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General LLM tools include frameworks, APIs, evaluation utilities, and infrastructure specifically built around large language models. This category covers 369 tools that help developers build, test, fine-tune, and run applications on top of LLMs, from embedding calculators to full model-serving platforms.
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Create short videos from text with AI
Free video chat with random people
Free AI blog writing tool
AI motion generation for characters
Generate talking head videos from prompts
AI tool that generates text descriptions, captions, and prompts from images
Create custom sound effects from text descriptions
Convert AI-generated text to read as human-written
Generate 3D character animations from text descriptions
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Convert text to audio with natural-sounding voices
Study complex topics through your favorite characters and memes
Rewrite text in Spanish and remove plagiarism
Dictate and auto-format text 9x faster across any app
Voice-to-text that understands technical terms and jargon
Convert AI-written text to natural, human-sounding writing
Rewrite AI text to bypass detection tools
Web search and semantic rerank API for LLM applications
Free online notepad for quick note-taking and organization
Convert images to detailed text prompts instantly
Image generation with Flux.1 models and LoRA support
Bypass AI detection with one click
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This is a technically focused category aimed primarily at developers and ML engineers. Tools like Cognee and UpTrain address retrieval-augmented generation (RAG) pipelines and model evaluation, while platforms like Dstack and PeriFlow handle compute infrastructure for training and inference. Embedding Similarity Calculator and similar utilities fill narrow but useful gaps in LLM development workflows. OpenAssistant and Cerebras-GPT represent open-source or open-weight models that developers can run or fine-tune directly. When comparing options, consider whether a tool is model-agnostic or tied to a specific provider, and whether it supports the models you are already using. Latency, throughput, and cost-per-token are the metrics that matter most for production workloads. Evaluation tools are often underinvested in early projects but become critical once you are shipping to users. Many tools here are open source with paid managed versions, while others are closed SaaS products with usage-based pricing.