meshapi-code 0.5.1

Terminal chat for Mesh API — OpenAI-compatible LLM gateway

meshapi-code

Agentic terminal CLI for Mesh API — one OpenAI-compatible key, 300+ models. Plans, writes files, runs commands, starts dev servers, searches the web — with streaming markdown, live cost, and permission modes. Modeled on Claude Code.

$ meshapi
███╗   ███╗███████╗███████╗██╗  ██╗
████╗ ████║██╔════╝██╔════╝██║  ██║
██╔████╔██║█████╗  ███████╗███████║   ✦  meshapi 0.5.1
██║╚██╔╝██║██╔══╝  ╚════██║██╔══██║   cwd:   ~/code/myproj
██║ ╚═╝ ██║███████╗███████║██║  ██║   model: anthropic/claude-sonnet-4.5
╚═╝     ╚═╝╚══════╝╚══════╝╚═╝  ╚═╝   route: off

type /help for commands, /exit to quit

──────────────────────────────────────────────────── myproj · main
› add a healthcheck endpoint to server.py and run the tests
──────────────────────────────────────────────────────────────────

✦ anthropic/claude-sonnet-4.5 · hop 2
⚙ write_file: server.py (+14 −2)      ✓ OK
⚙ run_bash: pytest -q                 ✓ exit 0
   anthropic/claude-sonnet-4.5  •  942→318 tok  •  $0.001234  •  session $0.001234  •  6.1s

Install

macOS / Linux (Terminal):

brew install pipx && pipx ensurepath   # if you don't have pipx yet
pipx install meshapi-code
meshapi

Windows (PowerShell):

py -m pip install --user pipx
py -m pipx ensurepath                  # then open a NEW PowerShell window
pipx install meshapi-code
meshapi

Alternatives on any OS: uv tool install meshapi-code or pip install meshapi-code. PyPI package is meshapi-code; the command on your $PATH is meshapi (same split Claude Code uses: @anthropic-ai/claude-code → claude).

First run asks for your API key (get one at app.meshapi.ai) — hidden input, verified live, saved to ~/.meshapi/credentials. No environment variable needed. To set one anyway (CI, scripts):

# macOS / Linux
export MESHAPI_API_KEY=rsk_your_key_here

# Windows (PowerShell) — current session:
$env:MESHAPI_API_KEY = "rsk_your_key_here"
# persistent:
setx MESHAPI_API_KEY "rsk_your_key_here"

The CLI also checks PyPI in the background and offers one-key upgrades when a new version ships (/update checks on demand).

What it does

• Agentic tool calling — the model plans multi-step work, reads/writes files, runs shell commands, starts dev servers in the background (with port auto-detection), and searches the web. Every step gated by permission modes.
• Quality guard — stub code (// Add game logic here) is caught before the model declares victory: one automatic fix-it pass, then an honest warning naming the files and suggesting a stronger model. No more "Server's up!" over a blank page.
• Self-healing tool calls — malformed arguments are repaired client-side; the model never re-reads its own broken JSON. Ends the retry doom-loop, biggest win on cheaper models.
• Type while it works — the input stays live during streaming; Enter stacks messages that auto-run in order; ESC aborts a turn; unfinished text prefills the next prompt. (macOS/Linux; on Windows input is available between turns.)
• Fuzzy model picker — /model qw pops a menu of every qwen model; gpt4m finds openai/gpt-4o-mini. /models browses the catalog with context sizes and $/1M pricing.
• Auto-routing — /route auto lets Mesh's gateway pick the best model per prompt; the resolved model shows in the status line. /fallback m1 m2 sets an ordered failover list.
• Real cost per turn — Mesh returns cost in the SSE tail; surfaced after every reply and accumulated per session.
• Streaming with live status — markdown rendering, phase-aware spinner (preparing write_file (↓ 3.2k chars)), always-visible permission mode, background servers listed under the prompt.

Tool calling & permission modes

Tool	What it does
read_file	Read a file (image files are auto-attached instead).
write_file	Create or overwrite a file; parent dirs created; scanned by the quality guard.
run_bash	Shell command in the working directory. 120s timeout, output capped.
start_server	Long-running dev server in the background — detects the port in your command, adopts what it actually binds, shows progress, killed on exit.
web_search	Search the web through the Mesh gateway.
create_plan / update_step	The model's visible step-by-step plan.

Permission modes, cycled live with Shift+Tab (works mid-run on macOS/Linux):

• default — ask for every tool call
• accept edits — auto-approve file writes inside the working directory
• auto — plus shell commands and web searches
• bypass — auto-approve everything (still asks before rm -rf, sudo, writes to ~/.ssh, …)

At any approval prompt, answer a to allow that tool for the rest of the session.

meshapi --mode bypass     # start in bypass (macOS/Linux/Windows alike)

Slash commands

Command	What it does
/model <name>	Switch model — fuzzy tab-completion from the live catalog
/models [free|query]	Browse the catalog: context, capabilities, $/1M pricing
/route auto|off|preview	Gateway auto-routing; preview shows the pick without running
/fallback <m1> <m2>|off	Ordered fallback models if the primary fails
/reasoning <level>	high/medium/low/none/off reasoning effort
/mode <perm>	default, accept-edits, auto, bypass (Shift+Tab cycles)
/file <path>	Inject a text file into the conversation
/image <path|url>	Attach an image (drag-dropped paths auto-attach too)
/system <text>	Replace system prompt and reset chat
/optimize <dial>	Token-savings dial (beta), see below
/login	Set or replace your API key
/update	Check PyPI and upgrade
/cost /clear /help /exit	The usual

Mesh Optimize (beta)

Beta feature. Off by default. /optimize off bypasses everything.

One dial that cuts token spend on every request. /optimize 0.3 enables it:

dial	levers	quality impact
0	off, byte-identical passthrough	none
0 to 0.2	prompt-cache breakpoints on stable prefixes, max_tokens defaults	none
0.2 to 0.95	plus pruning of tool results the model already consumed	minimal

Every turn re-sends the whole conversation — a 5000-line test log from ten turns ago is billed again on every request after it. The pruning lever truncates consumed outputs (last 4 messages untouched); the cache lever marks the stable prefix for the provider's ~90% cache discount. Savings are only claimed when measurable; if the gateway rejects an optimized request, the raw request is retried automatically. Reference implementation: mesh-optimize on GitHub.

Config & state

~/.meshapi/ (all files 0600):

File	What
credentials	Your API key (set on first run, /login replaces)
config.json	Settings — model, auto_route, fallback_models, reasoning_effort, optimize (never the key)
history	Prompt history (secrets scrubbed)
servers.json	Background-server records for crash recovery
update_check.json	Update-checker cache
toolcall_failures.jsonl	Forensics for malformed tool calls

Env overrides: MESHAPI_API_KEY, MESHAPI_BASE_URL.

Platform notes

• macOS / Linux — everything above.
• Windows — fully supported for chat, tools, servers, completion, and the update check; three POSIX-only niceties degrade gracefully: mid-run typing/queueing/ESC (input is available between turns), mid-run Shift+Tab (works at the prompt), and in-place self-upgrade (the CLI prints the exact command to run instead — the running .exe is file-locked).

About Mesh API

Mesh API is a unified LLM gateway: one API key, 300+ models from OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Alibaba, and more. OpenAI-compatible — change the model name, leave everything else alone.

• Zero platform fees for 12 months. You only pay for tokens.
• Auto-routing. Send model: "auto" and the gateway picks the best model per prompt.
• Automatic failover. Provider down? Your request routes to another.
• Highest rate limits. Capacity pooled across providers.
• Zero data retention. Prompts and completions pass through; not stored.
• Full observability. Every request, token, cost tracked in real time; per-key limits.

Built by the founders of TagMango (YC W20) and AI Fiesta (1M+ users across India).

Why this CLI exists

Any generic OpenAI-compatible CLI talks to Mesh. meshapi adds what a generic one can't: the gateway-only cost field after every turn, /route auto + /models driving Mesh's gateway-side selection, an agentic loop hardened for cheap models (argument repair, quality guard), and 300+ models behind one fuzzy picker.

Roadmap

• ✅ 0.5.1 — first-run key setup, update checker, auto-routing, fuzzy model picker, web search, quality guard, self-healing tool calls, always-visible input, ESC abort
• 0.6 — repo memory: context captured while the agent writes code (token-free), warm-start repo maps, optional graphify backend
• later — npm i -g meshapi-code (Node port), Homebrew tap

License

Apache 2.0