flight data:\n{json.dumps(leg_data, indent=2)}\n\n"
-        elif leg.get("error"):
-            context += f"Error: {leg['error']}\n\n"
-        else:
-            context += "No direct flights found for this leg.\n\n"
-
-    context += "Present this itinerary clearly. For each leg, show available flights by departure time. Note connection timing between legs."
-    return context
-
-
 app = FastAPI(title="Flight Information Agent", version="1.0.0")


@app.post("/v1/chat/completions")
 async def handle_request(request: Request):
    request_body = await request.json()
+    is_streaming = request_body.get("stream", True)
+    model = request_body.get("model", FLIGHT_MODEL)
+
+    if is_streaming:
+        return StreamingResponse(
+            invoke_flight_agent_stream(request, request_body, model),
+            media_type="text/event-stream",
+            headers={"content-type": "text/event-stream"},
+        )
+
    content = await invoke_flight_agent(request, request_body)
-    return JSONResponse({"content": content})
+    return JSONResponse(
+        {
+            "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": content},
+                    "finish_reason": "stop",
+                }
+            ],
+        }
+    )


 async def invoke_flight_agent(request: Request, request_body: dict):
+    """Generate flight information using a LangChain agent with the modern create_agent API."""
    messages = request_body.get("messages", [])
-
-    conversation = "\n".join(
-        [f"{m.get('role')}: {m.get('content')}" for m in messages if m.get("content")]
-    )
-
-    ctx = extract(request.headers)
-    extra_headers = {"x-envoy-max-retries": "3"}
-    inject(extra_headers, context=ctx)
-
-    @tool("search_flights", args_schema=FlightSearchInput)
-    async def search_flights(
-        origin_city: str, destination_city: str, travel_date: Optional[str] = None
-    ):
-        """Search for flights between two cities. Supports optional travel date."""
-        origin_code = await resolve_airport_code(origin_city, request)
-        dest_code = await resolve_airport_code(destination_city, request)
-
-        if not origin_code or not dest_code:
-            return {
-                "error": "Could not resolve airport codes for provided cities.",
-                "origin_city": origin_city,
-                "destination_city": destination_city,
-            }
-
-        flight_data = await fetch_flights(origin_code, dest_code, travel_date)
-        return {
-            "origin_city": origin_city,
-            "destination_city": destination_city,
-            "origin_code": origin_code,
-            "destination_code": dest_code,
-            "travel_date": travel_date or datetime.now().strftime("%Y-%m-%d"),
-            "flights": flight_data.get("flights", []),
-            "count": flight_data.get("count", 0),
-            "error": flight_data.get("error"),
-        }
-
-    tools = [search_flights]
-
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", SYSTEM_PROMPT),
-            (
-                "user",
-                "Conversation:\n{conversation}\n\nUse tools to fetch real flight options. Be concise and clear.",
-            ),
-        ]
-    )
-
-    llm = ChatOpenAI(
-        model=FLIGHT_MODEL,
-        api_key="EMPTY",
-        base_url=LLM_GATEWAY_ENDPOINT,
-        temperature=request_body.get("temperature", 0.7),
-        max_tokens=request_body.get("max_tokens", 1000),
-        streaming=False,
-        default_headers=extra_headers,
-    )
-
-    agent = create_tool_calling_agent(llm, tools, prompt)
-    executor = AgentExecutor(agent=agent, tools=tools, verbose=False)
+    agent = build_flight_agent(request, request_body, streaming=False)

    try:
-        result = await executor.ainvoke({"conversation": conversation})
-        return result.get("output", "")
+        # Invoke agent with messages
+        result = await agent.ainvoke({"messages": messages})
+
+        # Extract final response from messages
+        final_message = result["messages"][-1]
+        content = (
+            final_message.content
+            if hasattr(final_message, "content")
+            else str(final_message)
+        )
+        return content
    except Exception as e:
        logger.error(f"Error generating response: {e}")
-        return "I’m having trouble retrieving flight information right now. Please try again."
+        return "I'm having trouble retrieving flight information right now. Please try again."
+
+
+def build_openai_chunk(model: str, content: str, finish_reason: Optional[str] = None):
+    return {
+        "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
+        "object": "chat.completion.chunk",
+        "created": int(time.time()),
+        "model": model,
+        "choices": [
+            {
+                "index": 0,
+                "delta": {"content": content} if content else {},
+                "finish_reason": finish_reason,
+            }
+        ],
+    }
+
+
+async def invoke_flight_agent_stream(
+    request: Request,
+    request_body: dict,
+    model: str,
+):
+    messages = request_body.get("messages", [])
+    agent = build_flight_agent(request, request_body, streaming=True)
+
+    try:
+        async for event in agent.astream_events(
+            {"messages": messages},
+            version="v2",
+        ):
+            if event.get("event") != "on_chat_model_stream":
+                continue
+            chunk = event.get("data", {}).get("chunk")
+            content = getattr(chunk, "content", None)
+            if not content:
+                continue
+            if isinstance(content, list):
+                content = "".join(
+                    piece for piece in content if isinstance(piece, str)
+                ).strip()
+                if not content:
+                    continue
+            yield f"data: {json.dumps(build_openai_chunk(model, content))}\n\n"
+
+        yield f"data: {json.dumps(build_openai_chunk(model, '', 'stop'))}\n\n"
+        yield "data: [DONE]\n\n"
+    except Exception as e:
+        logger.error(f"Error streaming response: {e}")
+        error_message = "I'm having trouble retrieving flight information right now. Please try again."
+        yield f"data: {json.dumps(build_openai_chunk(model, error_message, 'stop'))}\n\n"
+        yield "data: [DONE]\n\n"


@app.get("/health")
--- a/demos/use_cases/langchain/src/travel_agents/weather_agent.py
+++ b/demos/use_cases/langchain/src/travel_agents/weather_agent.py
@ -1,86 +1,56 @@
 import json
-import re
-from fastapi import FastAPI, Request
-from fastapi.responses import StreamingResponse
-from openai import AsyncOpenAI
-import os
 import logging
+import os
 import time
 import uuid
-import uvicorn
-from datetime import datetime, timedelta
-import httpx
+from datetime import datetime
 from typing import Optional
 from urllib.parse import quote
+
+import httpx
+import uvicorn
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse, StreamingResponse
+from langchain.agents import create_agent
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from openai import AsyncOpenAI
 from opentelemetry.propagate import extract, inject
 from pydantic import BaseModel, Field
-from langchain_openai import ChatOpenAI
-from langchain_core.tools import tool
-from langchain.prompts import ChatPromptTemplate
-from langchain.agents import create_tool_calling_agent, AgentExecutor

-# Set up logging
 logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - [WEATHER_AGENT] - %(levelname)s - %(message)s",
 )
 logger = logging.getLogger(__name__)

-
-# Configuration for plano LLM gateway
 LLM_GATEWAY_ENDPOINT = os.getenv(
    "LLM_GATEWAY_ENDPOINT", "http://host.docker.internal:12001/v1"
 )
 WEATHER_MODEL = "openai/gpt-4o"
 LOCATION_MODEL = "openai/gpt-4o-mini"

-# Initialize OpenAI client for plano
 openai_client_via_plano = AsyncOpenAI(
    base_url=LLM_GATEWAY_ENDPOINT,
    api_key="EMPTY",
 )

-# FastAPI app for REST server
 app = FastAPI(title="Weather Forecast Agent", version="1.0.0")

-# HTTP client for API calls
 http_client = httpx.AsyncClient(timeout=10.0)


-# Utility functions
 def celsius_to_fahrenheit(temp_c: Optional[float]) -> Optional[float]:
-    """Convert Celsius to Fahrenheit."""
    return round(temp_c * 9 / 5 + 32, 1) if temp_c is not None else None


-def get_user_messages(messages: list) -> list:
-    """Extract user messages from message list."""
-    return [msg for msg in messages if msg.get("role") == "user"]
-
-
-def get_last_user_content(messages: list) -> str:
-    """Get the content of the most recent user message."""
-    for msg in reversed(messages):
-        if msg.get("role") == "user":
-            return msg.get("content", "").lower()
-    return ""
-
-
 async def get_weather_data(
    request: Request,
    messages: list,
    days: int = 1,
-    traceparent_header: str = None,
    request_id: str = None,
+    city_override: Optional[str] = None,
 ):
-    """Extract location from user's conversation and fetch weather data from Open-Meteo API.
-
-    This function does two things:
-    1. Uses an LLM to extract the location from the user's message
-    2. Fetches weather data for that location from Open-Meteo
-
-    Currently returns only current day weather. Want to add multi-day forecasts?
-    """
    instructions = """You are a city name extractor. Look at the FINAL user message ONLY and extract the city name.

 The FINAL user message will be the LAST message with role "user" in the conversation.
@ -88,69 +58,74 @@ The FINAL user message will be the LAST message with role "user" in the conversa
 IMPORTANT: Ignore all previous messages. Focus ONLY on the FINAL user message.

 Examples of what to extract from the FINAL user message:
- "What's the weather in Seattle?" → Seattle
- "What's the weather in San Francisco?" → San Francisco
- "What about Dubai?" → Dubai
- "How's the weather in Tokyo today?" → Tokyo
- "Tell me about Lahore" → Lahore
- "What about there?" → Look at conversation for the last mentioned city
+- "What's the weather in Seattle?" -> Seattle
+- "What's the weather in San Francisco?" -> San Francisco
+- "What about Dubai?" -> Dubai
+- "How's the weather in Tokyo today?" -> Tokyo
+- "Tell me about Lahore" -> Lahore
+- "What about there?" -> Look at conversation for the last mentioned city

 Output ONLY the city name. Nothing else. One word or city name only.
 If no city can be found, output: NOT_FOUND"""

-    try:
-        user_messages = [
-            msg.get("content") for msg in messages if msg.get("role") == "user"
-        ]
+    location = city_override
+    if not location:
+        try:
+            user_messages = [
+                msg.get("content") for msg in messages if msg.get("role") == "user"
+            ]

-        if not user_messages:
-            location = "New York"
-        else:
-            ctx = extract(request.headers)
-            extra_headers = {}
-            if request_id:
-                extra_headers["x-request-id"] = request_id
-            inject(extra_headers, context=ctx)
-            # For location extraction, pass full conversation for context (e.g., "there" = previous destination)
-            response = await openai_client_via_plano.chat.completions.create(
-                model=LOCATION_MODEL,
-                messages=[
-                    {"role": "system", "content": instructions},
-                    *[
-                        {"role": msg.get("role"), "content": msg.get("content")}
-                        for msg in messages
-                    ],
-                ],
-                temperature=0.1,
-                max_tokens=10,
-                extra_headers=extra_headers if extra_headers else None,
-            )
-
-            location = response.choices[0].message.content.strip().strip("\"'`.,!?")
-            logger.info(f"Location extraction result: '{location}'")
-
-            if not location or location.upper() == "NOT_FOUND":
+            if not user_messages:
                location = "New York"
-                logger.info(f"Location not found, defaulting to: {location}")
+            else:
+                ctx = extract(request.headers)
+                extra_headers = {}
+                if request_id:
+                    extra_headers["x-request-id"] = request_id
+                inject(extra_headers, context=ctx)
+                response = await openai_client_via_plano.chat.completions.create(
+                    model=LOCATION_MODEL,
+                    messages=[
+                        {"role": "system", "content": instructions},
+                        *[
+                            {"role": msg.get("role"), "content": msg.get("content")}
+                            for msg in messages
+                        ],
+                    ],
+                    temperature=0.1,
+                    max_tokens=10,
+                    extra_headers=extra_headers if extra_headers else None,
+                )

-    except Exception as e:
-        logger.error(f"Error extracting location: {e}")
-        location = "New York"
+                location = response.choices[0].message.content.strip().strip("\"'`.,!?")
+                logger.info("Location extraction result: '%s'", location)

-    logger.info(f"Fetching weather for location: '{location}' (days: {days})")
+                if not location or location.upper() == "NOT_FOUND":
+                    location = "New York"
+                    logger.info("Location not found, defaulting to: %s", location)
+
+        except Exception as e:
+            logger.error("Error extracting location: %s", e)
+            location = "New York"
+
+    logger.info("Fetching weather for location: '%s' (days: %s)", location, days)

-    # Step 2: Fetch weather data for the extracted location
    try:
-        # Geocode city to get coordinates
-        geocode_url = f"https://geocoding-api.open-meteo.com/v1/search?name={quote(location)}&count=1&language=en&format=json"
+        geocode_url = (
+            "https://geocoding-api.open-meteo.com/v1/search?"
+            f"name={quote(location)}&count=1&language=en&format=json"
+        )
        geocode_response = await http_client.get(geocode_url)

        if geocode_response.status_code != 200 or not geocode_response.json().get(
            "results"
        ):
-            logger.warning(f"Could not geocode {location}, using New York")
+            logger.warning("Could not geocode %s, using New York", location)
            location = "New York"
-            geocode_url = f"https://geocoding-api.open-meteo.com/v1/search?name={quote(location)}&count=1&language=en&format=json"
+            geocode_url = (
+                "https://geocoding-api.open-meteo.com/v1/search?"
+                f"name={quote(location)}&count=1&language=en&format=json"
+            )
            geocode_response = await http_client.get(geocode_url)

        geocode_data = geocode_response.json()
@ -173,15 +148,14 @@ If no city can be found, output: NOT_FOUND"""
        longitude = result["longitude"]

        logger.info(
-            f"Geocoded '{location}' to {location_name} ({latitude}, {longitude})"
+            "Geocoded '%s' to %s (%s, %s)", location, location_name, latitude, longitude
        )

-        # Get weather forecast
        weather_url = (
-            f"https://api.open-meteo.com/v1/forecast?"
+            "https://api.open-meteo.com/v1/forecast?"
            f"latitude={latitude}&longitude={longitude}&"
-            f"current=temperature_2m&"
-            f"daily=sunrise,sunset,temperature_2m_max,temperature_2m_min,weather_code&"
+            "current=temperature_2m&"
+            "daily=sunrise,sunset,temperature_2m_max,temperature_2m_min,weather_code&"
            f"forecast_days={days}&timezone=auto"
        )

@ -203,7 +177,6 @@ If no city can be found, output: NOT_FOUND"""
        current_temp = weather_data.get("current", {}).get("temperature_2m")
        daily = weather_data.get("daily", {})

-        # Build forecast for requested number of days
        forecast = []
        for i in range(days):
            date_str = daily["time"][i]
@ -225,7 +198,6 @@ If no city can be found, output: NOT_FOUND"""
            sunrise = daily.get("sunrise", [])[i] if daily.get("sunrise") else None
            sunset = daily.get("sunset", [])[i] if daily.get("sunset") else None

-            # Use current temp for today, otherwise use max temp
            temp_c = (
                temp_max
                if temp_max is not None
@ -253,7 +225,7 @@ If no city can be found, output: NOT_FOUND"""
        return {"location": location_name, "forecast": forecast}

    except Exception as e:
-        logger.error(f"Error getting weather data: {e}")
+        logger.error("Error getting weather data: %s", e)
        return {
            "location": location,
            "weather": {
@ -277,122 +249,199 @@ class WeatherToolInput(BaseModel):
    )


-@app.post("/v1/chat/completions")
-async def handle_request(request: Request):
-    """HTTP endpoint for chat completions with LangChain agent execution."""
+WEATHER_SYSTEM_PROMPT = """You are a weather assistant in a multi-agent system. You will receive weather data in JSON format with these fields:

-    request_body = await request.json()
-    messages = request_body.get("messages", [])
-    is_streaming = request_body.get("stream", True)
+- "location": City name
+- "forecast": Array of weather objects, each with date, day_name, temperature_c, temperature_f, temperature_max_c, temperature_min_c, weather_code, sunrise, sunset
+- weather_code: WMO code (0=clear, 1-3=partly cloudy, 45-48=fog, 51-67=rain, 71-86=snow, 95-99=thunderstorm)

-    logger.info("messages detail json dumps: %s", json.dumps(messages, indent=2))
+Your task:
+1. Present the weather/forecast clearly for the location
+2. For single day: show current conditions
+3. For multi-day: show each day with date and conditions
+4. Include temperature in both Celsius and Fahrenheit
+5. Describe conditions naturally based on weather_code
+6. Use conversational language

-    traceparent_header = request.headers.get("traceparent")
-    request_id = request.headers.get("x-request-id")
+Multi-agent context: You are part of a larger system. If the conversation includes additional context or information from other sources, acknowledge and incorporate it naturally into your response. Your primary focus is weather, but be aware of the full conversation context.

-    return StreamingResponse(
-        invoke_weather_agent(request, request_body, traceparent_header, request_id),
-        media_type="text/plain",
-        headers={
-            "content-type": "text/event-stream",
-        },
-    )
+Remember: Only use the provided data. If fields are null, mention data is unavailable."""


-async def invoke_weather_agent(
+def build_openai_chunk(model: str, content: str, finish_reason: Optional[str] = None):
+    return {
+        "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
+        "object": "chat.completion.chunk",
+        "created": int(time.time()),
+        "model": model,
+        "choices": [
+            {
+                "index": 0,
+                "delta": {"content": content} if content else {},
+                "finish_reason": finish_reason,
+            }
+        ],
+    }
+
+
+def build_weather_agent(
    request: Request,
    request_body: dict,
-    traceparent_header: str = None,
-    request_id: str = None,
+    streaming: bool,
 ):
-    """Generate chat completions using a LangChain tool-calling agent."""
    messages = request_body.get("messages", [])
-
-    # Build conversation string for agent context
-    conversation = "\n".join(
-        [f"{m.get('role')}: {m.get('content')}" for m in messages if m.get("content")]
-    )
-
-    # Trace propagation for downstream requests
    ctx = extract(request.headers)
    extra_headers = {"x-envoy-max-retries": "3"}
+    request_id = request.headers.get("x-request-id")
    if request_id:
        extra_headers["x-request-id"] = request_id
    inject(extra_headers, context=ctx)

-    # Detect if user wants multi-day forecast from the last user message
-    last_user_msg = get_last_user_content(messages)
-    days = 1
-    if "forecast" in last_user_msg or "week" in last_user_msg:
-        days = 7
-    elif "tomorrow" in last_user_msg:
-        days = 2
-    day_match = re.search(r"(\d{1,2})\s+day", last_user_msg)
-    if day_match:
-        requested_days = int(day_match.group(1))
-        days = min(requested_days, 16)
-
    @tool("get_weather_forecast", args_schema=WeatherToolInput)
    async def get_weather_forecast(city: str, days: int = 1):
-        """Lookup weather for a city and return structured forecast."""
+        """Fetch a structured weather forecast for a city."""
        return await get_weather_data(
            request,
-            messages + [{"role": "user", "content": city}],
+            messages,
            days,
-            traceparent_header,
-            request_id,
+            request_id=request_id,
+            city_override=city,
        )

-    tools = [get_weather_forecast]
-
-    system_prompt = """You are a weather assistant in a multi-agent system.
-Use tools to fetch live weather and provide concise, friendly answers.
-If no city is provided, pick a sensible default (e.g., New York) and say you defaulted."""
-
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", system_prompt),
-            (
-                "user",
-                "Conversation:\n{conversation}\n\nIf needed, call tools to get the latest weather.",
-            ),
-        ]
-    )
-
    llm = ChatOpenAI(
        model=WEATHER_MODEL,
        api_key="EMPTY",
        base_url=LLM_GATEWAY_ENDPOINT,
        temperature=request_body.get("temperature", 0.7),
        max_tokens=request_body.get("max_tokens", 1000),
-        streaming=False,
+        streaming=streaming,
        default_headers=extra_headers,
    )

-    agent = create_tool_calling_agent(llm, tools, prompt)
-    executor = AgentExecutor(agent=agent, tools=tools, verbose=False)
+    return create_agent(
+        model=llm,
+        tools=[get_weather_forecast],
+        system_prompt=WEATHER_SYSTEM_PROMPT,
+    )
+
+
+@app.post("/v1/chat/completions")
+async def handle_request(request: Request):
+    request_body = await request.json()
+    is_streaming = request_body.get("stream", True)

    try:
-        result = await executor.ainvoke({"conversation": conversation, "days": days})
-        content = result.get("output", "")
-        # Stream back plain SSE data like a LangChain agent out-of-the-box
-        yield f"data: {content}\n\n"
+        model = request_body.get("model", WEATHER_MODEL)
+
+        if is_streaming:
+            return StreamingResponse(
+                invoke_weather_agent_stream(request, request_body, model),
+                media_type="text/event-stream",
+                headers={"content-type": "text/event-stream"},
+            )
+
+        content = await invoke_weather_agent(request, request_body)
+        return JSONResponse(
+            {
+                "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
+                "object": "chat.completion",
+                "created": int(time.time()),
+                "model": model,
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {"role": "assistant", "content": content},
+                        "finish_reason": "stop",
+                    }
+                ],
+            }
+        )
+    except Exception as e:
+        logger.error("Error generating weather response: %s", e)
+        if is_streaming:
+            return StreamingResponse(
+                invoke_weather_agent_error_stream(
+                    request_body,
+                    "I'm having trouble retrieving weather information right now. Please try again.",
+                ),
+                media_type="text/event-stream",
+                headers={"content-type": "text/event-stream"},
+            )
+        return JSONResponse(
+            {
+                "error": {
+                    "message": "I'm having trouble retrieving weather information right now. Please try again.",
+                    "type": "server_error",
+                }
+            },
+            status_code=500,
+        )
+
+
+async def invoke_weather_agent(
+    request: Request,
+    request_body: dict,
+):
+    messages = request_body.get("messages", [])
+    agent = build_weather_agent(request, request_body, streaming=False)
+
+    result = await agent.ainvoke({"messages": messages})
+    final_message = result["messages"][-1]
+    return (
+        final_message.content
+        if hasattr(final_message, "content")
+        else str(final_message)
+    )
+
+
+async def invoke_weather_agent_stream(
+    request: Request,
+    request_body: dict,
+    model: str,
+):
+    messages = request_body.get("messages", [])
+    agent = build_weather_agent(request, request_body, streaming=True)
+
+    try:
+        async for event in agent.astream_events(
+            {"messages": messages},
+            version="v2",
+        ):
+            if event.get("event") != "on_chat_model_stream":
+                continue
+            chunk = event.get("data", {}).get("chunk")
+            content = getattr(chunk, "content", None)
+            if not content:
+                continue
+            if isinstance(content, list):
+                content = "".join(
+                    piece for piece in content if isinstance(piece, str)
+                ).strip()
+                if not content:
+                    continue
+            yield f"data: {json.dumps(build_openai_chunk(model, content))}\n\n"
+
+        yield f"data: {json.dumps(build_openai_chunk(model, '', 'stop'))}\n\n"
+        yield "data: [DONE]\n\n"
+    except Exception as e:
+        logger.error("Error streaming weather response: %s", e)
+        error_message = "I'm having trouble retrieving weather information right now. Please try again."
+        yield f"data: {json.dumps(build_openai_chunk(model, error_message, 'stop'))}\n\n"
        yield "data: [DONE]\n\n"

-    except Exception as e:
-        logger.error(f"Error generating weather response: {e}")
-        yield "data: I’m having trouble retrieving weather information right now. Please try again.\n\n"
-        yield "data: [DONE]\n\n"
+
+async def invoke_weather_agent_error_stream(request_body: dict, error_message: str):
+    model = request_body.get("model", WEATHER_MODEL)
+    yield f"data: {json.dumps(build_openai_chunk(model, error_message, 'stop'))}\n\n"
+    yield "data: [DONE]\n\n"


@app.get("/health")
 async def health_check():
-    """Health check endpoint."""
    return {"status": "healthy", "agent": "weather_forecast"}


 def start_server(host: str = "localhost", port: int = 10510):
-    """Start the REST server."""
    uvicorn.run(
        app,
        host=host,
@ -403,19 +452,16 @@ def start_server(host: str = "localhost", port: int = 10510):
            "formatters": {
                "default": {
                    "format": "%(asctime)s - [WEATHER_AGENT] - %(levelname)s - %(message)s",
-                },
+                }
            },
            "handlers": {
                "default": {
                    "formatter": "default",
                    "class": "logging.StreamHandler",
                    "stream": "ext://sys.stdout",
-                },
-            },
-            "root": {
-                "level": "INFO",
-                "handlers": ["default"],
+                }
            },
+            "root": {"level": "INFO", "handlers": ["default"]},
        },
    )

--- a/demos/use_cases/langchain/uv.lock
+++ b/demos/use_cases/langchain/uv.lock
@ -34,6 +34,15 @@ wheels = [
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 ]

+[[package]]
+name = "async-timeout"
+version = "4.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/87/d6/21b30a550dafea84b1b8eee21b5e23fa16d010ae006011221f33dcd8d7f8/async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f", size = 8345, upload-time = "2023-08-10T16:35:56.907Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721, upload-time = "2023-08-10T16:35:55.203Z" },
+]
+
 [[package]]
 name = "certifi"
 version = "2025.11.12"
@ -189,6 +198,61 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/34/2f/ff2fcc98f500713368d8b650e1bbc4a0b3ebcdd3e050dcdaad5f5a13fd7e/fastapi-0.125.0-py3-none-any.whl", hash = "sha256:2570ec4f3aecf5cca8f0428aed2398b774fcdfee6c2116f86e80513f2f86a7a1", size = 112888, upload-time = "2025-12-17T21:41:41.286Z" },
 ]

+[[package]]
+name = "greenlet"
+version = "3.3.0"
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