> ## Documentation Index
> Fetch the complete documentation index at: https://portkey-docs-feature-comparison-update.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Autogen
> Use Portkey with Autogen to take your AI Agents to production
## Getting Started
### 1. Install the required packages
```sh theme={null}
pip install -U "autogen-agentchat" "autogen-ext[openai,azure]" portkey-ai
```
### 2. Quickstart: Autogen AgentChat with Portkey
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_core.models import ModelFamily
from autogen_ext.models.openai import OpenAIChatCompletionClient
import asyncio
# Define a model client that talks to Portkey's OpenAI-compatible endpoint.
# Use a Model Catalog model string: "@provider_slug/model_name"
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={
"vision": True,
"function_calling": True,
"json_output": True,
"structured_output": True,
# Use GPT family for gpt-4o to allow tools + system message
"family": ModelFamily.GPT_45,
},
# Optional headers: attach Portkey Configs, tracing, or metadata
default_headers={
"x-portkey-config": "pc-xxxx",
"x-portkey-trace-id": "trace-id",
}
)
# Define a simple function tool that the agent can use.
async def get_weather(city: str) -> str:
"""Get the weather for a given city."""
return f"The weather in {city} is 73 degrees and Sunny."
# Create an AssistantAgent with the Portkey-backed model client
agent = AssistantAgent(
name="weather_agent",
model_client=model_client,
tools=[get_weather],
system_message="You are a helpful assistant.",
reflect_on_tool_use=True,
model_client_stream=True, # Enable token streaming
)
async def main() -> None:
await Console(agent.run_stream(task="What is the weather in New York?"))
await model_client.close()
if __name__ == "__main__":
asyncio.run(main())
```
#### Model Catalog
* Portkey now uses Model Catalog instead of Virtual Keys. Reference models directly with `model="@provider_slug/model_name"`.
* Learn more: [Upgrade to Model Catalog](/support/upgrade-to-model-catalog) and [Model Catalog](/product/model-catalog).
## Production Features
### 1. Enhanced Observability
Portkey provides comprehensive observability for your Autogen agents, helping you understand exactly what's happening during each execution.
Traces provide a hierarchical view of your agent's execution, showing the sequence of LLM calls, tool invocations, and state transitions.
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
import asyncio
# Add tracing to your Autogen agents via Portkey headers
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45},
default_headers={
"x-portkey-trace-id": "unique_execution_trace_id"
}
)
agent = AssistantAgent(
name="observer",
model_client=model_client,
system_message="You are a helpful assistant."
)
async def main():
await Console(agent.run_stream(task="Say hello"))
await model_client.close()
asyncio.run(main())
```
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 Autogen agent calls to enable powerful filtering and segmentation:
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
from portkey_ai import createHeaders
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45},
default_headers=createHeaders(
metadata={"agent_type": "research_agent"}
)
)
agent = AssistantAgent(
name="research_agent",
model_client=model_client,
system_message="You are a research assistant."
)
```
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 Autogen 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.
It's this simple to enable fallback in your Autogen agents:
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
from portkey_ai import createHeaders
# Create a config with fallbacks (recommend creating this in Portkey App)
config = {
"strategy": {"mode": "fallback"},
"targets": [
{"override_params": {"model": "@your-provider-slug/gpt-4o"}},
{"override_params": {"model": "@your-anthropic-provider/claude-3-opus-20240229"}}
]
}
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45},
default_headers=createHeaders(config=config)
)
agent = AssistantAgent(
name="resilient_agent",
model_client=model_client,
system_message="You are a helpful assistant."
)
```
This configuration will automatically try Claude if the GPT-4o request fails, ensuring your agent can continue operating.
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.
Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.
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.
Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.
Spread requests across multiple API keys or providers. Great for high-volume agent operations and staying within rate limits.
### 3. Prompting in Autogen Agents
Portkey's Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your Autogen agents. Instead of hardcoding prompts or instructions, use Portkey's prompt rendering API to dynamically fetch and apply your versioned prompts.
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 Autogen agent's workflow.
The Prompt Render API retrieves your prompt templates with all parameters configured:
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
from portkey_ai import Portkey
import asyncio
# Initialize Portkey admin client to render prompts
portkey_admin = Portkey(api_key="YOUR_PORTKEY_API_KEY")
prompt_data = portkey_admin.prompts.render(
prompt_id="YOUR_PROMPT_ID",
variables={"user_input": "Tell me about artificial intelligence"}
)
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45}
)
agent = AssistantAgent(
name="assistant",
model_client=model_client,
system_message=prompt_data.data.messages[0]["content"]
)
async def main():
await Console(agent.run_stream(task="Tell me about artificial intelligence"))
await model_client.close()
asyncio.run(main())
```
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:
```python theme={null}
# Use a specific prompt version
prompt_data = portkey_client.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 helping with {{task_type}}.
User question: {{user_input}}
Please respond in a {{tone}} tone and include {{required_elements}}.
```
When rendering, simply pass the variables:
```python theme={null}
prompt_data = portkey_client.prompts.render(
prompt_id="YOUR_PROMPT_ID",
variables={
"task_type": "research",
"user_input": "Tell me about quantum computing",
"tone": "professional",
"required_elements": "recent academic references"
}
)
```
Learn more about Portkey's prompt management features
### 4. Guardrails for Safe Autogen Agents
Guardrails ensure your Autogen agents operate safely and respond appropriately in all situations.
**Why Use Guardrails?**
Autogen 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 protect against these issues by validating both inputs and outputs.
**Implementing Guardrails**
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
from portkey_ai import createHeaders
# Create a config with input and output guardrails (recommend using Portkey App)
config = {
"input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"],
"output_guardrails": ["guardrails-id-xxx"]
}
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45},
default_headers=createHeaders(config=config)
)
agent = AssistantAgent(
name="safe_agent",
model_client=model_client,
system_message="You are a helpful assistant that provides safe responses."
)
```
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
Explore Portkey's guardrail features to enhance agent safety
### 5. User Tracking with Metadata
Track individual users through your Autogen 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.
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
from portkey_ai import createHeaders
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45},
default_headers=createHeaders(
metadata={
"_user": "user_123",
"user_name": "John Doe",
"user_tier": "premium",
"user_company": "Acme Corp"
}
)
)
agent = AssistantAgent(
name="personalized_agent",
model_client=model_client,
system_message="You are a personalized assistant."
)
```
**Filter Analytics by User**
With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:
This enables:
* Per-user cost tracking and budgeting
* Personalized user analytics
* Team or organization-level metrics
* Environment-specific monitoring (staging vs. production)
Explore how to use custom metadata to enhance your analytics
### 6. Caching for Efficient Agents
Implement caching to make your Autogen agents more efficient and cost-effective:
```python theme={null}
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily
from portkey_ai import createHeaders
portkey_config = {"cache": {"mode": "simple"}}
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-provider-slug/gpt-4o",
model_info={"family": ModelFamily.GPT_45},
default_headers=createHeaders(config=portkey_config)
)
agent = AssistantAgent(
name="cached_agent",
model_client=model_client,
system_message="You are a helpful assistant."
)
```
Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.
### 7. Model Interoperability: using different LLMs
One of Portkey's key strengths is providing access to 1600+ LLMs through a unified interface. Here's how to use different providers with Autogen:
See the full list of LLM providers supported by Portkey
```python theme={null}
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-openai-provider-slug/gpt-4o",
)
```
```python theme={null}
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-anthropic-provider-slug/claude-3-7-sonnet-latest",
)
```
```python theme={null}
model_client = OpenAIChatCompletionClient(
base_url="https://api.portkey.ai/v1",
api_key="YOUR_PORTKEY_API_KEY",
model="@your-google-provider-slug/gemini-2.5-pro",
)
```
## Set Up Enterprise Governance for Autogen agents
**Why Enterprise Governance?**
If you are using Autogen agents 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.
**Enterprise Implementation Guide**
Portkey allows you to use 1600+ LLMs with your Autogen setup, with minimal configuration required. Let's set up the core components in Portkey that you'll need for integration.
### Step 1: Implement Budget Controls & Rate Limits
Model Catalog enables you to have granular control over LLM access at the team/department level. This helps you:
* Set up [budget limits](/product/model-catalog/integrations#3-budget-%26-rate-limits)
* Prevent unexpected usage spikes using Rate limits
* Track departmental spending
#### Setting Up Department-Specific Controls:
1. Navigate to [Model Catalog](https://app.portkey.ai/model-catalog) in Portkey dashboard
2. Create new Provider for each engineering team with budget limits and rate limits
3. Configure department-specific limits
### Step 2: Define Model Access Rules
As your AI usage scales, controlling which teams can access specific models becomes crucial. You can simply manage AI models in your org by provisioning model at the top integration level.
Portkey allows you to control your routing logic very simply with it's Configs feature. Portkey Configs provide this control layer with things like:
* **Data Protection**: Implement guardrails for sensitive code and data
* **Reliability Controls**: Add fallbacks, load-balance, retry and smart conditional routing logic
* **Caching**: Implement Simple and Semantic Caching. and more....
#### Example Configuration:
Here's a basic configuration to load-balance requests to OpenAI and Anthropic:
```json theme={null}
{
"strategy": {
"mode": "load-balance"
},
"targets": [
{
"override_params": {
"model": "@YOUR_OPENAI_PROVIDER-SLUG/MODEL_NAME"
}
},
{
"override_params": {
"model": "@YOUR_ANTHROPIC_PROVIDER-SLUG/MODEL_NAME"
}
}
]
}
```
Create your config on the [Configs page](https://app.portkey.ai/configs) in your Portkey dashboard. You'll need the config ID for connecting to your Autogen setup.
Configs can be updated anytime to adjust controls without affecting running applications.
### Step 3: Implement Access Controls
Create User-specific API keys that automatically:
* Track usage per developer/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](https://app.portkey.ai/)
* [API Key Management API](/api-reference/admin-api/control-plane/api-keys/create-api-key)
Example using Python SDK:
```python theme={null}
from portkey_ai import Portkey
portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")
api_key = portkey.api_keys.create(
name="frontend-engineering",
type="organisation",
workspace_id="YOUR_WORKSPACE_ID",
defaults={
"config_id": "your-config-id",
"metadata": {
"environment": "development",
"department": "engineering",
"team": "frontend"
}
},
scopes=["logs.view", "configs.read"]
)
```
For detailed key management instructions, see our [API Keys documentation](/api-reference/admin-api/control-plane/api-keys/create-api-key).
### Step 4: Deploy & Monitor
After distributing API keys to your engineering teams, your enterprise-ready Autogen setup is ready to go. Each developer can now use their designated API keys with appropriate access levels and budget controls.
Apply your governance setup using the integration steps from earlier sections
Monitor usage in Portkey dashboard:
* Cost tracking by engineering team
* Model usage patterns for AI agent tasks
* Request volumes
* Error rates and debugging logs
### Enterprise Features Now Available
**Autogen agents now have:**
* Departmental budget controls
* Model access governance
* Usage tracking & attribution
* Security guardrails
* Reliability features
## Frequently Asked Questions
Portkey adds production-grade features to Autogen agents including comprehensive observability (traces, logs, analytics), reliability (fallbacks, retries, load balancing), access to 1600+ LLMs, cost management, and enterprise governance - all without changing your agent logic.
Yes! Portkey provides access to 1600+ LLMs from providers like OpenAI, Anthropic, Google, Cohere, and many more. Just change the model ID in your configuration to switch between providers.
Portkey automatically tracks costs for all LLM calls. You can segment costs by agent type, user, or custom metadata. Set up AI Provider integrations with budget limits to control spending on Model Catalog.
Yes! Portkey works seamlessly with Autogen's tool use and agent workflows. It adds observability and reliability without limiting any Autogen functionality.
Portkey's detailed logs and traces make debugging easy. You can see the complete execution flow, including failed tool calls, LLM errors, and retry attempts. Filter by trace ID or metadata to find specific issues.
## Resources
Learn more about building agents with Autogen
Explore all Portkey capabilities
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