diff --git a/test-langchain2/main_tool.py b/test-langchain2/main_tool.py
index 3591150..e15939c 100644
--- a/test-langchain2/main_tool.py
+++ b/test-langchain2/main_tool.py
@@ -3,7 +3,7 @@
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage, SystemMessage
 from langchain_core.tools import tool
-from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain.agents import create_agent
 from langchain_core.prompts import ChatPromptTemplate
 
 import sentry_sdk
@@ -27,35 +27,36 @@ def calculate_tip(bill_amount: float, tip_percentage: float) -> float:
 def my_pipeline(llm, tools):
     with sentry_sdk.start_transaction(name="langchain-sync-tool"):
         # Create agent with tools
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", "You are a helpful assistant that can use tools to help answer questions."),
-            ("placeholder", "{chat_history}"),
-            ("human", "{input}"),
-            ("placeholder", "{agent_scratchpad}"),
-        ])
-
-        agent = create_tool_calling_agent(llm, tools, prompt)
-        agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+        agent = create_agent(
+            model=llm,
+            tools=tools,
+            system_prompt="You are a helpful assistant that can use tools to help answer questions",
+        ).with_config(config={"run_name": "MyAgent1"})
 
         # Test with weather tool
-        result1 = agent_executor.invoke({
-            "input": "What's the weather like in San Francisco, CA?"
-        })
+        result1 = agent.invoke(
+            {
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": "What's the weather like in San Francisco, CA?",
+                    }
+                ]
+            }
+        )
         print("Weather Result:")
         print(result1)
 
         # Test with calculation tool
-        result2 = agent_executor.invoke({
-            "input": "Calculate a 18% tip on a $45.50 bill"
-        })
+        result2 = agent.invoke({"input": "Calculate a 18% tip on a $45.50 bill"})
         print("Tip Calculation Result:")
         print(result2)
 
         # Test streaming with agent
         print("Streaming Result:")
-        for chunk in agent_executor.stream({
-            "input": "What's the weather in New York and calculate a 20% tip on $30?"
-        }):
+        for chunk in agent.stream(
+            {"input": "What's the weather in New York and calculate a 20% tip on $30?"}
+        ):
             print(chunk)
 
 
diff --git a/test-langchain2/pyproject.toml b/test-langchain2/pyproject.toml
index 2e7584f..d9b8d8f 100644
--- a/test-langchain2/pyproject.toml
+++ b/test-langchain2/pyproject.toml
@@ -5,9 +5,9 @@ requires-python = ">=3.12"
 
 dependencies = [
     "ipdb>=0.13.13",
-    "langchain>=0.3.27",
-    "langchain-community>=0.3.27",
-    "langchain-openai>=0.3.28",
+    "langchain>=1.0.3",
+    # "langchain-community>=0.3.27",
+    "langchain-openai>=1.0.2",
     "pudb>=2025.1",
     "sentry-sdk",
     "tiktoken>=0.10.0",
diff --git a/test-langchain2/run_tool.sh b/test-langchain2/run_tool.sh
old mode 100644
new mode 100755
diff --git a/test-langchain2/uv.lock b/test-langchain2/uv.lock
index 764a769..e7bbf55 100644
--- a/test-langchain2/uv.lock
+++ b/test-langchain2/uv.lock
@@ -1,91 +1,18 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.13'",
     "python_full_version < '3.13'",
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diff --git a/test-openai/main_truncation.py b/test-openai/main_truncation.py
new file mode 100644
index 0000000..2951cd2
--- /dev/null
+++ b/test-openai/main_truncation.py
@@ -0,0 +1,98 @@
+import os
+
+from openai import OpenAI
+
+import sentry_sdk
+from sentry_sdk.ai.monitoring import ai_track
+from sentry_sdk.integrations.openai import OpenAIIntegration
+from sentry_sdk.integrations.stdlib import StdlibIntegration
+import dotenv
+
+dotenv.load_dotenv()
+
+
+def generate_long_message(index):
+    base_message = f"Message number {index}: "
+    filler = "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. "
+    
+    message = base_message
+    while len(message) < 1000:
+        message += filler
+    
+    return message[:1000]
+
+
+@ai_track("My truncation test workflow")
+def my_truncation_workflow(client):
+    with sentry_sdk.start_transaction(name="openai-truncation-test"):
+        messages = []
+        
+        for i in range(25):
+            if i % 2 == 0:
+                messages.append({
+                    "role": "user",
+                    "content": generate_long_message(i)
+                })
+            else:
+                messages.append({
+                    "role": "assistant",
+                    "content": generate_long_message(i)
+                })
+        
+        messages.append({
+            "role": "user",
+            "content": "Please summarize our conversation so far in one sentence."
+        })
+        
+        print(f"Total messages: {len(messages)}")
+        total_chars = sum(len(msg["content"]) for msg in messages)
+        print(f"Total characters in messages: {total_chars}")
+        print(f"Approximate size in KB: {total_chars / 1024:.2f}")
+        
+        response = client.chat.completions.create(
+            messages=messages,
+            model="gpt-4o-mini",
+            max_tokens=100,
+            temperature=0.7,
+        )
+        
+        print("--------------------------------")
+        print("Response:")
+        print(response.model_dump())
+        print("--------------------------------")
+        print("Assistant reply:")
+        print(response.choices[0].message.content)
+
+
+def main():
+    sentry_sdk.init(
+        dsn=os.getenv("SENTRY_DSN", None),
+        environment=os.getenv("ENV", "openai-test-truncation"),
+        traces_sample_rate=1.0,
+        profiles_sample_rate=1.0,
+        send_default_pii=True,
+        debug=True,
+        integrations=[
+            OpenAIIntegration(
+                include_prompts=True,
+                tiktoken_encoding_name="cl100k_base",
+            ),
+        ],
+        disabled_integrations=[
+            StdlibIntegration(),
+        ],
+    )
+
+    client = OpenAI(
+        api_key=os.environ.get("OPENAI_API_KEY"),
+    )
+
+    my_truncation_workflow(client)
+
+    print("--------------------------------")
+    print("Done!")
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/test-openai/run_truncation.sh b/test-openai/run_truncation.sh
new file mode 100644
index 0000000..b95b1b8
--- /dev/null
+++ b/test-openai/run_truncation.sh
@@ -0,0 +1,14 @@
+#!/usr/bin/env bash
+
+# Install uv if it's not installed
+if ! command -v uv &> /dev/null; then
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+fi
+
+# Run the truncation test script
+export SENTRY_SPOTLIGHT=1
+uv run python main_truncation.py
+
+
+
+
diff --git a/test-pydantic-ai/README.md b/test-pydantic-ai/README.md
index 74f7c05..65fa579 100644
--- a/test-pydantic-ai/README.md
+++ b/test-pydantic-ai/README.md
@@ -1,132 +1,44 @@
-# Pydantic AI Sentry Integration Tests
+# Pydantic AI Testing Suite
 
-Clean, focused tests for the Sentry Pydantic AI integration.
+Comprehensive testing suite for Pydantic AI integration with Sentry.
 
-## Prerequisites
+## Agent Types
 
-You need to have Python and `curl` installed.
+1. **Customer Support Agent** - Single tool with structured context data
+2. **Math Agent** - Multiple calculation tools
+3. **MCP Agent** - Connects to MCP server via stdio transport
 
-## Configure
+## Models Tested
 
-Set the following environment variables:
-- `SENTRY_DSN`
-- `OPENAI_API_KEY`
-- `ANTHROPIC_API_KEY` (for Anthropic provider tests)
-
-## Run Tests
-
-### Synchronous Tests
-```bash
-./run.sh
-```
-
-### Asynchronous Tests
-```bash
-./run_async.sh
-```
-
-## Test Structure
-
-The tests are organized into three main scenarios, each tested in both sync/async and streaming/non-streaming modes:
-
-### 1. **Simple Agent**
-- Basic agent without tools
-- Tests fundamental agent functionality
-- Demonstrates simple Q&A interactions
-
-### 2. **Agent with Tools**
-- Mathematical agent with calculation tools
-- Tools: `add()`, `multiply()`, `calculate_percentage()`
-- Tests tool integration and structured output
-- Returns `CalculationResult` with explanation
-
-### 3. **Two-Agent Workflow**
-- **Data Collector Agent**: Extracts and organizes data
-- **Data Analyzer Agent**: Analyzes data and provides insights
-- Demonstrates agent handoff and workflow patterns
-- Returns `AnalysisResult` with findings and recommendations
-
-### 4. **Anthropic Provider Tests**
-- **Anthropic Simple Agent**: Basic Claude agent without tools
-- **Anthropic Math Agent**: Claude agent with calculation tools
-- Tests both OpenAI and Anthropic providers for comparison
-- Uses `anthropic:claude-3-5-haiku-20241022` model
+- OpenAI: `gpt-4o-mini`
+- Anthropic: `claude-3-5-haiku-20241022`
 
-## Test Modes
+## Methods Tested
 
-Each scenario is tested in four different modes:
+- `run_sync()` - Synchronous execution
+- `run()` - Async execution  
+- `run_stream()` - Streaming responses
+- `iter()` - Structured streaming
+- `run_stream_events()` - Event streaming
 
-| Mode | Sync/Async | Streaming | Description |
-|------|------------|-----------|-------------|
-| 1 | Sync | No | `agent.run_sync()` |
-| 2 | Sync | Yes | `agent.run_stream_sync()` |
-| 3 | Async | No | `await agent.run()` |
-| 4 | Async | Yes | `async with agent.run_stream()` |
-
-## Additional Features
-
-### Parallel Processing (Async Only)
-- Demonstrates running multiple agents concurrently
-- Uses `asyncio.gather()` for parallel execution
-- Shows scalable multi-agent patterns
-
-### Model Settings
-All agents use optimized model settings:
-- **Simple Agent**: Balanced settings (temp: 0.3)
-- **Math Agent**: Low temperature for precision (temp: 0.1)
-- **Data Collector**: Focused extraction (temp: 0.2)
-- **Data Analyzer**: Creative analysis (temp: 0.4)
-- **Anthropic Simple Agent**: Balanced settings (temp: 0.3)
-- **Anthropic Math Agent**: Low temperature for precision (temp: 0.1)
-
-## Sentry Integration
-
-The integration automatically creates Sentry spans for:
-- `gen_ai.pipeline` - Agent workflow execution (root span)
-- `gen_ai.invoke_agent` - Individual agent invocations
-- `gen_ai.chat` - Model requests (AI client calls)
-- `gen_ai.execute_tool` - Tool executions
-
-## File Structure
-
-```
-test-pydantic-ai/
-├── agents.py           # Agent definitions and tools
-├── main.py            # Synchronous tests
-├── main_async.py      # Asynchronous tests
-├── run.sh             # Run sync tests
-├── run_async.sh       # Run async tests
-├── pyproject.toml     # Dependencies
-└── README.md          # This file
-```
-
-## Output Example
+## Running Tests
 
+```bash
+./run.sh
 ```
-🚀 Running Pydantic AI Synchronous Tests
-==================================================
 
-=== SIMPLE AGENT (Non-Streaming) ===
-Question: What is the capital of France?
-Answer: The capital of France is Paris.
-
-=== SIMPLE AGENT (Streaming) ===
-Question: Tell me a short story about a robot.
-Answer (streaming): Once upon a time, a curious robot named Zara discovered...
+Set these environment variables:
+- `SENTRY_DSN`
+- `OPENAI_API_KEY`
+- `ANTHROPIC_API_KEY`
 
-=== AGENT WITH TOOLS (Non-Streaming) ===
-Task: Multi-step calculation
-Result: CalculationResult(result=126, operation='multi-step', explanation='...')
+## Test Matrix
 
-=== TWO-AGENT WORKFLOW (Non-Streaming) ===
-Step 1: Data Collection
-Data Collector Result: Extracted sales data: [150, 200, 175, 225]
+The test suite runs all combinations:
+- 3 agent types × 2 models × 5 methods = 30 tests
 
-Step 2: Data Analysis
-Data Analyzer Result: AnalysisResult(summary='...', key_findings=[...], recommendation='...')
+Each agent is created with a unique name like `openai_customer_support_run_sync` or `anthropic_mcp_agent_run` for easy identification in Sentry spans.
 
-==================================================
-✅ All synchronous tests completed!
-```
+## MCP Server
 
-This clean structure focuses on the core functionality while thoroughly testing all aspects of the Pydantic AI integration with Sentry.
\ No newline at end of file
+The MCP agent connects to a simple MCP server (`mcp_server.py`) that runs via stdio transport and provides basic calculation and text analysis tools.
diff --git a/test-pydantic-ai/agents.py b/test-pydantic-ai/agents.py
index 6a69563..26fb37f 100644
--- a/test-pydantic-ai/agents.py
+++ b/test-pydantic-ai/agents.py
@@ -1,185 +1,164 @@
-"""
-Simple, focused agents for testing Pydantic AI integration with Sentry.
-"""
+"""Agent factory functions for Pydantic AI testing."""
 
 from pydantic_ai import Agent
+from pydantic_ai.mcp import MCPServerStdio
 from pydantic import BaseModel
+from dataclasses import dataclass
 
 
+# ============================================================================
+# Data Models
+# ============================================================================
+
 class CalculationResult(BaseModel):
     """Result from mathematical calculations."""
+    result: float
+    operation: str
+    explanation: str
+
+
+class CustomerSupportResponse(BaseModel):
+    """Response from customer support agent."""
+    response: str
+    customer_tier: str
+    action_taken: str
+
+
+class MCPCalculationResult(BaseModel):
+    """Result from MCP-based calculations."""
     result: int
     operation: str
     explanation: str
 
 
-class AnalysisResult(BaseModel):
-    """Result from data analysis."""
-    summary: str
-    key_findings: list[str]
-    recommendation: str
-
-
-# Simple agent without tools
-simple_agent = Agent(
-    "openai:gpt-4o-mini",
-    name="simple_agent",
-    instructions="You are a helpful assistant. Provide clear, concise answers.",
-    model_settings={
-        "temperature": 0.3,
-        "max_tokens": 200,
-    },
-)
-
-
-# Agent with mathematical tools
-math_agent = Agent(
-    "openai:gpt-4o-mini",
-    name="math_agent",
-    instructions="You are a mathematical assistant. Use the available tools to perform calculations and return structured results.",
-    output_type=CalculationResult,
-    model_settings={
-        "temperature": 0.1,
-        "max_tokens": 300,
-    },
-)
-
-
-@math_agent.tool_plain
-def add(a: int, b: int) -> int:
-    """Add two numbers together."""
-    return a + b
-
-
-@math_agent.tool_plain
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers together."""
-    return a * b
-
-
-@math_agent.tool_plain
-def calculate_percentage(part: float, total: float) -> float:
-    """Calculate what percentage 'part' is of 'total'."""
-    if total == 0:
-        return 0.0
-    return (part / total) * 100
-
-
-# First agent in two-agent setup - data collector
-data_collector_agent = Agent(
-    "openai:gpt-4o-mini",
-    name="data_collector",
-    instructions="You collect and prepare data for analysis. Extract key numbers and organize information clearly.",
-    model_settings={
-        "temperature": 0.2,
-        "max_tokens": 400,
-    },
-)
-
-
-@data_collector_agent.tool_plain
-def extract_numbers(text: str) -> list[int]:
-    """Extract all numbers from a text string."""
-    import re
-    numbers = re.findall(r'\d+', text)
-    return [int(n) for n in numbers]
-
-
-@data_collector_agent.tool_plain
-def organize_data(items: list[str]) -> dict:
-    """Organize a list of items into categories."""
-    return {
-        "total_items": len(items),
-        "items": items,
-        "categories": list(set(item.split()[0] if item.split() else "unknown" for item in items))
-    }
-
-
-# Second agent in two-agent setup - data analyzer
-data_analyzer_agent = Agent(
-    "openai:gpt-4o-mini",
-    name="data_analyzer",
-    instructions="You analyze data provided by the data collector and provide insights and recommendations.",
-    output_type=AnalysisResult,
-    model_settings={
-        "temperature": 0.4,
-        "max_tokens": 500,
-    },
-)
-
-
-@data_analyzer_agent.tool_plain
-def calculate_statistics(numbers: list[int]) -> dict:
-    """Calculate basic statistics for a list of numbers."""
-    if not numbers:
-        return {"error": "No numbers provided"}
+@dataclass
+class CustomerContext:
+    """Structured context data for customer support agent."""
+    customer_id: str
+    name: str
+    tier: str
+    account_balance: float
+    open_tickets: int
+    last_purchase_days_ago: int
+
+
+# ============================================================================
+# Agent Factory Functions
+# ============================================================================
+
+def create_customer_support_agent(name: str, model: str) -> Agent:
+    """Create a customer support agent with single tool and structured context."""
+    agent = Agent(
+        model,
+        name=name,
+        instructions=(
+            "You are a customer support assistant. Use the customer's tier and account information "
+            "to provide personalized support. Use the tool to check if the customer is eligible for perks."
+        ),
+        output_type=CustomerSupportResponse,
+        model_settings={
+            "temperature": 0.3,
+            "max_tokens": 300,
+        },
+    )
+    
+    @agent.tool
+    def check_perk_eligibility(ctx, perk_name: str) -> dict:
+        """Check if customer is eligible for a specific perk based on their tier."""
+        customer: CustomerContext = ctx.deps
+        
+        perk_requirements = {
+            "priority_support": ["gold", "platinum"],
+            "free_shipping": ["silver", "gold", "platinum"],
+            "discount_20": ["platinum"],
+            "discount_10": ["gold", "platinum"],
+            "early_access": ["gold", "platinum"],
+        }
+        
+        eligible = customer.tier in perk_requirements.get(perk_name, [])
+        
+        return {
+            "perk_name": perk_name,
+            "eligible": eligible,
+            "customer_tier": customer.tier,
+            "reason": f"Customer tier '{customer.tier}' {'is' if eligible else 'is not'} eligible for '{perk_name}'"
+        }
+    
+    return agent
+
+
+def create_math_agent(name: str, model: str) -> Agent:
+    """Create a math agent with multiple calculation tools."""
+    agent = Agent(
+        model,
+        name=name,
+        instructions=(
+            "You are a mathematical assistant. Use the available tools to perform calculations "
+            "and return structured results. Always explain your work step by step."
+        ),
+        output_type=CalculationResult,
+        model_settings={
+            "temperature": 0.1,
+            "max_tokens": 400,
+        },
+    )
     
-    return {
-        "count": len(numbers),
-        "sum": sum(numbers),
-        "average": sum(numbers) / len(numbers),
-        "min": min(numbers),
-        "max": max(numbers),
-    }
-
-
-@data_analyzer_agent.tool_plain
-def identify_trends(numbers: list[int]) -> str:
-    """Identify trends in a sequence of numbers."""
-    if len(numbers) < 2:
-        return "Not enough data to identify trends"
+    @agent.tool_plain
+    def add(a: float, b: float) -> float:
+        """Add two numbers together."""
+        return a + b
+    
+    @agent.tool_plain
+    def subtract(a: float, b: float) -> float:
+        """Subtract b from a."""
+        return a - b
+    
+    @agent.tool_plain
+    def multiply(a: float, b: float) -> float:
+        """Multiply two numbers together."""
+        return a * b
+    
+    @agent.tool_plain
+    def divide(a: float, b: float) -> float:
+        """Divide a by b."""
+        if b == 0:
+            raise ValueError("Cannot divide by zero")
+        return a / b
+    
+    @agent.tool_plain
+    def calculate_percentage(part: float, total: float) -> float:
+        """Calculate what percentage 'part' is of 'total'."""
+        if total == 0:
+            return 0.0
+        return (part / total) * 100
+    
+    @agent.tool_plain
+    def power(base: float, exponent: float) -> float:
+        """Calculate base raised to the power of exponent."""
+        return base ** exponent
+    
+    return agent
+
+
+def create_mcp_agent(name: str, model: str) -> Agent:
+    """Create an agent that connects to an MCP server via stdio."""
+    # Connect to the MCP server
+    mcp_server = MCPServerStdio("python", args=["mcp_server.py"])
     
-    differences = [numbers[i+1] - numbers[i] for i in range(len(numbers)-1)]
+    # Create agent with MCP server as a toolset
+    agent = Agent(
+        model,
+        name=name,
+        instructions=(
+            "You are a helpful assistant that can perform calculations and text analysis "
+            "using MCP tools. Use the available tools to answer user questions."
+        ),
+        output_type=MCPCalculationResult,
+        toolsets=[mcp_server],
+        model_settings={
+            "temperature": 0.2,
+            "max_tokens": 400,
+        },
+    )
     
-    if all(d > 0 for d in differences):
-        return "Increasing trend"
-    elif all(d < 0 for d in differences):
-        return "Decreasing trend"
-    elif all(d == 0 for d in differences):
-        return "Constant values"
-    else:
-        return "Mixed trend"
-
-
-# Anthropic agents for provider testing
-anthropic_simple_agent = Agent(
-    "anthropic:claude-3-5-haiku-20241022",
-    name="anthropic_simple_agent",
-    instructions="You are a helpful assistant using Claude. Provide clear, concise answers.",
-    model_settings={
-        "temperature": 0.3,
-        "max_tokens": 200,
-    },
-)
-
-
-anthropic_math_agent = Agent(
-    "anthropic:claude-3-5-haiku-20241022",
-    name="anthropic_math_agent",
-    instructions="You are a mathematical assistant using Claude. Use the available tools to perform calculations and return structured results.",
-    output_type=CalculationResult,
-    model_settings={
-        "temperature": 0.1,
-        "max_tokens": 300,
-    },
-)
-
-
-@anthropic_math_agent.tool_plain
-def anthropic_add(a: int, b: int) -> int:
-    """Add two numbers together (Anthropic version)."""
-    return a + b
-
-
-@anthropic_math_agent.tool_plain
-def anthropic_multiply(a: int, b: int) -> int:
-    """Multiply two numbers together (Anthropic version)."""
-    return a * b
-
-
-@anthropic_math_agent.tool_plain
-def anthropic_calculate_percentage(part: float, total: float) -> float:
-    """Calculate what percentage 'part' is of 'total' (Anthropic version)."""
-    if total == 0:
-        return 0.0
-    return (part / total) * 100
+    return agent
diff --git a/test-pydantic-ai/main.py b/test-pydantic-ai/main.py
index e1d9f11..17d877b 100644
--- a/test-pydantic-ai/main.py
+++ b/test-pydantic-ai/main.py
@@ -1,126 +1,194 @@
 """
-Synchronous Pydantic AI tests with Sentry integration.
-Tests: simple agent, agent with tools, two-agent workflow.
-Includes both streaming and non-streaming tests.
+Comprehensive Pydantic AI tests with Sentry integration.
+Tests all combinations of agents, models, and execution methods.
+
+Test matrix: 3 agent types × 2 models × 5 methods = 30 tests
 """
 
 import os
+import asyncio
 import sentry_sdk
 from sentry_sdk.integrations.pydantic_ai import PydanticAIIntegration
 from sentry_sdk.integrations.stdlib import StdlibIntegration
+from sentry_sdk.integrations.openai import OpenAIIntegration
 
 from agents import (
-    simple_agent,
-    math_agent,
-    data_collector_agent,
-    data_analyzer_agent,
-    anthropic_simple_agent,
-    anthropic_math_agent,
+    create_customer_support_agent,
+    create_math_agent,
+    create_mcp_agent,
+    CustomerContext,
 )
 
 
-def test_simple_agent():
-    """Test simple agent without tools."""
-    print("\n=== SIMPLE AGENT ===")
-    
-    result = simple_agent.run_sync("What is the capital of France? Keep it brief.")
-    print(f"Question: What is the capital of France?")
-    print(f"Answer: {result.output}")
+# Test data
+GOLD_CUSTOMER = CustomerContext(
+    customer_id="CUST001",
+    name="Alice Johnson",
+    tier="gold",
+    account_balance=1500.0,
+    open_tickets=1,
+    last_purchase_days_ago=5
+)
 
 
+# Models to test
+MODELS = [
+    "openai:gpt-4o-mini",
+    "anthropic:claude-3-5-haiku-20241022",
+]
 
 
-def test_agent_with_tools():
-    """Test agent with mathematical tools."""
-    print("\n=== AGENT WITH TOOLS ===")
+def test_run_sync(agent_factory, model: str, agent_type: str):
+    """Test synchronous execution."""
+    agent = agent_factory(f"{model.split(':')[0]}_{agent_type}_run_sync", model)
     
-    result = math_agent.run_sync(
-        "Calculate 15 + 27, then multiply the result by 3. "
-        "Also calculate what percentage 42 is of 100. "
-        "Explain your work step by step."
-    )
-    print(f"Task: Multi-step calculation")
-    print(f"Result: {result.output}")
-
-
-
-
-def test_two_agent_workflow():
-    """Test two-agent workflow."""
-    print("\n=== TWO-AGENT WORKFLOW ===")
+    if agent_type == "customer_support":
+        agent.run_sync(
+            "I'm interested in priority support and a 10% discount. Am I eligible?",
+            deps=GOLD_CUSTOMER
+        )
+    elif agent_type == "math_agent":
+        agent.run_sync(
+            "Calculate 25 + 17, then multiply the result by 3. "
+            "Also calculate what percentage 42 is of 100."
+        )
+    else:  # mcp_agent
+        agent.run_sync("Add 15 and 27, then multiply the result by 2.")
+
+
+async def test_run(agent_factory, model: str, agent_type: str):
+    """Test async execution."""
+    agent = agent_factory(f"{model.split(':')[0]}_{agent_type}_run", model)
     
-    # Step 1: Data Collector
-    print("Step 1: Data Collection")
-    raw_data = "Sales report: January 150 units, February 200 units, March 175 units, April 225 units"
+    if agent_type == "customer_support":
+        await agent.run(
+            "Can I get free shipping on my next order?",
+            deps=GOLD_CUSTOMER
+        )
+    elif agent_type == "math_agent":
+        await agent.run(
+            "Calculate (15 + 25) * 2, then divide the result by 4."
+        )
+    else:  # mcp_agent
+        async with agent:
+            await agent.run("Multiply 12 by 8, then add 5.")
+
+
+async def test_run_stream(agent_factory, model: str, agent_type: str):
+    """Test streaming execution."""
+    agent = agent_factory(f"{model.split(':')[0]}_{agent_type}_run_stream", model)
     
-    collection_result = data_collector_agent.run_sync(
-        f"Extract and organize the numerical data from this text: {raw_data}"
-    )
-    print(f"Data Collector Result: {collection_result.output}")
+    if agent_type == "customer_support":
+        async with agent.run_stream(
+            "What perks am I eligible for as a gold member?",
+            deps=GOLD_CUSTOMER
+        ) as result:
+            # Structured outputs can't use stream_text(), just wait for completion
+            pass
+    elif agent_type == "math_agent":
+        async with agent.run_stream(
+            "Calculate 100 - 25, multiply by 3, and calculate the percentage of 500."
+        ) as result:
+            # Structured outputs can't use stream_text(), just wait for completion
+            pass
+    else:  # mcp_agent
+        async with agent:
+            async with agent.run_stream("Add 20 and 30.") as result:
+                pass
+
+
+async def test_iter(agent_factory, model: str, agent_type: str):
+    """Test structured streaming with iter."""
+    agent = agent_factory(f"{model.split(':')[0]}_{agent_type}_iter", model)
     
-    # Step 2: Data Analyzer (using the collected data)
-    print("\nStep 2: Data Analysis")
-    analysis_result = data_analyzer_agent.run_sync(
-        f"Analyze this sales data and provide insights: {collection_result.output}. "
-        "The data represents monthly sales units. Identify trends and make recommendations."
-    )
-    print(f"Data Analyzer Result: {analysis_result.output}")
-
-
-def test_anthropic_simple_agent():
-    """Test Anthropic simple agent without tools."""
-    print("\n=== ANTHROPIC SIMPLE AGENT ===")
+    if agent_type == "customer_support":
+        async with agent.iter(
+            "Check my eligibility for early access and priority support perks.",
+            deps=GOLD_CUSTOMER
+        ) as result:
+            async for _ in result:
+                pass
+    elif agent_type == "math_agent":
+        async with agent.iter(
+            "Calculate 10 + 20 + 30, then multiply by 2."
+        ) as result:
+            async for _ in result:
+                pass
+    else:  # mcp_agent
+        async with agent:
+            async with agent.iter("Multiply 5 by 6.") as result:
+                async for _ in result:
+                    pass
+
+
+async def test_run_stream_events(agent_factory, model: str, agent_type: str):
+    """Test event streaming with run_stream_events."""
+    agent = agent_factory(f"{model.split(':')[0]}_{agent_type}_run_stream_events", model)
     
-    result = anthropic_simple_agent.run_sync("What is the capital of Italy? Keep it brief.")
-    print(f"Question: What is the capital of Italy?")
-    print(f"Answer: {result.output}")
-
-
-def test_anthropic_agent_with_tools():
-    """Test Anthropic agent with mathematical tools."""
-    print("\n=== ANTHROPIC AGENT WITH TOOLS ===")
+    if agent_type == "customer_support":
+        async for _ in agent.run_stream_events(
+            "What perks am I eligible for with my tier?",
+            deps=GOLD_CUSTOMER
+        ):
+            pass
+    elif agent_type == "math_agent":
+        async for _ in agent.run_stream_events(
+            "Calculate 50 + 30, then multiply by 2."
+        ):
+            pass
+    else:  # mcp_agent
+        async with agent:
+            async for _ in agent.run_stream_events("Add 7 and 8."):
+                pass
+
+
+async def run_all_async_tests():
+    """Run all async tests for all combinations."""
+    agent_factories = [
+        (create_customer_support_agent, "customer_support"),
+        (create_math_agent, "math_agent"),
+        (create_mcp_agent, "mcp_agent"),
+    ]
     
-    result = anthropic_math_agent.run_sync(
-        "Calculate 12 + 18, then multiply the result by 4. "
-        "Also calculate what percentage 25 is of 80. "
-        "Show your work step by step."
-    )
-    print(f"Task: Multi-step calculation with Claude")
-    print(f"Result: {result.output}")
+    for model in MODELS:
+        for agent_factory, agent_type in agent_factories:
+            await test_run(agent_factory, model, agent_type)
+            await test_run_stream(agent_factory, model, agent_type)
+            await test_iter(agent_factory, model, agent_type)
+            await test_run_stream_events(agent_factory, model, agent_type)
 
 
 def main():
-    """Run all synchronous tests."""
-    # Initialize Sentry
+    """Run all tests."""
     sentry_sdk.init(
         dsn=os.environ.get("SENTRY_DSN"),
-        environment=os.environ.get("ENV", "test-sync"),
+        environment=os.environ.get("ENV", "test-pydantic-ai"),
         traces_sample_rate=1.0,
         send_default_pii=True,
         integrations=[PydanticAIIntegration()],
-        disabled_integrations=[StdlibIntegration()],
+        disabled_integrations=[StdlibIntegration(), OpenAIIntegration()],
         debug=True,
     )
 
-    # Set user context
     sentry_sdk.set_user({"id": "test-user", "email": "test@example.com"})
-    sentry_sdk.set_tag("test_type", "pydantic_ai_sync")
+    sentry_sdk.set_tag("test_type", "pydantic_ai_comprehensive")
 
-    print("🚀 Running Pydantic AI Synchronous Tests")
-    print("=" * 50)
+    print("🚀 Running Pydantic AI Tests")
 
-    # Run all tests
-    test_simple_agent()
-    test_agent_with_tools()
-    test_two_agent_workflow()
-    
-    # Run Anthropic provider tests
-    test_anthropic_simple_agent()
-    test_anthropic_agent_with_tools()
+    agent_factories = [
+        (create_customer_support_agent, "customer_support"),
+        (create_math_agent, "math_agent"),
+        (create_mcp_agent, "mcp_agent"),
+    ]
+
+    # Run synchronous tests
+    for model in MODELS:
+        for agent_factory, agent_type in agent_factories:
+            test_run_sync(agent_factory, model, agent_type)
 
-    print("\n" + "=" * 50)
-    print("✅ All synchronous tests completed!")
+    # Run async tests
+    asyncio.run(run_all_async_tests())
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/test-pydantic-ai/mcp_server.py b/test-pydantic-ai/mcp_server.py
new file mode 100644
index 0000000..4197c76
--- /dev/null
+++ b/test-pydantic-ai/mcp_server.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""Simple MCP server with basic tools."""
+
+from mcp.server.fastmcp import FastMCP
+
+app = FastMCP("SimpleCalculator")
+
+
+@app.tool()
+def add_numbers(a: int, b: int) -> int:
+    """Add two numbers together."""
+    return a + b
+
+
+@app.tool()
+def multiply_numbers(a: int, b: int) -> int:
+    """Multiply two numbers together."""
+    return a * b
+
+
+@app.tool()
+def get_word_count(text: str) -> int:
+    """Count the number of words in a text string."""
+    return len(text.split())
+
+
+if __name__ == "__main__":
+    app.run(transport="stdio")
+
diff --git a/test-pydantic-ai/pyproject.toml b/test-pydantic-ai/pyproject.toml
index 5b97442..64f0492 100644
--- a/test-pydantic-ai/pyproject.toml
+++ b/test-pydantic-ai/pyproject.toml
@@ -5,7 +5,7 @@ requires-python = ">=3.12"
 
 dependencies = [
     "ipdb>=0.13.13",
-    "pydantic-ai>=0.0.14",
+    "pydantic-ai[mcp]>=0.0.14",
     "sentry-sdk",
 ]
 
diff --git a/test-pydantic-ai/run.sh b/test-pydantic-ai/run.sh
index 3ca4a02..9452673 100755
--- a/test-pydantic-ai/run.sh
+++ b/test-pydantic-ai/run.sh
@@ -8,5 +8,4 @@ if ! command -v uv &> /dev/null; then
     curl -LsSf https://astral.sh/uv/install.sh | sh
 fi
 
-echo "🔄 Running Synchronous Pydantic AI Tests..."
-uv run python main.py
\ No newline at end of file
+uv run python main.py
diff --git a/test-pydantic-ai/uv.lock b/test-pydantic-ai/uv.lock
index 21da125..5bfe13a 100644
--- a/test-pydantic-ai/uv.lock
+++ b/test-pydantic-ai/uv.lock
@@ -1823,7 +1823,7 @@ wheels = [
 
 [[package]]
 name = "sentry-sdk"
-version = "2.40.0"
+version = "2.41.0"
 source = { editable = "../../sentry-python" }
 dependencies = [
     { name = "certifi" },
@@ -1851,6 +1851,7 @@ requires-dist = [
     { name = "falcon", marker = "extra == 'falcon'", specifier = ">=1.4" },
     { name = "fastapi", marker = "extra == 'fastapi'", specifier = ">=0.79.0" },
     { name = "flask", marker = "extra == 'flask'", specifier = ">=0.11" },
+    { name = "google-genai", marker = "extra == 'google-genai'", specifier = ">=1.29.0" },
     { name = "grpcio", marker = "extra == 'grpcio'", specifier = ">=1.21.1" },
     { name = "httpcore", extras = ["http2"], marker = "extra == 'http2'", specifier = "==1.*" },
     { name = "httpx", marker = "extra == 'httpx'", specifier = ">=0.16.0" },
@@ -1869,6 +1870,7 @@ requires-dist = [
     { name = "opentelemetry-distro", marker = "extra == 'opentelemetry-experimental'" },
     { name = "protobuf", marker = "extra == 'grpcio'", specifier = ">=3.8.0" },
     { name = "pure-eval", marker = "extra == 'pure-eval'" },
+    { name = "pydantic-ai", marker = "extra == 'pydantic-ai'", specifier = ">=1.0.0" },
     { name = "pymongo", marker = "extra == 'pymongo'", specifier = ">=3.1" },
     { name = "pyspark", marker = "extra == 'pyspark'", specifier = ">=2.4.4" },
     { name = "quart", marker = "extra == 'quart'", specifier = ">=0.16.1" },
@@ -1883,7 +1885,7 @@ requires-dist = [
     { name = "unleashclient", marker = "extra == 'unleash'", specifier = ">=6.0.1" },
     { name = "urllib3", specifier = ">=1.26.11" },
 ]
-provides-extras = ["aiohttp", "anthropic", "arq", "asyncpg", "beam", "bottle", "celery", "celery-redbeat", "chalice", "clickhouse-driver", "django", "falcon", "fastapi", "flask", "grpcio", "http2", "httpx", "huey", "huggingface-hub", "langchain", "langgraph", "launchdarkly", "litellm", "litestar", "loguru", "openai", "openfeature", "opentelemetry", "opentelemetry-experimental", "pure-eval", "pymongo", "pyspark", "quart", "rq", "sanic", "sqlalchemy", "starlette", "starlite", "statsig", "tornado", "unleash"]
+provides-extras = ["aiohttp", "anthropic", "arq", "asyncpg", "beam", "bottle", "celery", "celery-redbeat", "chalice", "clickhouse-driver", "django", "falcon", "fastapi", "flask", "grpcio", "http2", "httpx", "huey", "huggingface-hub", "langchain", "langgraph", "launchdarkly", "litellm", "litestar", "loguru", "openai", "openfeature", "opentelemetry", "opentelemetry-experimental", "pure-eval", "pydantic-ai", "pymongo", "pyspark", "quart", "rq", "sanic", "sqlalchemy", "starlette", "starlite", "statsig", "tornado", "unleash", "google-genai"]
 
 [[package]]
 name = "six"
@@ -1983,7 +1985,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "ipdb", specifier = ">=0.13.13" },
-    { name = "pydantic-ai", specifier = ">=0.0.14" },
+    { name = "pydantic-ai", extras = ["mcp"], specifier = ">=0.0.14" },
     { name = "sentry-sdk", editable = "../../sentry-python" },
 ]