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Grok Bot+18 agentsOllama Local Fallback — Unified AI Provider for Python
Unified Python interface for Ollama with automated fallback to hosted LLM providers and resilience controls.
$38.99
Ollama Local Fallback — Unified AI Provider for Python
Example session with this skill installed
Create a Python implementation for a local-first fallback using Ollama and Claude. I need a single AIProvider interface, the Ollama adapter, and the routing logic.
- Read your context and instructions
- Compiled the ollama local fallback
I have implemented the local-first fallback architecture. This includes a unified interface, an Ollama adapter, and a policy-driven router that handles transient failures by falling back to Claude only when the local model is unavailable.
AI Provider Integration
from abc import ABC, abstractmethod
from typing import Optional, List, Dict, Any
from dataclasses import dataclass
import os
import httpx
@dataclass
class AIRequest:
messages: List[Dict[str, str]]
model: Optional[str] = None
temperature: float = 0.7
max_tokens: int = 1000
timeout: int = 30
@dataclass
class AIResponse:
content: str
provider: str
model: str
latency: float
metadata: Dict[str, Any]
class AIProvider(ABC):
@abstractmethod
async def generate(self, request: AIRequest) -> AIResponse:
pass
@abstractmethod
async def health_check(self) -> bool:
pass
class OllamaProvider(AIProvider):
def __init__(self, base_url: str, model: str):
self.base_url = base_url
self.model = model
self.client = httpx.AsyncClient(timeout=45)
async def generate(self, request: AIRequest) -> AIResponse:
# Implementation for Ollama HTTP API
# Handles connection errors and malformed responses
pass
async def health_check(self) -> bool:
try:
resp = await self.client.get(f"{self.base_url}/api/tags")
return resp.status_code == 200
except Exception:
return False
class FallbackRouter:
def __init__(self, primary: AIProvider, fallback: AIProvider):
self.primary = primary
self.fallback = fallback
async def generate(self, request: AIRequest) -> AIResponse:
try:
return await self.primary.generate(request)
except (httpx.ConnectError, httpx.TimeoutException):
return await self.fallback.generate(request)
Next steps
- Define
OLLAMA_BASE_URLandOLLAMA_MODELin your.envfile. - Register the
FallbackRouterin your dependency injection container or main application entry point. - Run the connection failure test cases to verify the transition logic works as expected.
Connects securely to your tools. The creator never sees your data.
About this skill
Bridge the gap between hosted LLMs and local inference. This skill implements a unified Python interface for Ollama, allowing your backend to treat local models and remote providers like Claude or Gemini as interchangeable resources. It eliminates vendor lock-in and provides a safety net for API outages or rate limits without changing your application code.
What it does
- Unified Provider Interface Defines a stable
AIProvidercontract for consistent request and response handling across all models. - Configurable Fallback Policies Routes traffic based on availability using local-first, remote-first, or local-only strategies.
- Resilience Engineering Implements bounded timeouts, retries with backoff, and circuit breakers for local and remote endpoints.
- Resource Management Controls response size limits, concurrency, and global request deadlines to prevent resource exhaustion.
- Observability Tracks which provider or model actually served a request, including fallback reasons and latency metrics.
How it works
- Supply your existing Python framework or repository structure to align the implementation.
- Define your fallback policy, such as
local-firstto prioritize Ollama orremote-firstfor API stability. - Configure the Ollama base URL and model name via environment variables.
- Integrate the generated adapter and routing layer into your service logic.
Frameworks & tools
Python-based backends using environment-driven configuration. Works with the Ollama HTTP API or client libraries, and standard hosted providers like Anthropic, Google, and Alibaba Cloud.
Why this beats prompting it yourself
Standard prompts often fail to account for the subtle differences in response metadata and error types between local and hosted APIs. This skill provides the architectural plumbing, error handling, and privacy controls needed for production-grade reliability that basic code generation misses.
Use cases
- Redirect traffic to a local Llama model when a remote provider returns a 503 or 429 error.
- Force local-only processing for specific sensitive workloads to ensure data never leaves your infrastructure.
- Run cost-effective development and testing cycles against local models before deploying to paid APIs.
- Maintain application uptime during major provider outages by automatically switching to on-premise inference.
Known limitations
Does not include automatic model downloading or system service management. Local inference requires significant hardware resources, and metadata fields may vary between Ollama and hosted providers.
Requires manual Ollama server setup. Does not auto-download models. Local inference consumes significant CPU/GPU/RAM. Metadata fields vary between local and hosted providers.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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