Complete AI CLI Suite + Python SDK. 5 Terminal AI Tools: ask, chat, review, debug, logs. Multi-provider LLM interface supporting OpenAI GPT-4, Claude, Gemini, IBM WatsonX, Ollama. Built for developers, DevOps, students. Features AI CLI debugger, terminal code review, log analysis. 50-90% cost savings through intelligent caching.
Two Powerful Interfaces: 5 AI CLI Tools + Complete Python SDK
# Install once, get 5 AI CLI tools
pip install llmswap
# 1. One-line AI assistant
llmswap ask "How to optimize PostgreSQL queries?"
# 2. Interactive AI chat
llmswap chat
# 3. AI code reviewer
llmswap review app.py --focus security
# 4. AI debugger
llmswap debug --error "ConnectionTimeout: Connection timed out"
# 5. AI log analyzer
llmswap logs --analyze /var/log/app.log --since "2h ago"
pip install llmswap
from llmswap import LLMClient
client = LLMClient() # Auto-detects OpenAI, Claude, Gemini, etc.
response = client.query("Analyze this data trend")
print(response.content)
Complete AI-powered development workflow in your terminal + Python library for applications
# Install once, use everywhere
pip install llmswap
# Universal AI workflows for any use case
llmswap ask "Write a professional email response"
llmswap ask "Analyze this sales data trend"
llmswap ask "Explain quantum physics simply"
llmswap review myfile.py --focus security
llmswap debug --error "ConnectionError: timeout"
llmswap chat # Interactive AI assistant
Built from hackathon experience to help developers ship faster:
# Perfect hackathon starter - works with any API key you have
from llmswap import LLMClient
client = LLMClient(cache_enabled=True) # Save money from day 1
response = client.query("Help me build an AI-powered app")
print(response.content)
pip install llmswap
# Choose your provider (any one is enough)
export ANTHROPIC_API_KEY="your-anthropic-key"
export OPENAI_API_KEY="your-openai-key"
export GEMINI_API_KEY="your-gemini-key"
# Ask questions
llmswap ask "What is Docker?"
llmswap ask "How to optimize SQL queries?" --provider openai
# Interactive chat
llmswap chat
# Code review with AI
llmswap review app.py --focus security
llmswap review script.js --focus performance
llmswap review main.go --focus bugs
# Debug assistance
llmswap debug --error "TypeError: 'NoneType' object is not callable"
llmswap debug --error "ECONNREFUSED: Connection refused"
# Get help
llmswap --help
llmswap review --help
# DevOps workflows
llmswap ask "Why is my Node.js app using too much memory?"
llmswap debug --error "Error: listen EADDRINUSE: address already in use :::3000"
# Code review automation
find . -name "*.py" -exec llmswap review {} --focus security --quiet \;
# Daily development
llmswap ask "Best practices for REST API design"
llmswap review pull_request.diff --focus style
pip install llmswap
from llmswap import LLMClient
client = LLMClient()
response = client.query("What is Python?")
print(response.content)
# Choose your provider
export ANTHROPIC_API_KEY="your-key-here"
# OR
export OPENAI_API_KEY="your-key-here"
# OR
export GEMINI_API_KEY="your-key-here"
# OR
export WATSONX_API_KEY="your-ibm-api-key"
export WATSONX_PROJECT_ID="your-project-id"
# OR run Ollama locally
import asyncio
from llmswap import AsyncLLMClient
async def main():
client = AsyncLLMClient(provider="openai")
# Async query
response = await client.query("Explain quantum computing")
print(response.content)
# Streaming response
print("Streaming: ", end="")
async for chunk in client.stream("Write a haiku"):
print(chunk, end="", flush=True)
asyncio.run(main())
What is Response Caching?
Intelligent caching stores LLM responses temporarily to avoid repeated expensive API calls for identical queries.
Default State: DISABLED (for security in multi-user environments)
Key Advantages:
from llmswap import LLMClient
# Step 1: Enable caching (disabled by default)
client = LLMClient(cache_enabled=True)
# First call: hits API ($$$)
response = client.query("What is machine learning?")
print(f"From cache: {response.from_cache}") # False
# Identical call: returns from cache (FREE!)
response = client.query("What is machine learning?")
print(f"From cache: {response.from_cache}") # True
# Customize cache behavior
client = LLMClient(
cache_enabled=True,
cache_ttl=3600, # 1 hour expiry
cache_max_size_mb=50 # Memory limit
)
# Multi-user security: separate cache per user
response = client.query(
"Show my account balance",
cache_context={"user_id": "user123"}
)
# Per-query settings
response = client.query(
"Current weather",
cache_ttl=300, # 5 minutes for weather
cache_bypass=True # Force fresh API call
)
# Monitor performance
stats = client.get_cache_stats()
print(f"Hit rate: {stats['hit_rate']}%")
print(f"Cost savings: ~{stats['hit_rate']}%")
# WRONG: Shared cache (security risk)
client = LLMClient(cache_enabled=True)
client.query("Show my private data") # User A
client.query("Show my private data") # User B gets User A's data!
# RIGHT: Context-aware caching
client.query("Show my private data", cache_context={"user_id": current_user.id})
When to Use Caching:
When NOT to Use:
from llmswap import AsyncLLMClient
# Enable logging to file
client = AsyncLLMClient(
provider="anthropic",
log_file="/tmp/requests.log",
log_level="info"
)
# All requests and responses are logged with metadata
response = await client.query("Hello world")
# Logs include: timestamp, provider, model, latency, token counts
from llmswap import LLMClient
client = LLMClient()
print(f"Using: {client.get_current_provider()}")
response = client.query("Explain machine learning")
print(response.content)
client = LLMClient(provider="anthropic")
client = LLMClient(provider="openai")
client = LLMClient(provider="gemini")
client = LLMClient(provider="ollama")
client = LLMClient(provider="anthropic", model="claude-3-opus-20240229")
client = LLMClient(provider="openai", model="gpt-4")
client = LLMClient(provider="gemini", model="gemini-1.5-pro")
client = LLMClient(provider="anthropic")
client.set_provider("openai")
client.set_provider("gemini", model="gemini-1.5-flash")
response = client.query("What is OpenStack?")
print(f"Content: {response.content}")
print(f"Provider: {response.provider}")
print(f"Model: {response.model}")
print(f"Latency: {response.latency:.2f}s")
client = LLMClient(fallback=True)
response = client.query("Hello world")
print(f"Succeeded with: {response.provider}")
| Provider | Models | Setup |
|---|---|---|
| Anthropic | Claude 3 (Sonnet, Haiku, Opus) | export ANTHROPIC_API_KEY=... |
| OpenAI | GPT-3.5, GPT-4, GPT-4o | export OPENAI_API_KEY=... |
| Gemini 1.5 (Flash, Pro) | export GEMINI_API_KEY=... | |
| Ollama | 100+ local models (see below) | Run Ollama locally |
| IBM watsonx | Granite, Llama, and foundation models | export WATSONX_API_KEY=... |
OpenAI's new open-source models are now supported via Ollama:
# Pull the model first: ollama pull gpt-oss-20b
client = LLMClient(provider="ollama", model="gpt-oss-20b")
client = LLMClient(provider="ollama", model="gpt-oss-120b")
# Run reasoning tasks locally
response = client.query("Solve this step by step: What is 47 * 23?")
GPT-OSS (OpenAI Open-Weight)
gpt-oss-20b - Efficient 20B reasoning model (16GB RAM)gpt-oss-120b - Advanced 120B model (80GB VRAM)Llama Family
llama3.2 (1B, 3B, 8B, 70B, 90B)llama3.1 (8B, 70B, 405B)llava-llama3 (Vision + Language)Mistral Models
mistral (7B)mistral-nemo (12B)mistral-small (22B)codestral (22B - Code specialist)Google Gemma
gemma2 (2B, 9B, 27B)gemma3 (Latest from Google)Qwen Series
qwen2.5 (0.5B, 1.5B, 3B, 7B, 14B, 32B)qwen2.5-coder (Code specialist)qwq (32B - Reasoning model)Microsoft Phi
phi3 (3.8B - Efficient small model)phi4 (14B - Advanced reasoning)Other Popular Models
granite-code (IBM - Code generation)deepseek-coder (Code specialist)zephyr (Assistant fine-tuned)smollm2 (135M, 360M, 1.7B)# Any Ollama model works out of the box
client = LLMClient(provider="ollama", model="llama3.2")
client = LLMClient(provider="ollama", model="mistral-nemo")
client = LLMClient(provider="ollama", model="qwen2.5-coder")
client = LLMClient(provider="ollama", model="phi4")
# Check what models you have locally
# ollama list
# Pull new models
# ollama pull mistral-nemo
# ollama pull gpt-oss-20b
Enterprise-grade AI with IBM's foundation models:
# Set environment variables
# export WATSONX_API_KEY="your-ibm-cloud-api-key"
# export WATSONX_PROJECT_ID="your-project-id"
client = LLMClient(provider="watsonx")
client = LLMClient(provider="watsonx", model="ibm/granite-3-8b-instruct")
# Popular watsonx models
client = LLMClient(provider="watsonx", model="ibm/granite-3-8b-instruct")
client = LLMClient(provider="watsonx", model="meta-llama/llama-3-70b-instruct")
client = LLMClient(provider="watsonx", model="mistralai/mixtral-8x7b-instruct-v01")
response = client.query("Analyze this business data and provide insights")
The package includes ready-to-use examples and CLI tools for common developer workflows:
cli_assistant.py - Full-featured CLI with interactive mode and commandscode_reviewer.py - AI-powered code review with focus areas (bugs, security, style)debug_helper.py - Error analysis, stack trace interpretation, debugging strategiesprovider_comparison.py - Compare responses from different LLM providerssmart_cost_optimizer.py - Demonstrates cost savings through caching (Python SDK)quick_chat.py - Minimal chat interface showing llmswap simplicityask - One-liner CLI script for quick questionsbasic_usage.py - Simple integration exampleshackathon_starter.py - Perfect starting point for hackathons and student projects# One-liner questions
./examples/ask "What is Python?"
./examples/ask "Explain machine learning"
# Interactive chat
python examples/cli_assistant.py
# Code review
python examples/code_reviewer.py myfile.py --focus security
python examples/code_reviewer.py --language javascript --focus bugs < script.js
# Debug assistance
python examples/debug_helper.py --error "IndexError: list index out of range"
python examples/debug_helper.py --stack-trace "$(cat error.log)"
# Provider comparison
python examples/provider_comparison.py
# Cost optimization demo
python examples/smart_cost_optimizer.py
from llmswap import LLMClient
class SimpleChatbot:
def __init__(self):
self.llm = LLMClient()
def chat(self, message):
response = self.llm.query(f"User: {message}\nAssistant:")
return response.content
def get_provider(self):
return f"Using {self.llm.get_current_provider()}"
# Usage
bot = SimpleChatbot()
print(bot.chat("Hello!"))
print(bot.get_provider())
# BEFORE: Direct provider usage
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello"}]
)
content = response.choices[0].message.content
# AFTER: llmswap (works with any provider!)
from llmswap import LLMClient
client = LLMClient()
response = client.query("Hello")
content = response.content
# API Keys (set at least one)
export ANTHROPIC_API_KEY="your-anthropic-key"
export OPENAI_API_KEY="your-openai-key"
export GEMINI_API_KEY="your-gemini-key"
# IBM watsonx (enterprise models)
export WATSONX_API_KEY="your-ibm-cloud-api-key"
export WATSONX_PROJECT_ID="your-watsonx-project-id"
# Ollama (if using local models)
export OLLAMA_URL="http://localhost:11434" # default
# With API key
client = LLMClient(
provider="anthropic",
api_key="your-key-here"
)
# With custom model
client = LLMClient(
provider="openai",
model="gpt-4-turbo-preview"
)
# Disable fallback
client = LLMClient(fallback=False)
client = LLMClient()
# List configured providers
available = client.list_available_providers()
print(f"Available: {available}")
# Check specific provider
if client.is_provider_available("anthropic"):
client.set_provider("anthropic")
MIT License - see LICENSE file for details.
Star this repo if llmswap helps simplify your language model integration.
FAQs
Complete AI CLI Suite + Python SDK. 5 Terminal AI Tools: ask, chat, review, debug, logs. Multi-provider LLM interface supporting OpenAI GPT-4, Claude, Gemini, IBM WatsonX, Ollama. Built for developers, DevOps, students. Features AI CLI debugger, terminal code review, log analysis. 50-90% cost savings through intelligent caching.
We found that llmswap demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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