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gptme

gptme

Operate the gptme terminal AI agent, provider configuration, tools, server/Web UI, protocols, evals, and repository-maintenance workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: gptme description: "Operate the gptme terminal AI agent, provider configuration, tools, server/Web UI, protocols, evals, and repository-maintenance workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

gptme Repo Skill

Use this skill when a task names gptme, gptme-server, gptme-agent, gptme-acp, gptme-eval, gptme tools/plugins/hooks/MCP, gptme provider configuration, gptme Web UI/API, or maintenance of the gptme repository.

gptme is a terminal-first local AI agent package with a chat CLI, persistent conversations, tool execution, provider-agnostic LLM routing, browser/computer/MCP/extensibility surfaces, a REST/SSE server plus Web UI, ACP/TUI interfaces, benchmark/eval tooling, and a Python/Web UI source repository.

First checks

For installed-package tasks, verify the target runtime before making claims:

python scripts/check_gptme_environment.py --check-server-app
python scripts/run_gptme_help_matrix.py

These generated helpers only perform import, entrypoint, help, and route-registration checks. They do not start a chat, server, browser, Docker container, eval run, or model call.

For maintainer tasks, first identify the target gptme checkout and read sub-skills/repo-development/SKILL.md. Checkout-maintainer commands are scoped to that checkout; generated helper scripts live in this skill tree and accept explicit target paths where needed.

Install and runtime orientation

Read references/installation-and-entrypoints.md when the task asks how to install gptme, choose extras, or locate an entry point. Core facts:

  • Distribution/import root: gptme.
  • Verified version baseline: 0.32.1.
  • Python metadata: >=3.10.
  • Common isolated installs: pipx install gptme or uv tool install gptme.
  • Use focused extras: [server], [browser], [acp], [tui], [telemetry], or [eval] only when that workflow needs them.
  • Do not install [all], browser binaries, Node dependencies, Docker images, or system services unless the task explicitly requires them.

Read references/package-architecture.md when you need source/module ownership, verified public signatures, or routing by package area. Read references/troubleshooting.md for cross-cutting install/import/optional-extra/provider/server/eval failures before drilling into a sub-skill.

Read references/repo-provenance.md before trusting exact signatures or maintainer commands in a different checkout. If commit, metadata, entry points, major docs, server API, tool APIs, eval schemas, or maintainer policy changed, run refresh-repo-skill.

Route by task

Task signalRead
Run gptme from terminal, build a command, manage named/resumed/forked conversations, inspect logs, use slash commands, queue prompts, automate non-interactively, or use gptme-agent.sub-skills/cli-and-conversations/SKILL.md
Configure global/project/chat files, API keys, /account, gptme-auth, default model priority, provider prefixes, local/Ollama models, OpenRouter privacy/routing, or custom provider plugins.sub-skills/configuration-and-providers/SKILL.md
Choose built-in tools, debug tool allowlists/formats, create custom ToolSpec tools, package plugins, register hooks/commands, configure MCP, or decide between plugins, skills, and lessons.sub-skills/tools-and-extensibility/SKILL.md
Start or deploy gptme-server, operate the Web UI, call REST/SSE APIs, handle tokens/CORS/Host validation, use ACP/TUI, or troubleshoot hosted/local Web UI connectivity.sub-skills/server-webui-and-protocols/SKILL.md
Build gptme-eval commands, list suites, run or avoid model-costly benchmarks, summarize eval_results.csv, process leaderboard/trends, or use SWE-bench/T-bench integrations.sub-skills/evals-and-benchmarks/SKILL.md
Modify the gptme source repo, select focused pytest/Web UI commands, follow branch/commit/staging policy, update docs, validate packages, or handle release/Web UI development.sub-skills/repo-development/SKILL.md

Safety boundaries

  • Do not make live LLM calls, OAuth/browser auth attempts, eval runs, Docker builds, or browser/computer actions unless the task explicitly asks for them and credentials/runtime are available.
  • Do not print raw API keys, OAuth tokens, server bearer tokens, local credential file contents, or private paths.
  • Treat browser, computer-use, Docker, Node/Web UI, provider API, and full eval workflows as optional/external runtime surfaces.
  • For server deployment beyond loopback, require an explicit auth, CORS/Host, TLS/proxy, and exposure plan.
  • For repository maintenance, never push directly to master, stage files explicitly, and keep the change/test scope narrow.

Router metadata

references/repo-routing-metadata.json contains structured scenario metadata for managed repo-skill import. This creation run was requested with not import, so no managed import has been attempted.

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