LangChain Agent Integration

name: "Hosted LangChain Agent"
model_id: "langchain-agent-executor"
description: "Execute hosted LangChain agents for complex AI workflows and tool usage"
endpoint: "https://api.langchain.com/v1/agents/{{agent_id}}/invoke"
method: "POST"

headers:
  Authorization: "Bearer {{langchain_api_key}}"
  Content-Type: "application/json"
  X-LangChain-Version: "0.1.0"

request_schema:
  agent_id: "{{agent_id}}"
  input:
    message: "{{message}}"
    user_id: "{{user_id}}"
    session_id: "{{session_id}}"
    timestamp: "{{timestamp}}"
  config:
    callbacks: []
    tags: ["chat-platform"]
    metadata:
      user_id: "{{user_id}}"
      session_id: "{{session_id}}"
  tools:
    - name: "web_search"
      description: "Search the web for current information"
    - name: "calculator"
      description: "Perform mathematical calculations"
    - name: "code_executor"
      description: "Execute code snippets"
  memory:
    type: "conversation_buffer"
    max_token_limit: 2000

response_path: "data.output"

message_format:
  preset: "langchain_agent"
  mapping:
    role:
      source: "role"
      target: "input.role"
      transform: "lowercase"
    content:
      source: "content"
      target: "input.message"
      transform: "none"
    timestamp:
      source: "timestamp"
      target: "input.timestamp"
      transform: "iso8601"
    session_id:
      source: "session_id"
      target: "input.session_id"
      transform: "none"
  customFields:
    - name: "langchain_configuration"
      value:
        platform: "langchain"
        version: "0.1.0"
        agent_type: "conversational"
        capabilities: ["tool_usage", "memory", "reasoning"]
      type: "object"
    - name: "agent_settings"
      value:
        max_iterations: 10
        timeout: 300
        memory_type: "conversation_buffer"
        max_token_limit: 2000
      type: "object"

suggestion_prompts:
  - "Create an agent that can research topics and provide comprehensive summaries"
  - "Build an agent for automated data analysis and visualization"
  - "Set up an agent for code review and optimization suggestions"
  - "Create an agent for customer support with access to knowledge base"
  - "Build an agent for automated report generation from multiple data sources"

Thought: I need to search for recent quantum computing developments.
Action: web_search
Action Input: "quantum computing breakthroughs 2024"
Observation: [Search results...]

Thought: I now have enough information to summarize.
Final Answer: [Comprehensive summary...]

from langchain.tools import BaseTool
from pydantic import BaseModel, Field

class SearchInput(BaseModel):
    query: str = Field(description="The search query")
    num_results: int = Field(default=5, description="Number of results")

class WebSearchTool(BaseTool):
    name = "web_search"
    description = "Search the web for current information"
    args_schema = SearchInput

    def _run(self, query: str, num_results: int = 5) -> str:
        # Implement search logic
        results = search_api(query, limit=num_results)
        return format_results(results)

    async def _arun(self, query: str, num_results: int = 5) -> str:
        # Async implementation
        results = await async_search_api(query, limit=num_results)
        return format_results(results)

# Research Agent
research_agent = create_agent(
    llm=ChatOpenAI(model="gpt-4"),
    tools=[web_search_tool, summarizer_tool]
)

# Analysis Agent
analysis_agent = create_agent(
    llm=ChatOpenAI(model="gpt-4"),
    tools=[calculator_tool, data_viz_tool]
)

# Coordinator Agent
coordinator = create_agent(
    llm=ChatOpenAI(model="gpt-4"),
    tools=[research_agent, analysis_agent]
)

from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", """You are an expert data analyst with access to various tools.
    When analyzing data:
    1. First, understand the question
    2. Gather necessary data using tools
    3. Perform calculations/analysis
    4. Provide clear insights
    5. Suggest actionable recommendations

    Be concise but thorough."""),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

from langchain.agents import AgentExecutor

agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True,
    max_iterations=10,
    max_execution_time=300,
    handle_parsing_errors=True,
    return_intermediate_steps=True
)