> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Portkey integrations > Integrate with Portkey using LangChain Python. [Portkey](https://portkey.ai) is the Control Panel for AI apps. With it's popular AI Gateway and Observability Suite, hundreds of teams ship **reliable**, **cost-efficient**, and **fast** apps. ## LLMOps for LangChain Portkey brings production readiness to LangChain. With Portkey, you can * [x] Connect to 150+ models through a unified API, * [x] View 42+ **metrics & logs** for all requests, * [x] Enable **semantic cache** to reduce latency & costs, * [x] Implement automatic **retries & fallbacks** for failed requests, * [x] Add **custom tags** to requests for better tracking and analysis and [more](https://portkey.ai/docs). ## Quickstart - portkey & LangChain Since Portkey is fully compatible with the OpenAI signature, you can connect to the Portkey AI Gateway through the [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) interface. * Set the `base_url` as `PORTKEY_GATEWAY_URL` * Add `default_headers` to consume the headers needed by Portkey using the `createHeaders` helper method. To start, get your Portkey API key by [signing up here](https://app.portkey.ai/signup). (Click the profile icon on the bottom left, then click on "Copy API Key") or deploy the open source AI gateway in [your own environment](https://github.com/Portkey-AI/gateway/blob/main/docs/installation-deployments.md). Next, install the Portkey SDK ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U portkey_ai ``` We can now connect to the Portkey AI Gateway by updating the [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) model in LangChain ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL PORTKEY_API_KEY = "..." # Not needed when hosting your own gateway PROVIDER_API_KEY = "..." # Add the API key of the AI provider being used portkey_headers = createHeaders(api_key=PORTKEY_API_KEY,provider="openai") llm = ChatOpenAI(api_key=PROVIDER_API_KEY, base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers) llm.invoke("What is the meaning of life, universe and everything?") ``` The request is routed through your Portkey AI Gateway to the specified `provider`. Portkey will also start logging all the requests in your account that makes debugging extremely simple. ![View logs from LangChain in Portkey](https://assets.portkey.ai/docs/langchain-logs.gif) ## Using 150+ models through the AI Gateway The power of the AI gateway comes when you're able to use the above code snippet to connect with 150+ models across 20+ providers supported through the AI gateway. Let's modify the code above to make a call to Anthropic's `claude-opus-4-8` model. Portkey supports **[Virtual Keys](https://docs.portkey.ai/docs/product/ai-gateway-streamline-llm-integrations/virtual-keys)** which are an easy way to store and manage API keys in a secure vault. Let's try using a Virtual Key to make LLM calls. You can navigate to the Virtual Keys tab in Portkey and create a new key for Anthropic. The `virtual_key` parameter sets the authentication and provider for the AI provider being used. In our case we're using the Anthropic Virtual key. > Notice that the `api_key` can be left blank as that authentication won't be used. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL PORTKEY_API_KEY = "..." VIRTUAL_KEY = "..." # Anthropic's virtual key we copied above portkey_headers = createHeaders(api_key=PORTKEY_API_KEY,virtual_key=VIRTUAL_KEY) llm = ChatOpenAI(api_key="X", base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers, model="claude-opus-4-8") llm.invoke("What is the meaning of life, universe and everything?") ``` The Portkey AI gateway will authenticate the API request to Anthropic and get the response back in the OpenAI format for you to consume. The AI gateway extends LangChain's [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) class making it a single interface to call any provider and any model. ## Advanced routing - load balancing, fallbacks, retries The Portkey AI Gateway brings capabilities like load-balancing, fallbacks, experimentation and canary testing to LangChain through a configuration-first approach. Let's take an **example** where we might want to split traffic between `gpt-4` and `claude-opus` 50:50 to test the two large models. The gateway configuration for this would look like the following: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} config = { "strategy": { "mode": "loadbalance" }, "targets": [{ "virtual_key": "openai-25654", # OpenAI's virtual key "override_params": {"model": "gpt4"}, "weight": 0.5 }, { "virtual_key": "anthropic-25654", # Anthropic's virtual key "override_params": {"model": "claude-opus-4-8"}, "weight": 0.5 }] } ``` We can then use this config in our requests being made from langchain. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} portkey_headers = createHeaders( api_key=PORTKEY_API_KEY, config=config ) llm = ChatOpenAI(api_key="X", base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers) llm.invoke("What is the meaning of life, universe and everything?") ``` When the LLM is invoked, Portkey will distribute the requests to `gpt-4` and `claude-opus-4-8` in the ratio of the defined weights. You can find more [Portkey config examples](https://docs.portkey.ai/docs/api-reference/config-object#examples). ## **Tracing chains & agents** Portkey's LangChain integration gives you full visibility into the running of an agent. Let's take an example of a [popular agentic workflow](https://python.langchain.com/docs/use_cases/tool_use/quickstart/#agents). We only need to modify the [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) class to use the AI Gateway as above. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_classic import hub from langchain.agents import AgentExecutor, create_openai_tools_agent from langchain_openai import ChatOpenAI from langchain.tools import tool from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders prompt = hub.pull("hwchase17/openai-tools-agent") portkey_headers = createHeaders( api_key=PORTKEY_API_KEY, virtual_key=OPENAI_VIRTUAL_KEY, trace_id="uuid-uuid-uuid-uuid" ) @tool def multiply(first_int: int, second_int: int) -> int: """Multiply two integers together.""" return first_int * second_int @tool def exponentiate(base: int, exponent: int) -> int: "Exponentiate the base to the exponent power." return base**exponent tools = [multiply, exponentiate] model = ChatOpenAI(api_key="X", base_url=PORTKEY_GATEWAY_URL, default_headers=portkey_headers, temperature=0) # Construct the OpenAI Tools agent agent = create_openai_tools_agent(model, tools, prompt) # Create an agent executor by passing in the agent and tools agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) agent_executor.invoke({ "input": "Take 3 to the fifth power and multiply that by thirty six, then square the result" }) ``` **You can see the requests' logs along with the trace id on Portkey dashboard:** ![LangChain Agent Logs on Portkey](https://assets.portkey.ai/docs/agent_tracing.gif) Additional Docs are available here: * Observability - [portkey.ai/docs/product/observability-modern-monitoring-for-llms](https://portkey.ai/docs/product/observability-modern-monitoring-for-llms) * AI Gateway - [portkey.ai/docs/product/ai-gateway-streamline-llm-integrations](https://portkey.ai/docs/product/ai-gateway-streamline-llm-integrations) * Prompt Library - [portkey.ai/docs/product/prompt-library](https://portkey.ai/docs/product/prompt-library) You can check out our popular Open Source AI Gateway here - [github.com/portkey-ai/gateway](https://github.com/portkey-ai/gateway) For detailed information on each feature and how to use it, [please refer to the Portkey docs](https://portkey.ai/docs). If you have any questions or need further assistance, [reach out to us on Twitter.](https://twitter.com/portkeyai) or our [support email](mailto:hello@portkey.ai). ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/providers/portkey/index.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).