agenta-fastapi-integration
Sub-skill of agenta: FastAPI Integration.
Best use case
agenta-fastapi-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of agenta: FastAPI Integration.
Teams using agenta-fastapi-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fastapi-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agenta-fastapi-integration Compares
| Feature / Agent | agenta-fastapi-integration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of agenta: FastAPI Integration.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# FastAPI Integration
## FastAPI Integration
```python
"""
Integrate Agenta with FastAPI for production deployments.
"""
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional
import agenta as ag
from agenta import Agenta
app = FastAPI(title="Agenta-Powered API")
# Initialize Agenta
ag.init()
client = Agenta()
class QueryRequest(BaseModel):
"""Request model for queries."""
input: str
variant: Optional[str] = None
parameters: Optional[dict] = None
class QueryResponse(BaseModel):
"""Response model."""
output: str
variant_used: str
latency: float
@app.post("/generate", response_model=QueryResponse)
async def generate(request: QueryRequest):
"""Generate response using Agenta-managed prompts."""
import time
try:
# Get variant (default or specified)
if request.variant:
variant = client.get_variant_by_name(
app_name="production-app",
variant_name=request.variant
)
else:
variant = client.get_default_variant(app_name="production-app")
# Get prompt template
template = variant.config.get("template", "{input}")
prompt = template.format(input=request.input)
# Get parameters
params = variant.config.get("parameters", {})
if request.parameters:
params.update(request.parameters)
# Generate
start_time = time.time()
response = ag.llm.complete(prompt=prompt, **params)
latency = time.time() - start_time
return QueryResponse(
output=response.text,
variant_used=variant.name,
latency=latency
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/variants")
async def list_variants():
"""List available variants."""
variants = client.list_variants(app_name="production-app")
return [{"name": v.name, "id": v.id, "is_default": v.is_default} for v in variants]
# Run with: uvicorn api:app --reload
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