LangGraph

Use Portkey with LangGraph to take your AI agent workflows to production

Introduction

LangGraph is a library for building stateful, multi-actor applications with LLMs, designed to make developing complex agent workflows easier. It provides a flexible framework to create directed graphs where nodes process information and edges define the flow between them.

Portkey enhances LangGraph with production-readiness features, turning your experimental agent workflows into robust systems by providing:

• Complete observability of every agent step, tool use, and state transition
• Built-in reliability with fallbacks, retries, and load balancing
• Cost tracking and optimization to manage your AI spend
• Access to 1600+ LLMs through a single integration
• Guardrails to keep agent behavior safe and compliant
• Version-controlled prompts for consistent agent performance

LangGraph Official Documentation

Learn more about LangGraph’s core concepts and features

Installation & Setup

1. Install the required packages

   pip install -U langgraph langchain langchain_openai portkey-ai
   

   Depending on your use case, you may also need additional packages:

   • For search capabilities: pip install langchain_community
   • For memory functionality: pip install langgraph[checkpoint]
2. Generate API Key

   Create a Portkey API key with optional budget/rate limits from the Portkey dashboard. You can attach configurations for reliability, caching, and more to this key.

3. Configure LangChain with Portkey

   For a simple setup, configure a LangChain ChatOpenAI instance to use Portkey:

   from langchain_openai import ChatOpenAI
   from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
   
   # Set up LangChain model with Portkey
   llm = ChatOpenAI(
       api_key="dummy",  # This is just a placeholder
       base_url="https://api.portkey.ai/v1",
       default_headers=createHeaders(
           api_key="YOUR_PORTKEY_API_KEY",
           provider="@YOUR_PROVIDER",
           trace_id="unique-trace-id",               # Optional, for request tracing
           metadata={                                # Optional, for request segmentation
               "app_env": "production",
               "_user": "user_123"                   # Optional Special _user field for user analytics
           }
       )
   )
   

Basic Agent Implementation

Let’s create a simple LangGraph chatbot using Portkey. This example shows how to set up a basic conversational agent:

from typing import Annotated
from langchain_openai import ChatOpenAI
from typing_extensions import TypedDict
from portkey_ai import createHeaders
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages

# Define state structure with message history
class State(TypedDict):
    messages: Annotated[list, add_messages]

# Initialize graph builder
graph_builder = StateGraph(State)

# Set up LLM with Portkey
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_PROVIDER",
        trace_id="chat-session-123"  # Optional
    )
)

# Define chatbot node function
def chatbot(state: State):
    return {"messages": [llm.invoke(state["messages"])]}

# Add node to graph and set entry/exit points
graph_builder.add_node("chatbot", chatbot)
graph_builder.set_entry_point("chatbot")
graph_builder.set_finish_point("chatbot")
graph = graph_builder.compile()

# Function to handle streaming updates
def stream_graph_updates(user_input: str):
    for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
        for value in event.values():
            print("Assistant:", value["messages"][-1].content)

# Interactive chat loop
while True:
    try:
        user_input = input("User: ")
        if user_input.lower() in ["quit", "exit", "q"]:
            print("Goodbye!")
            break
        stream_graph_updates(user_input)
    except Exception as e:
        print(f"Error: {e}")
        break

This basic implementation:

1. Creates a state graph with a message history
2. Configures a ChatOpenAI model with Portkey
3. Defines a simple chatbot node that processes messages with the LLM
4. Compiles the graph and provides a streaming interface for chat

Advanced Features

1. Adding Tools to Your Agent

LangGraph can be enhanced with tools to allow your agent to perform actions. Here’s how to add the Tavily search tool:

from langgraph.prebuilt import ToolNode, tools_condition
from typing import Annotated
from langchain_openai import ChatOpenAI
from typing_extensions import TypedDict
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from portkey_ai import createHeaders
from langchain_community.tools.tavily_search import TavilySearchResults

class State(TypedDict):
    messages: Annotated[list, add_messages]

graph_builder = StateGraph(State)

# Initialize the Tavily search tool
# Note: Requires TAVILY_API_KEY environment variable or passed as argument
tool = TavilySearchResults(max_results=2)
tools = [tool]

# Set up LLM with Portkey
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_PROVIDER",
        trace_id="search-agent-session"  # Optional
    )
)
# Bind tools to the LLM
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)

# Add tool node to handle tool execution
tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

# Add conditional routing based on tool usage
graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
# Return to chatbot after tool execution
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")
graph = graph_builder.compile()

This example requires a Tavily API key for the search functionality. You can sign up for one at Tavily’s website.

2. Creating Custom Tools

You can create custom tools for your agents using the @tool decorator. Here’s how to create a simple multiplication tool:

from langchain_core.tools import tool
from pydantic import BaseModel, Field
from langgraph.prebuilt import ToolNode, tools_condition
from typing import Annotated
from langchain_openai import ChatOpenAI
from typing_extensions import TypedDict
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from portkey_ai import createHeaders

class State(TypedDict):
    messages: Annotated[list, add_messages]

# Define input schema for the tool
class MultiplyInputSchema(BaseModel):
    """Multiply two numbers"""
    a: int = Field(description="First operand")
    b: int = Field(description="Second operand")

# Create the tool
@tool("multiply_tool", args_schema=MultiplyInputSchema)
def multiply(a: int, b: int) -> int:
   return a * b

graph_builder = StateGraph(State)

tools = [multiply]

# Set up LLM with Portkey
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_PROVIDER",
    )
)
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)
tool_node = ToolNode(tools=tools)
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")
graph = graph_builder.compile()

This example:

1. Defines a Pydantic model for the tool’s input schema
2. Creates a custom multiplication tool with the @tool decorator
3. Integrates it into LangGraph with a tool node

3. Adding Memory to Your Agent

For persistent conversations, you can add memory to your LangGraph agents:

from typing import Annotated
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from typing_extensions import TypedDict
from portkey_ai import createHeaders
from langgraph.prebuilt import ToolNode, tools_condition
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages

class State(TypedDict):
    messages: Annotated[list, add_messages]

graph_builder = StateGraph(State)

# Set up tools and LLM with Portkey
tool = TavilySearchResults(max_results=2)
tools = [tool]
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_PROVIDER",
        trace_id="memory-agent-session"  # Optional
    )
)
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)
tool_node = ToolNode(tools=[tool])
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")

# Initialize memory saver for persistent state
memory = MemorySaver()
graph = graph_builder.compile(checkpointer=memory)

# Configuration for memory thread id
config = {"configurable": {"thread_id": "1"}}

# Function to handle streaming updates with memory
def stream_graph_updates(user_input: str):
    events = graph.stream(
        {"messages": [{"role": "user", "content": user_input}]},
        config,
        stream_mode="values",
    )
    for event in events:
        if "messages" in event:
            # Use pretty_print method for better formatting
            event["messages"][-1].pretty_print()

The thread_id in the config allows you to maintain separate conversation threads for different users or contexts.

Production Features

1. Enhanced Observability

Portkey provides comprehensive observability for your LangGraph agents, helping you understand exactly what’s happening during each execution.

Traces

Traces provide a hierarchical view of your agent’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.

# Add trace_id to enable hierarchical tracing in Portkey
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_LLM_PROVIDER",
        trace_id="unique-session-id",  # Add unique trace ID
        metadata={"request_type": "user_query"}
    )
)

LangGraph also offers its own tracing via LangSmith, which can be used alongside Portkey for even more detailed workflow insights.

Traces provide a hierarchical view of your agent’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.

# Add trace_id to enable hierarchical tracing in Portkey
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_LLM_PROVIDER",
        trace_id="unique-session-id",  # Add unique trace ID
        metadata={"request_type": "user_query"}
    )
)

LangGraph also offers its own tracing via LangSmith, which can be used alongside Portkey for even more detailed workflow insights.

Portkey logs every interaction with LLMs, including:

• Complete request and response payloads
• Latency and token usage metrics
• Cost calculations
• Tool calls and function executions

All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific agent runs.

Portkey provides built-in dashboards that help you:

• Track cost and token usage across all agent runs
• Analyze performance metrics like latency and success rates
• Identify bottlenecks in your agent workflows
• Compare different agent configurations and LLMs

You can filter and segment all metrics by custom metadata to analyze specific agent types, user groups, or use cases.

Add custom metadata to your LangGraph agent calls to enable powerful filtering and segmentation:

llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_LLM_PROVIDER",
        metadata={
            "agent_type": "search_agent",
            "environment": "production",
            "_user": "user_123",   # Special _user field for user analytics
            "graph_id": "complex_workflow"
        }
    )
)

This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific agent runs, users, or environments.

2. Reliability - Keep Your Agents Running Smoothly

When running agents in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey’s reliability features ensure your agents keep running smoothly even when problems occur.

Enable fallback in your LangGraph agents by using a Portkey Config:

from portkey_ai import createHeaders

# Create LLM with fallback configuration
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        config={
            "strategy": {
                "mode": "fallback"
            },
            "targets": [
                {
                    "provider":"@YOUR_OPENAI_VIRTUAL_API_KEY",
                    "override_params": {"model": "gpt-4o"}
                },
                {
                    "provider":"@YOUR_ANTHROPIC_VIRTUAL_API_KEY",
                    "override_params": {"model": "claude-3-opus-20240229"}
                }
            ]
        }
    )
)

This configuration will automatically try Claude if the GPT-4o request fails, ensuring your agent can continue operating.

Automatic Retries

Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips.

Request Timeouts

Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.

Conditional Routing

Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.

Fallbacks

Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.

Load Balancing

Spread requests across multiple API keys or providers. Great for high-volume agent operations and staying within rate limits.

3. Prompting in LangGraph

Portkey’s Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your LangGraph agents. Instead of hardcoding prompts or instructions, use Portkey’s prompt rendering API to dynamically fetch and apply your versioned prompts.

Manage prompts in Portkey's Prompt Library

Prompt Playground

Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It’s where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:

1. Iteratively develop prompts before using them in your agents
2. Test prompts with different variables and models
3. Compare outputs between different prompt versions
4. Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your LangGraph agent’s workflow.

Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It’s where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:

1. Iteratively develop prompts before using them in your agents
2. Test prompts with different variables and models
3. Compare outputs between different prompt versions
4. Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your LangGraph agent’s workflow.

The Prompt Render API retrieves your prompt templates with all parameters configured:

from portkey_ai import Portkey
from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# Initialize Portkey clients
portkey_admin = Portkey(api_key="YOUR_PORTKEY_API_KEY")

# Retrieve prompt using the render API
prompt_data = portkey_admin.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "user_input": "Tell me about artificial intelligence"
    }
).data.dict()

# Set up LLM with rendered system prompt
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER",
    )
)

# Define chatbot node with the rendered system prompt
def chatbot(state):
    messages = state["messages"]
    # Add the system prompt from Portkey to the beginning of the conversation
    all_messages = [
        {"role": "system", "content": prompt_data["messages"][0]["content"]},
        *messages
    ]
    return {"messages": [llm.invoke(all_messages)]}

You can:

• Create multiple versions of the same prompt
• Compare performance between versions
• Roll back to previous versions if needed
• Specify which version to use in your code:

# Use a specific prompt version
prompt_data = portkey_admin.prompts.render(
    prompt_id="YOUR_PROMPT_ID@version_number",
    variables={
        "user_input": "Tell me about quantum computing"
    }
)

Portkey prompts use Mustache-style templating for easy variable substitution:

You are an AI assistant specialized in {{agent_role}}.

User question: {{user_input}}

Please respond in a {{tone}} tone and include {{required_elements}}.

When rendering, simply pass the variables:

prompt_data = portkey_admin.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "agent_role": "search navigator",
        "user_input": "Find information about climate change",
        "tone": "informative",
        "required_elements": "recent scientific findings"
    }
)

Prompt Engineering Studio

Learn more about Portkey’s prompt management features

4. Guardrails for Safe Agents

Guardrails ensure your LangGraph agents operate safely and respond appropriately in all situations.

Why Use Guardrails?

LangGraph agents can experience various failure modes:

• Generating harmful or inappropriate content
• Leaking sensitive information like PII
• Hallucinating incorrect information
• Generating outputs in incorrect formats

Portkey’s guardrails add protection for both inputs and outputs.

Implementing Guardrails

from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# Create LLM with guardrails
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER",
        config={
            "input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"],
            "output_guardrails": ["guardrails-id-zzz"]
        }
    )
)

Portkey’s guardrails can:

• Detect and redact PII in both inputs and outputs
• Filter harmful or inappropriate content
• Validate response formats against schemas
• Check for hallucinations against ground truth
• Apply custom business logic and rules

Learn More About Guardrails

Explore Portkey’s guardrail features to enhance agent safety

5. User Tracking with Metadata

Track individual users through your LangGraph agents using Portkey’s metadata system.

What is Metadata in Portkey?

Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special _user field is specifically designed for user tracking.

from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# Configure LLM with user tracking
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER",
        metadata={
            "_user": "user_123",  # Special _user field for user analytics
            "user_tier": "premium",
            "user_company": "Acme Corp",
            "session_id": "abc-123",
            "graph_id": "search_workflow"
        }
    )
)

Filter Analytics by User

With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:

Filter analytics by user

This enables:

• Per-user cost tracking and budgeting
• Personalized user analytics
• Team or organization-level metrics
• Environment-specific monitoring (staging vs. production)

Learn More About Metadata

Explore how to use custom metadata to enhance your analytics

6. Caching for Efficient Agents

Implement caching to make your LangGraph agents more efficient and cost-effective:

Simple Caching

from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# Configure LLM with simple caching
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER",
        config={
            "cache": {
                "mode": "simple"
            }
        }
    )
)

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# Configure LLM with simple caching
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER",
        config={
            "cache": {
                "mode": "simple"
            }
        }
    )
)

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# Configure LLM with semantic caching
llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER",
        config={
            "cache": {
                "mode": "semantic"
            }
        }
    )
)

Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs.

7. Model Interoperability

LangGraph works with multiple LLM providers, and Portkey extends this capability by providing access to over 200 LLMs through a unified interface. You can easily switch between different models without changing your core agent logic:

from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders

# OpenAI configuration
openai_llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_OPENAI_PROVIDER"
    )
)

# Anthropic configuration
anthropic_llm = ChatOpenAI(
    api_key="dummy",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        provider="@YOUR_ANTHROPIC_PROVIDER"
    )
)

# Choose which LLM to use based on your needs
active_llm = openai_llm  # or anthropic_llm

# Use in your LangGraph nodes
def chatbot(state):
    return {"messages": [active_llm.invoke(state["messages"])]}

Portkey provides access to LLMs from providers including:

• OpenAI (GPT-4o, GPT-4 Turbo, etc.)
• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.)
• Mistral AI (Mistral Large, Mistral Medium, etc.)
• Google Vertex AI (Gemini 1.5 Pro, etc.)
• Cohere (Command, Command-R, etc.)
• AWS Bedrock (Claude, Titan, etc.)
• Local/Private Models

Supported Providers

See the full list of LLM providers supported by Portkey

Set Up Enterprise Governance for LangGraph

Why Enterprise Governance? If you are using LangGraph inside your organization, you need to consider several governance aspects:

• Cost Management: Controlling and tracking AI spending across teams
• Access Control: Managing which teams can use specific models
• Usage Analytics: Understanding how AI is being used across the organization
• Security & Compliance: Maintaining enterprise security standards
• Reliability: Ensuring consistent service across all users

Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step.

1. Create Integration

   To create a new LLM integration: Go to Integrations in the Portkey App. Set budget / rate limits, model access if required and save the integration.

   This creates a “Portkey Provider” that you can then use in any of your Portkey requests without having to send auth details for that LLM provider again.

2. Create Config

   Configs in Portkey define how your requests are routed, with features like advanced routing, fallbacks, and retries.

   To create your config:

   1. Go to Configs in Portkey dashboard
   2. Create new config with:
      {
          "provider":"@YOUR_PROVIDER_FROM_STEP1",
         	"override_params": {
            "model": "gpt-4o" // Your preferred model name
          }
      }
      

   3. Save and note the Config name for the next step

   
3. Configure Portkey API Key

   Now create a Portkey API key and attach the config you created in Step 2:

   1. Go to API Keys in Portkey and Create new API key
   2. Select your config from Step 2
   3. Generate and save your API key

   
4. Connect to LangGraph

   After setting up your Portkey API key with the attached config, connect it to your LangGraph agents:

   from langchain_openai import ChatOpenAI
   from portkey_ai import createHeaders
   
   # Configure LLM with your Portkey API key
   llm = ChatOpenAI(
       api_key="dummy",
       base_url="https://api.portkey.ai/v1",
       default_headers=createHeaders(
           api_key="YOUR_PORTKEY_API_KEY"  # The API key with attached config from step 3
       )
   )
   

Step 1: Implement Budget Controls & Rate Limits

Step 1: Implement Budget Controls & Rate Limits

Integrations enable granular control over LLM access at the team/department level. This helps you:

• Set up budget limits
• Prevent unexpected usage spikes using Rate limits
• Track departmental spending

Setting Up Department-Specific Controls:

1. Navigate to Integrations in Portkey dashboard and create a new Integration.
2. Provision this Integration for each department with their budget limits and rate limits
3. Configure model access if required.

Step 2: Define Model Access Rules

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like:

Access Control Features:

• Model Restrictions: Limit access to specific models
• Data Protection: Implement guardrails for sensitive data
• Reliability Controls: Add fallbacks and retry logic

Example Configuration:

Here’s a basic configuration to route requests to OpenAI, specifically using GPT-4o:

{
	"strategy": {
		"mode": "single"
	},
	"targets": [
		{
			"provider":"@YOUR_OPENAI_PROVIDER",
			"override_params": {
				"model": "gpt-4o"
			}
		}
	]
}

Create your config on the Configs page in your Portkey dashboard.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 3: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

• Track usage per user/team with the help of metadata
• Apply appropriate configs to route requests
• Collect relevant metadata to filter logs
• Enforce access permissions

Create API keys through:

• Portkey App
• API Key Management API

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="engineering-team",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "production",
            "department": "engineering"
        }
    },
    scopes=["logs.view", "configs.read"]
)

For detailed key management instructions, see our API Keys documentation.

Step 4: Deploy & Monitor

Step 4: Deploy & Monitor

After distributing API keys to your team members, your enterprise-ready LangGraph setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls.

Monitor usage in Portkey dashboard:

• Cost tracking by department
• Model usage patterns
• Request volumes
• Error rates

Enterprise Features Now Available

Your LangGraph integration now has:

• Departmental budget controls
• Model access governance
• Usage tracking & attribution
• Security guardrails
• Reliability features

Frequently Asked Questions

How does Portkey enhance LangGraph?

Portkey adds production-readiness to LangGraph through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 1600+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent workflows.

Can I use Portkey with existing LangGraph applications?

Yes! Portkey integrates seamlessly with existing LangGraph applications. You just need to replace your LLM initialization code with the Portkey-enabled version. The rest of your graph code remains unchanged.

Does Portkey work with all LangGraph features?

Portkey supports all LangGraph features, including tools, memory, conditional routing, and complex multi-node workflows. It adds observability and reliability without limiting any of the framework’s functionality.

How do I filter logs and traces for specific graph runs?

Portkey allows you to add custom metadata and trace IDs to your LLM calls, which you can then use for filtering. Add fields like graph_id, workflow_type, or session_id to easily find and analyze specific graph executions.

Can I use LangGraph's memory features with Portkey?

Yes! The examples in this documentation show how to use LangGraph’s MemorySaver checkpointer with Portkey-enabled LLMs. All the memory and state management features work seamlessly with Portkey.

Resources

LangGraph Docs

Official LangGraph documentation

Portkey Docs

Official Portkey documentation

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