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release1.4.0

Release 1.4.0

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.

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Major Changes

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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: 
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)

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Provider Adapters

Direct integration adapters for major AI providers - no MCPAgent required! Use MCP tools directly with your preferred provider’s SDK:
  • OpenAI
  • Anthropic
  • Google
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)
Available adapters:
  • OpenAIMCPAdapter - OpenAI & compatible APIs
  • AnthropicMCPAdapter - Claude & Anthropic APIs
  • GoogleMCPAdapter - Gemini & Google AI APIs
  • LangChainMCPAdapter - LangChain integration (existing)

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Enhancements

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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

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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

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Breaking Changes

None - all existing methods have been updated internally to use LangChain 1.0.0 while maintaining the same API.

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Staying on LangChain 0.3.x

If you need to remain on LangChain 0.3.x, pin mcp-use to version 1.3.13: 
# 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: 
# 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.

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Dependencies

langchain>=1.0.0
langchain-core>=1.0.0

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See Also