Version 1.4.0

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.

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:

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:

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)

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)

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:

# 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.

Dependencies

langchain>=1.0.0
langchain-core>=1.0.0

Changelog

Versions

Release 1.4.0

Major Changes

LangChain 1.0.0 Migration

Provider Adapters

Enhancements

Agent Improvements

Quality & Reliability

Breaking Changes

Staying on LangChain 0.3.x

Dependencies

See Also

Release 1.4.0

​Major Changes

​LangChain 1.0.0 Migration

​Provider Adapters

​Enhancements

​Agent Improvements

​Quality & Reliability

​Breaking Changes

​Staying on LangChain 0.3.x

​Dependencies

​See Also

Major Changes

LangChain 1.0.0 Migration

Provider Adapters

Enhancements

Agent Improvements

Quality & Reliability

Breaking Changes

Staying on LangChain 0.3.x

Dependencies

See Also