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z

An agentic coding harness I built because I got tired of context-window anxiety and provider lock-in.

I use my own API keys. GLM 4.7, GLM 5.2, DeepSeek V4 Flash, DeepSeek V4 Pro — hot-swap mid-session with /providers use. No subscriptions, no rate limits I didn't sign up for.

What makes it different

Constant streaming. Every token streams to the terminal as it arrives. Thinking/reasoning content visible in real time — nothing hidden, no "loading..." spinners. Background tool output streams transparently too.

Telegram remote. Fire up z --telegram <token> and drive your agent from your phone. Walking the dog, taking a dump, whatever — you can still ship.

Context survives compaction. Half, quarter, lastTwo strategies with breadcrumbs. Todos persist. Tool results are cached for retrieval. If z already read it, it doesn't re-read it.

26 providers, one z. Z.AI, DeepSeek, Anthropic, OpenAI, Ollama, Bedrock, Copilot OAuth — all through LiteLLM. Normalizes model names automatically.

Quick start

pip install -e ".[dev]"
z --install
z

Config lives in ~/.z.json:

{ "api_url": "https://api.z.ai/api/paas/v4/", "api_key": "sk-...", "model": "glm-4.7" }

Telegram:

z --telegram <bot-token> --telegram-username your_username

Commands

Command What
/providers use <name> Switch models mid-session
/compact Force context compaction
/cost Token usage and spend
/todo Todo panel
!cmd Run shell command

19 built-in tools: file ops, shell, search, web search, image analysis, MCP, background processes, sub-agents.

Borrows Claude Code semantics where they make sense — CLAUDE.md, hooks (PreToolUse, PostToolUse), persistent memory, scheduled tasks (cron).

Plugins: drop a .py in ~/.z/plugins/.

MIT

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Agentic harness optimized for z.ai GLM models

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