OpenAI Agents (Python)

OpenAI Agents

OpenAI Agents (Type Script)

OpenAI Agents

Pydantic AI

Pydantic AI Agents

Autogen

Autogen

CrewAI

CrewAI

Agno AI

Agno-AI

Llama Index

Llama Index

LangChain

LangChain

LangGraph

Langgraph

Langroid

Langroid

OpenAI Swarm

Swarm

Control Flow

Control Flow

Strands Agents

Strands

Bring Your Agent

Bring Your Agent

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Integrate Portkey with your agents with just 2 lines of code

Langchain
from langchain_openai import ChatOpenAI
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL

llm = ChatOpenAI(
    api_key="OpenAI_API_Key",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai", #choose your provider
        api_key="PORTKEY_API_KEY"
    )
)

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Get Started with Portkey x Agent Cookbooks

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Key Production Features

By routing your agent’s requests through Portkey, you make your agents production-grade with the following features.

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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 the provider and API key in the LLM object.

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2. Caching

Improve performance and reduce costs on your Agent’s LLM calls by storing past responses in the Portkey cache. Choose between Simple and Semantic cache modes in your Portkey’s gateway config. 
{
 "cache": {
    "mode": "semantic" // Choose between "simple" or "semantic"
 }
}

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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. 
{
  "retry": {
    "attempts": 5
  },
  "strategy": {
    "mode": "loadbalance" // Choose between "loadbalance" or "fallback"
  },
  "targets": [
    {
      "provider": "openai",
      "api_key": "OpenAI_API_Key"
    },
    {
      "provider": "anthropic",
      "api_key": "Anthropic_API_Key"
    }
  ]
}

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4. Observability

Portkey 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.

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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.
azure

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6. Prompt Management

Use Portkey as a centralized hub to store, version, and experiment with your agent’s prompts across multiple LLMs. Easily modify your prompts and run A/B tests without worrying about the breaking prod.

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7. Continuous Improvement

Improve your Agent runs by capturing qualitative & quantitative user feedback on your requests, and then using that feedback to make your prompts AND LLMs themselves better.

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8. Security & Compliance

Set budget limits on provider API keys and implement fine-grained user roles and permissions for both the app and the Portkey APIs.