agenta-fastapi-integration

Sub-skill of agenta: FastAPI Integration.

5 stars

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

$curl -o ~/.claude/skills/fastapi-integration/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/ai/prompting/agenta/fastapi-integration/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/fastapi-integration/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How agenta-fastapi-integration Compares

Feature / Agentagenta-fastapi-integrationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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