> ## Documentation Index > Fetch the complete documentation index at: https://docs.obiguard.ai/llms.txt > Use this file to discover all available pages before exploring further. # Overview > Obiguard helps bring your agents to production OpenAI Agents

## Integrate Obiguard with your agents with just 2 lines of code ```py Langchain theme={null} from langchain_openai import ChatOpenAI from obiguard import OBIGUARD_GATEWAY_URL, createHeaders llm = ChatOpenAI( api_key="OpenAI_API_Key", base_url=OBIGUARD_GATEWAY_URL, default_headers=createHeaders( provider="openai", #choose your provider obiguard_api_key="sk-obg******", # Your Obiguard API key ) ) ``` ## Key Production Features By routing your agent's requests through Obiguard, you make your agents production-grade with the following features. ### 1. [Interoperability](/gateway/universal-api) Easily switch between LLM providers. Call various LLMs such as Anthropic, Gemini, Mistral, Azure OpenAI, Google Vertex AI, AWS Bedrock and much more by simply changing the `provider ` and `Obiguard API key` in the LLM object. ### 2. [Observability](/product/observability) Obiguard automatically logs key details about your agent runs, including cost, tokens used, response time, etc. For agent-specific observability, add Trace IDs to the request headers for each agent. This enables filtering analytics by Trace IDs, ensuring deeper monitoring and analysis. ### 3. [Logs](/product/observability/logs) Access a dedicated section to view records of action executions, including parameters, outcomes, and errors. Filter logs of your agent run based on multiple parameters such as trace ID, model, tokens used, metadata, etc.