Screenshots on this page show the previous product branding and navigation. The workflow they
illustrate is current; the interface around it has since been rebranded to Prisma AIRS.
OpenAI Agents (Python)
OpenAI Agents (Type Script)
AWS AgentCore
Pydantic AI
Autogen
CrewAI
Agno AI
Mastra Agents
Llama Index
LangChain
LangGraph
Langroid
OpenAI Swarm
Control Flow
AWS AgentCore
Strands Agents
Bring Your Agent
Integrate the AI Gateway with your agents with just 2 lines of code
Get Started with the AI Gateway x Agent Cookbooks
Key Production Features
By routing your agent’s requests through the AI Gateway, you make your agents production-grade with the following features.1. Interoperability
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 theprovider and API key in the LLM object.
2. Caching
Improve performance and reduce costs on your Agent’s LLM calls by storing past responses in the AI Gateway cache. Choose between Simple and Semantic cache modes in your AI Gateway config.3. Reliability
Set up fallbacks between different LLMs or providers, load balance your requests across multiple instances or API keys, set automatic retries, and request timeouts. Ensure your agents’ resilience with advanced reliability features.4. Observability
The AI Gateway 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.5. 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.

