> ## Documentation Index
> Fetch the complete documentation index at: https://mcp-use.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
## Submitting Feedback
If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:
POST https://mcp-use.com/docs/feedback
```json
{
"path": "/python/changelog/1_4_0",
"feedback": "Description of the issue"
}
```
Only submit feedback when you have something specific and actionable to report.
# Version 1.4.0
> Major upgrade to LangChain 1.0.0 with direct provider integrations
We've migrated to **LangChain 1.0.0**, unlocking better performance and simplified agent execution patterns, as well as direct provider integrations for OpenAI, Anthropic, and Google.
## Major Changes
### LangChain 1.0.0 Migration
We've successfully migrated to **LangChain 1.0.0**, unlocking better performance and simplified agent execution patterns.
**Key improvements:**
* Upgraded from LangChain 0.3.27 → 1.0.0
* Simplified agent instantiation using `create_agent()`
* Reduced MCPAgent codebase by \~440 lines while adding functionality
* Enhanced internal agent loop handling
**Usage example:**
```python theme={null}
from mcp_use import MCPAgent
agent = MCPAgent(config={"mcpServers": {...}})
# Run method now uses LangChain 1.0.0 internally
result = await agent.run("Your query")
# Stream method leverages create_agent() and astream()
async for chunk in agent.stream("Your query"):
print(chunk)
```
### Provider Adapters
Direct integration adapters for major AI providers - **no MCPAgent required!**
Use MCP tools directly with your preferred provider's SDK:
```python theme={null}
from openai import OpenAI
from mcp_use import MCPClient
from mcp_use.agents.adapters import OpenAIMCPAdapter
client = MCPClient(config={"mcpServers": {...}})
# Create adapter for OpenAI format
adapter = OpenAIMCPAdapter()
await adapter.create_all(client)
# Use with OpenAI SDK
openai = OpenAI()
response = openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Find hotels in Trapani"}],
tools=adapter.tools + adapter.resources + adapter.prompts
)
# Execute tool calls
for tool_call in response.choices[0].message.tool_calls:
executor = adapter.tool_executors[tool_call.function.name]
result = await executor(**json.loads(tool_call.function.arguments))
content = adapter.parse_result(result)
```
```python theme={null}
from anthropic import Anthropic
from mcp_use import MCPClient
from mcp_use.agents.adapters import AnthropicMCPAdapter
client = MCPClient(config={"mcpServers": {...}})
# Create adapter for Anthropic format
adapter = AnthropicMCPAdapter()
await adapter.create_all(client)
# Use with Anthropic SDK
anthropic = Anthropic()
response = anthropic.messages.create(
model="claude-sonnet-4-5",
messages=[{"role": "user", "content": "Find hotels in Trapani"}],
tools=adapter.tools + adapter.resources + adapter.prompts,
max_tokens=1024
)
# Execute tool calls
for content in response.content:
if content.type == "tool_use":
executor = adapter.tool_executors[content.name]
result = await executor(**content.input)
parsed = adapter.parse_result(result)
```
```python theme={null}
from google import genai
from google.genai import types
from mcp_use import MCPClient
from mcp_use.agents.adapters import GoogleMCPAdapter
client = MCPClient(config={"mcpServers": {...}})
# Create adapter for Google format
adapter = GoogleMCPAdapter()
await adapter.create_all(client)
# Use with Google SDK
gemini = genai.Client()
google_tools = [types.Tool(function_declarations=adapter.tools)]
response = gemini.models.generate_content(
model="gemini-flash-lite-latest",
contents=[types.Content(role="user", parts=[types.Part.from_text("Find hotels")])],
config=types.GenerateContentConfig(tools=google_tools)
)
# Execute function calls
for function_call in response.function_calls:
executor = adapter.tool_executors[function_call.name]
result = await executor(**function_call.args)
content = adapter.parse_result(result)
```
**Available adapters:**
* `OpenAIMCPAdapter` - OpenAI & compatible APIs
* `AnthropicMCPAdapter` - Claude & Anthropic APIs
* `GoogleMCPAdapter` - Gemini & Google AI APIs
* `LangChainMCPAdapter` - LangChain integration (existing)
## Enhancements
### Agent Improvements
* **Dynamic tool updates**: Tools can now be refreshed during agent execution
* **Enhanced step tracking**: Better visibility into agent execution steps with middleware support
* **Streaming improvements**: More reliable streaming output with proper null checking
* **History filtering**: Automatic filtering of ToolMessage from conversation history
* **Remote mode delegation**: Query streaming properly delegated to remote agents
### Quality & Reliability
* Stricter streaming tests for better reliability
* Improved observability callback handling
* Better null checks for node output message extraction
* Fixed max\_steps assignment issues
## Breaking Changes
None - all existing methods have been updated internally to use LangChain 1.0.0 while maintaining the same API.
### Staying on LangChain 0.3.x
If you need to remain on LangChain 0.3.x, pin `mcp-use` to version 1.3.13:
```bash theme={null}
# Using pip
pip install mcp-use==1.3.13
# Using uv
uv pip install mcp-use==1.3.13
```
Or in your `requirements.txt` or `pyproject.toml`:
```toml theme={null}
# requirements.txt
mcp-use==1.3.13
# pyproject.toml
[project]
dependencies = [
"mcp-use==1.3.13",
]
```
Version 1.3.13 is the last release compatible with LangChain 0.3.27.
## Dependencies
```toml theme={null}
langchain>=1.0.0
langchain-core>=1.0.0
```
## See Also
* [OpenAI Integration Guide](/python/integration/openai)
* [Anthropic Integration Guide](/python/integration/anthropic)
* [Google Integration Guide](/python/integration/google)
* [Agent Configuration](/python/agent/agent-configuration)