jasmine-cli 0.1.0

Jasmine: a compact terminal coding agent with streaming, precise patches, plans, context compaction, command approval, and token usage tracking.

Jasmine

Jasmine is a compact terminal coding agent with streaming Markdown, command approval, precise patches, live plans, background sessions, and API usage tracking.

Run

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
jasmine .

Offline smoke test:

jasmine --backend mock .

Model-Facing Tools

Jasmine exposes a compact tool surface:

• exec_command: inspect code with rg, sed, and Git; run focused checks.
• write_stdin: interact with a background shell or PTY session.
• terminal_screen: inspect a model-controlled PTY screen and cursor.
• terminal_close: close an interactive PTY and its child processes.
• apply_patch: edit files with simple exact replacements or advanced patches.
• view_image: inspect local images and attach a thumbnail for vision-capable providers.
• web_search: search the web for current information or docs.
• web_extract: extract readable text and structured data from a web page.
• list_skills: discover reusable workspace and built-in workflows.
• read_skill: load one relevant workflow without scanning unrelated guidance.
• ask_user: ask for a missing decision instead of guessing.
• multi_tool_use_parallel: batch 1-4 independent read-only inspections, including web and image reads.
• update_plan: keep one short visible implementation plan.

Codebase search uses focused rg commands through exec_command. Jasmine does not maintain a noisy pseudo-database or expose overlapping search wrappers.

Skills

Jasmine ships with reusable skills under jasmine/skills/. A workspace can add or override skills with skills/<name>/SKILL.md or .jasmine/skills/<name>/SKILL.md. The model lists skills only when specialized work may benefit from one, then reads the matching skill by name.

Context And Tokens

• Tool calls use the provider-native assistant/tool protocol with tool_call_id.
• Repeated read-only shell commands are reused within a turn.
• Identical tool calls are stopped after two attempts.
• Shell output enters model context unchanged. A complete cat file remains a complete file read in conversation history.
• Conversation history is preserved normally without automatic compaction.
• /usage shows API request, prompt, completion, reasoning, and cache-hit token totals.
• Large pasted prompts are preserved by default. --paste-threshold can opt in to saving large pastes under .jasmine/pastes/.
• Large user messages keep their full model context while the terminal shows a short [Pasted Content N chars] preview.

Commands

• /menu: show the menu.
• /tools: show the model-facing tools.
• /provider: switch provider from the configured-provider menu.
• /resume: load a saved conversation and replay its visible transcript.
• /restart: clear and rebuild the UI without losing the current conversation.
• /usage: show API token usage.
• /trusted: show remembered shell prefixes.
• /clear: clear chat context.
• /cd <path>: switch workspace.
• /exit: quit.

Runtime Modes

• Default DeepSeek mode: thinking enabled with xhigh reasoning effort.
• --fast: keep DeepSeek thinking enabled with high reasoning effort.
• --quality: enable DeepSeek thinking mode with xhigh reasoning effort.
• --model, --reasoning-effort, and --thinking: explicit overrides.

Providers

OpenAI-compatible providers live in .jasmine/providers/<name>.toml for a workspace or ~/.jasmine/providers/<name>.toml for all workspaces. A workspace file takes precedence. Select one with --backend <name> or switch at runtime with /provider.

base_url = "https://api.deepseek.com"
model = "deepseek-v4-pro"
api_key = "..."
supports_thinking = true
thinking_default = true
reasoning_effort = "max"
input_price_per_million = 0.435
cached_input_price_per_million = 0.003625
output_price_per_million = 0.87

Pricing fields are optional. When present, /usage includes the estimated API cost.

Architecture

jasmine/
├── main.py           # CLI entry point, argument parsing, command dispatch
├── agent.py          # Model loop: streaming, tool dispatch, nudge logic
├── ai.py             # AIBackend protocol + MockBackend + loader
├── openai_backend.py # Generic OpenAI-compatible streaming backend
├── deepseek_backend.py # DeepSeek-specific defaults (extends OpenAIBackend)
├── providers.py      # Multi-provider TOML config loading
├── tools.py          # ToolRegistry + all built-in tool implementations
├── ui.py             # TerminalUI: Rich rendering, prompt_toolkit input
├── terminal.py       # PTY management, command capture, keyboard encoding
├── config.py         # .jasmine.toml workspace config + theme definitions
├── conversation.py   # Auto-save/load/resume of agent conversations
├── diff_utils.py     # Unified diff application and generation
├── prompts.py        # System prompt (Ultra Mode instruction set)
└── skills/           # Built-in reusable skill workflows

Code Style

• 120-char lines, from __future__ import annotations in every file, type hints on public methods.

System prompt

The system prompt lives in jasmine/prompts.py. It is a single continuous Markdown string sent as the first message of every conversation. Providers that support prompt caching (DeepSeek, Anthropic) will automatically cache repeated prefix content — keep structural changes minimal and prefer appending instructions at the end when possible.