# Genkit Python SDK 0.5.0: A Major Leap Forward
Building intelligent AI-powered applications in Python just got significantly better. Today, we're thrilled to announce the release of **Genkit Python SDK 0.5.0**—our most significant update yet, with **178 commits**, **680+ files changed**, and contributions from **13 developers** across **188 PRs** over the past 8 months.
This release transforms Genkit for Python from an experimental SDK into a production-ready framework with comprehensive plugin coverage, enterprise-grade security, and first-class developer experience.
## What's New in 0.5.0
### Massive Plugin Ecosystem Expansion
We've added **7 new model provider plugins** and **3 telemetry plugins**, giving you access to virtually every major AI provider:
**New Model Providers:**
- **AWS Bedrock**: Access Claude, Titan, Llama, and more through AWS
- **Azure OpenAI (Microsoft Foundry)**: Enterprise Azure OpenAI integration
- **Cloudflare Workers AI**: Edge AI with Cloudflare's global network
- **Mistral AI**: Mistral Large, Small, Codestral, and Pixtral models
- **Hugging Face**: 17+ inference providers through one plugin
- **Anthropic**: Full Claude model support
- **DeepSeek**: DeepSeek models with structured output
**New Telemetry Plugins:**
- **AWS X-Ray**: Production observability with SigV4 signing
- **Observability**: Third-party backends (Sentry, Honeycomb, Datadog)
- **Google Cloud Telemetry**: Full parity with JS/Go SDKs
### Async-First Architecture
The Python SDK now embraces async-first design throughout. Here's how clean your code can be:
```python
from genkit.ai import Genkit
from genkit.plugins.google_genai import GoogleAI
ai = Genkit(
plugins=[GoogleAI()],
model='googleai/gemini-2.5-flash',
)
@ai.flow()
async def analyze_sentiment(text: str) -> str:
"""Analyzes sentiment of the given text."""
response = await ai.generate(
prompt=f'Analyze the sentiment of this text: {text}'
)
return response.text # Property, not method
```
### Agentive Tool Calling
Genkit makes it easy to give your AI agents the ability to call functions. Define tools with the `@ai.tool()` decorator:
```python
from pydantic import BaseModel, Field
class WeatherInput(BaseModel):
location: str = Field(description='City and state, e.g. San Francisco, CA')
@ai.tool()
def get_weather(input: WeatherInput) -> dict:
"""Get the current weather for a location."""
return {
'location': input.location,
'temperature': 21.5,
'conditions': 'sunny',
}
@ai.flow()
async def weather_agent(location: str) -> str:
"""AI agent that can check the weather."""
response = await ai.generate(
prompt=f"What's the weather in {location}?",
tools=[get_weather],
)
return response.text
```
### Enhanced Dotprompt Integration
We've deeply integrated with [Dotprompt](https://github.com/google/dotprompt), our prompt templating engine, bringing:
- **Directory/file prompt loading**: Automatic prompt discovery matching the JS SDK
- **Handlebars partials**: Template reuse with `define_partial`
- **Python 3.14 support**: Full compatibility via our Rust-based Handlebars engine
- **Cycle detection**: Prevents infinite recursion in partial resolution
- **Path traversal hardening**: Security fix for CWE-22 vulnerability
```python
from genkit.ai import Genkit
from genkit.plugins.google_genai import GoogleAI
ai = Genkit(plugins=[GoogleAI()], model='googleai/gemini-2.5-flash')
# Define partials for reusable template components
ai.define_partial('greeting', 'Hello, {{name}}!')
ai.define_partial('signature', '\n\nBest regards,\n{{sender}}')
# Use partials in prompts ({{> greeting}} and {{> signature}})
@ai.flow()
async def send_email(name: str, sender: str) -> str:
response = await ai.generate(
prompt=f'Write an email to {name} from {sender}'
)
return response.text
```
### Comprehensive Type Safety
We now run **three type checkers** on every commit:
| Type Checker | Provider | Purpose |
|-------------|----------|---------|
| **ty** | Astral (Ruff) | Fast, strict checking |
| **pyrefly** | Meta | Additional coverage |
| **pyright** | Microsoft | Full type analysis |
This means you get better IDE support, fewer runtime errors, and more confident refactoring.
### Pydantic Output Instances
Generate structured data directly into Pydantic models:
```python
from pydantic import BaseModel, Field
from genkit.ai import Genkit, Output
from genkit.plugins.google_genai import GoogleAI
ai = Genkit(plugins=[GoogleAI()], model='googleai/gemini-2.5-flash')
class RpgCharacter(BaseModel):
name: str = Field(description='name of the character')
backstory: str = Field(description='character backstory')
abilities: list[str] = Field(description='list of abilities (3-4)')
@ai.flow()
async def generate_character(name: str) -> RpgCharacter:
result = await ai.generate(
prompt=f'Generate an RPG character named {name}',
output=Output(schema=RpgCharacter), # Use Output wrapper
)
# Returns an RpgCharacter instance, not dict!
return result.output
```
## Critical Fixes & Security
This release addresses several important issues:
- **Race Condition Fix**: Dev server startup race condition resolved (#4225)
- **Thread Safety**: Per-event-loop HTTP client caching prevents event loop binding errors
- **Security Audit**: Full Ruff security rules (S) audit completed
- **SigV4 Signing**: AWS X-Ray OTLP exporter now uses proper AWS signatures
## Developer Experience Improvements
### Hot Reloading
All samples now support hot reloading via [Watchdog](https://github.com/gorakhargosh/watchdog):
```bash
# Start with hot reloading
genkit start -- python main.py
```
### CI Consolidation
Every commit is now release-worthy. Our consolidated CI runs:
- All three type checkers
- Full test suite across Python 3.10-3.14
- Security scanning
- License compliance
- Package builds
### Rich Tracebacks
Better error output with [Rich](https://github.com/Textualize/rich) tracebacks in all samples.
## Available Plugins
### Model Providers
| Plugin | Models | Status |
|--------|--------|--------|
| `genkit-plugin-google-genai` | Gemini 2.5, Imagen, embeddings | ✅ Stable |
| `genkit-plugin-ollama` | Gemma, Llama, Mistral (local) | ✅ Stable |
| `genkit-plugin-anthropic` | Claude 3.5 Sonnet, Opus | ✅ New |
| `genkit-plugin-amazon-bedrock` | Claude, Titan, Llama via AWS | ✅ New |
| `genkit-plugin-microsoft-foundry` | Azure OpenAI | ✅ New |
| `genkit-plugin-cloudflare-workers-ai` | Cloudflare Workers AI | ✅ New |
| `genkit-plugin-mistral` | Mistral Large, Codestral | ✅ New |
| `genkit-plugin-huggingface` | 17+ providers | ✅ New |
| `genkit-plugin-deepseek` | DeepSeek models | ✅ New |
| `genkit-plugin-xai` | Grok models | ✅ New |
### Telemetry & Observability
| Plugin | Destination | Status |
|--------|-------------|--------|
| `genkit-plugin-google-cloud` | Cloud Trace/Logging | ✅ Stable |
| `genkit-plugin-aws` | AWS X-Ray | ✅ New |
| `genkit-plugin-observability` | Sentry, Honeycomb, Datadog | ✅ New |
| `genkit-plugin-firebase` | Firebase/Firestore | ✅ Stable |
### Data & Retrieval
| Plugin | Purpose | Status |
|--------|---------|--------|
| `genkit-plugin-firebase` | Vector search with Firestore | ✅ Stable |
| `genkit-plugin-dev-local-vectorstore` | Local vector store for dev | ✅ Stable |
## Get Started
Install Genkit Python SDK 0.5.0:
```bash
pip install genkit==0.5.0
```
Or with specific plugins:
```bash
pip install genkit[google-genai,anthropic,amazon-bedrock]==0.5.0
```
### Quick Start Example
```python
import asyncio
from genkit.ai import Genkit
from genkit.plugins.google_genai import GoogleAI
# Initialize at module level (best practice)
ai = Genkit(
plugins=[GoogleAI()],
model='googleai/gemini-2.5-flash',
)
@ai.flow()
async def greeting_flow(name: str) -> str:
"""Generates a personalized greeting."""
response = await ai.generate(
prompt=f'Write a creative greeting for {name}'
)
return response.text # Property, not method
async def main():
result = await greeting_flow('World')
print(result)
if __name__ == '__main__':
ai.run_main(main()) # Use ai.run_main() for proper lifecycle
```
Run with the Developer UI:
```bash
genkit start -- python main.py
```
## Contributors
This release was made possible by an incredible team effort. Thank you to all **13 contributors** who made this release possible:
| Contributor | Contributions |
|-------------|---------------|
| [@yesudeep](https://github.com/yesudeep) | Core architecture, 7 plugins, type safety, security |
| [@MengqinShen](https://github.com/MengqinShen) | Resources, samples, model configs |
| [@AbeJLazaro](https://github.com/AbeJLazaro) | Model Garden, Ollama, Gemini |
| [@pavelgj](https://github.com/pavelgj) | Reflection API, embedders |
| [@zarinn3pal](https://github.com/zarinn3pal) | Anthropic, DeepSeek, xAI, GCP telemetry |
| [@huangjeff5](https://github.com/huangjeff5) | PluginV2, type safety, telemetry |
| [@hendrixmar](https://github.com/hendrixmar) | Evaluators, OpenAI compat, Dotprompt |
| [@ssbushi](https://github.com/ssbushi) | Evaluator plugins |
| [@shrutip90](https://github.com/shrutip90) | ResourcePartSchema |
| [@schlich](https://github.com/schlich) | Type annotations |
| [@ktsmadhav](https://github.com/ktsmadhav) | Windows support |
| [@junhyukhan](https://github.com/junhyukhan) | Documentation |
| [@CorieW](https://github.com/CorieW) | Community contribution |
Special thanks to the [google/dotprompt](https://github.com/google/dotprompt) team for the deep integration work.
## What's Next?
We're committed to continuously evolving Genkit Python. Coming soon:
- **Session/Chat API**: Multi-turn conversation management
- **Reflection API v2**: WebSocket and JSON-RPC 2.0 support
- **More plugins**: Checks, Chroma, Pinecone, Cloud SQL PostgreSQL
- **Feature parity**: Continued alignment with JS/Go SDKs
## Get Involved
Got questions or feedback? Join us on:
- [Discord](https://discord.gg/qXt5zzQKpc)
- [Stack Overflow](https://stackoverflow.com/questions/tagged/genkit)
- [GitHub Issues](https://github.com/firebase/genkit/issues)
Explore the [full documentation](https://python.api.genkit.dev) and start building!
Happy coding, and we look forward to seeing what you create with Genkit Python 0.5.0!
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*Tags: Launch | Genkit | AI | Python*
