Show HN: Clai – Context engineering for terminal powerusers

Developer baalimago released Clai, a command-line context feeder for AI tasks that supports multiple LLM vendors and MCP servers. The tool follows Unix philosophy, enabling piped data workflows, conversation management, and customizable profiles for power users.

Test coverage: 68.860% 😌👏 clai /klaɪ/ , like "cli" in " cli mate" is a command line context-feeder for any ai task. Installing: curl -fsSL https://raw.githubusercontent.com/baalimago/clai/main/setup.sh | sh You can also install via go: go install github.com/baalimago/clai@latest Then run: clai help | clai query Please give a concise explanation of clai Either look at clai help or the examples /baalimago/clai/blob/main/EXAMPLES.md for how to use clai . If you have time, you can also check out this blogpost https://lorentz.app/blog-item.html?id=clai for a slightly more structured introduction on how to use Clai efficiently. Install Glow https://github.com/charmbracelet/glow for formatted markdown output when querying text responses. - Add any MCP server you'd like by simply pasting their configuration. MCP client support /baalimago/clai/blob/main/EXAMPLES.md mcp-tools-external-tool-servers Vendor agnosticism - Use any functionality in Clai with most LLM vendors supported-vendors interchangeably.- Create, manage and continue conversations. Conversations /baalimago/clai/blob/main/EXAMPLES.md bind-a-previous-conversation-to-the-current-directory - Pre-prompted profiles enabling customized workflows and agents. Profiles /baalimago/clai/blob/main/EXAMPLES.md profiles--workflow-presets Unix-like - Clai follows the unix philosophy https://en.wikipedia.org/wiki/Unix philosophy and works seamlessly with data piped in and out. All of these features are easily combined and tweaked, empowering users to accomplish very diverse use cases. See examples /baalimago/clai/blob/main/EXAMPLES.md for additional info. | Vendor | Environment Variable | Models | |---|---|---| | Berget AI | BERGET API KEY | berget: | OPENROUTER API KEY Text models https://openrouter.ai/models , use prefix or: MISTRAL API KEY Text models https://docs.mistral.ai/getting-started/models/ OPENAI API KEY Text models https://platform.openai.com/docs/models , photo models https://platform.openai.com/docs/models/dall-e ANTHROPIC API KEY Text models https://platform.claude.com/docs/en/about-claude/models/overview GEMINI API KEY Text models https://ai.google.dev/gemini-api/docs/models , photo models https://ai.google.dev/gemini-api/docs/image-generation image generation text-to-image HF API KEY Text models https://huggingface.co/docs/inference-endpoints/chat-completions , use prefix hf: XAI API KEY Text models https://docs.x.ai/docs/models INCEPTION API KEY Text models https://platform.inceptionlabs.ai/docs models ollama: defaults to llama3 , server defaults to localhost:11434Shell completions are also available: bash mkdir -p ~/.local/share/bash-completion/completions clai completion bash ~/.local/share/bash-completion/completions/clai zsh mkdir -p ~/.zsh/completions clai completion zsh ~/.zsh/completions/ clai For zsh, ensure your fpath includes the completions directory before compinit , for example: fpath= ~/.zsh/completions $fpath autoload -Uz compinit && compinit Then restart your shell or reload your shell configuration.