LangChain Integration

Use Jobko tools in your LangChain agents.

Installation

pip install langchain requests

Quick Setup

from langchain.tools import StructuredTool
import requests
import os

JOBKO_API_KEY = os.environ.get("JOBKO_API_KEY")
JOBKO_URL = "https://jobko.ai/api/mcp/tools/call"

def jobko_request(name: str, arguments: dict = None):
    """Make a request to the Jobko API."""
    response = requests.post(
        JOBKO_URL,
        headers={"Authorization": f"Bearer {JOBKO_API_KEY}"},
        json={"name": name, "arguments": arguments or {}}
    )
    return response.json()

Creating Tools

Get Profile Tool

get_profile = StructuredTool.from_function(
    func=lambda: jobko_request("get_profile"),
    name="jobko_get_profile",
    description="Get the user's job profile including skills, experience, and preferences"
)

Analyze Job Tool

from pydantic import BaseModel, Field

class AnalyzeJobInput(BaseModel):
    job_url: str = Field(description="URL of the job posting to analyze")

def analyze_job(job_url: str) -> dict:
    return jobko_request("analyze_job", {"job_url": job_url})

analyze_job_tool = StructuredTool.from_function(
    func=analyze_job,
    name="jobko_analyze_job",
    description="Analyze a job posting URL against the user's profile. Returns match score, matching/missing skills, and recommendations. Costs $0.02.",
    args_schema=AnalyzeJobInput
)

Generate CV Tool

class GenerateCVInput(BaseModel):
    job_id: int = Field(description="ID of the analyzed job")
    format: str = Field(default="pdf", description="Output format: pdf, docx, or markdown")

def generate_cv(job_id: int, format: str = "pdf") -> dict:
    return jobko_request("generate_cv", {"job_id": job_id, "format": format})

generate_cv_tool = StructuredTool.from_function(
    func=generate_cv,
    name="jobko_generate_cv",
    description="Generate a tailored CV for a specific job. Costs $0.20.",
    args_schema=GenerateCVInput
)

Full Example Agent

from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder

# Create tools
tools = [
    StructuredTool.from_function(
        func=lambda: jobko_request("get_profile"),
        name="jobko_get_profile",
        description="Get user's job profile"
    ),
    StructuredTool.from_function(
        func=lambda: jobko_request("get_credits"),
        name="jobko_get_credits",
        description="Check Jobko credit balance"
    ),
    StructuredTool.from_function(
        func=lambda job_url: jobko_request("analyze_job", {"job_url": job_url}),
        name="jobko_analyze_job",
        description="Analyze a job posting URL. Costs $0.02."
    ),
]

# Create agent
llm = ChatOpenAI(model="gpt-4")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful job search assistant with access to Jobko tools."),
    MessagesPlaceholder(variable_name="chat_history", optional=True),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])

agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run
result = agent_executor.invoke({
    "input": "Analyze this job for me: https://linkedin.com/jobs/view/123456"
})
print(result["output"])

Using with LangGraph

from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model=ChatOpenAI(model="gpt-4"),
    tools=tools,
    state_modifier="You are a job search assistant with access to Jobko tools."
)

response = agent.invoke({
    "messages": [("user", "What's my skill profile?")]
})

Error Handling

def jobko_request_safe(name: str, arguments: dict = None):
    """Make a request to Jobko API with error handling."""
    try:
        response = requests.post(
            JOBKO_URL,
            headers={"Authorization": f"Bearer {JOBKO_API_KEY}"},
            json={"name": name, "arguments": arguments or {}},
            timeout=30
        )
        response.raise_for_status()
        return response.json()
    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 402:
            return {"error": "Insufficient credits. Please add credits at jobko.ai/dashboard"}
        elif e.response.status_code == 429:
            return {"error": "Rate limit exceeded. Please wait and try again."}
        raise