Llmswap v3.0 – CLI and SDK for OpenAI, Claude, Gemini, Watsonx

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LLMSwap is a CLI and Python SDK for switching between AI providers (OpenAI, Claude, Gemini, IBM watsonx, Ollama) with automatic fallbacks and response caching. Started this during a hackathon when constantly switching between Claude for code review and GPT-4 for debugging. Got tired of managing different API clients and keys. After the hackathon, kept building on it. Added CLI when I wanted to pipe error logs directly to AI for analysis. Added caching when I realized I was testing the same prompts repeatedly during development and burning API credits. Added code review features when our team started using it in CI/CD. New in v3.0 - Professional CLI: 1. llmswap ask "Debug this SQL query performance issue" 2. llmswap review app.py --focus security 3. llmswap chat # Interactive AI sessions 4. llmswap debug --error "ConnectionTimeout in production" Python SDK features: 1. Multi-provider support (OpenAI, Claude, Gemini, IBM watsonx, Ollama) 2. Response caching for cost reduction (great for development/testing) 3. Auto-fallback when providers fail 4. Async support with streaming 5. Thread-safe for production apps Real-world use cases: 1. Developers: Code review, debugging, documentation 2. Content teams: Writing, analysis, research 3. Startups: Prototype quickly, switch providers based on cost/features 4. Students: Free local models (Ollama) + cloud when needed 5. Enterprise: IBM watsonx integration for regulated environments Three versions later, it's become our go-to tool for anything AI-related in development workflows. The CLI works great in automation - we use it for automated code review and log analysis in CI/CD. Technical notes: 1. Zero dependencies for basic CLI usage 2. Works offline with local models (Ollama) 3. Production-ready with error handling and retries 4. Full backward compatibility from v1.0 Real-world adoption: 5k+ PyPI downloads Install: pip install llmswap

Visit product View on Hacker NewsAug 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, apps · Missing: mac, agents, macos

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, code review, ide · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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