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agent-cli-to-api

Expose popular agent CLIs as a small OpenAI-compatible HTTP API (/v1/*).

Works great as a local gateway (localhost) or behind a reverse proxy.

Think of it as LiteLLM for agent CLIs: you point existing OpenAI SDKs/tools at base_url, and choose a backend by model.

Supported backends:

  • OpenAI Codex - defaults to backend /responses for vision; falls back to codex exec
  • Cursor Agent - via cursor-agent CLI
  • Claude Code - via CLI or direct API (auto-detects ~/.claude/settings.json config)
  • Gemini - via CLI or CloudCode direct (set GEMINI_USE_CLOUDCODE_API=1)

Why this exists:

  • Many tools/SDKs only speak the OpenAI API (/v1/chat/completions) - this lets you plug agent CLIs into that ecosystem.
  • One gateway, multiple CLIs: pick a backend by model (with optional prefixes like cursor: / claude: / gemini:).

Table of Contents

Requirements

  • Python 3.10+ (tested on 3.13)
  • Install and authenticate the CLI(s) you want to use (codex, cursor-agent, claude, gemini)

Install

Option A: uv (recommended)

uv sync

Option B: pip

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run (No .env Needed)

Pick a provider and start the gateway:

uv run agent-cli-to-api codex
uv run agent-cli-to-api gemini
uv run agent-cli-to-api claude
uv run agent-cli-to-api cursor-agent
uv run agent-cli-to-api doctor

By default agent-cli-to-api does NOT load .env implicitly.

Optional auth:

CODEX_GATEWAY_TOKEN=devtoken uv run agent-cli-to-api codex

Custom bind host/port:

uv run agent-cli-to-api codex --host 127.0.0.1 --port 8000

Log request curl commands (optional):

uv run agent-cli-to-api codex curl
# or
uv run agent-cli-to-api codex --log-curl

Notes:

  • If CODEX_WORKSPACE is unset, the gateway creates an empty temp workspace under /tmp (so you don't need to configure a repo path).
  • When you start with a fixed provider (e.g. ... gemini), the client-sent model string is accepted but ignored by default (gateway uses the provider's default model).
  • Each provider still requires its own local CLI login state (no API key is required for Codex / Gemini CloudCode / Claude OAuth).
  • Claude auto-detects ~/.claude/settings.json and uses direct API mode if ANTHROPIC_AUTH_TOKEN and ANTHROPIC_BASE_URL are configured.
  • uv run agent-cli-to-api cursor-agent defaults to Cursor Auto routing (CURSOR_AGENT_MODEL=auto). If you want faster responses, run with --preset cursor-fast.
  • When running in an interactive terminal (TTY), the gateway enables colored logs and Markdown rendering by default. To disable: CODEX_RICH_LOGS=0 or CODEX_LOG_RENDER_MARKDOWN=0.

Quick smoke test (optional):

# In another terminal, run:
#   uv run agent-cli-to-api codex
# Then:
BASE_URL=http://127.0.0.1:8000/v1 ./scripts/smoke.sh
# If you enabled auth:
TOKEN=devtoken BASE_URL=http://127.0.0.1:8000/v1 ./scripts/smoke.sh

Core Configuration

Presets

export CODEX_PRESET=codex-fast
uv run agent-cli-to-api codex

Supported presets:

  • codex-fast
  • autoglm-phone
  • cursor-auto
  • cursor-fast (Cursor model pinned for speed)
  • gemini-cloudcode (defaults to gemini-3-flash-preview)
  • claude-oauth

Multi-provider routing

Use CODEX_PROVIDER=auto and select providers per-request by prefixing model:

  • Codex: "gpt-5.4"
  • Cursor: "cursor:<model>"
  • Claude: "claude:<model>"
  • Gemini: "gemini:<model>"

Codex backend options

  • CODEX_CODEX_ALLOW_TOOLS=0 to disable Codex backend tool calls (default: enabled).
  • OpenAI tools/tool_choice are mapped for Codex backend, Claude OAuth, and Gemini CloudCode (best-effort).

Claude direct API (recommended)

The gateway auto-detects your Claude CLI configuration from ~/.claude/settings.json:

# If you have Claude CLI configured with a custom API endpoint (e.g. 小米 MiMo, 腾讯混元, etc.)
# Just run - no extra config needed:
uv run agent-cli-to-api claude

The gateway will automatically:

  1. Read ANTHROPIC_AUTH_TOKEN and ANTHROPIC_BASE_URL from ~/.claude/settings.json
  2. Use direct HTTP API calls (fast, ~0ms gateway overhead)
  3. Log timing breakdown: auth_ms, prepare_ms, api_latency_ms

Alternative: Claude OAuth (Anthropic official):

uv run python -m codex_gateway.claude_oauth_login
CLAUDE_USE_OAUTH_API=1 uv run agent-cli-to-api claude

uvx (no venv)

uvx --from git+https://github.com/leeguooooo/agent-cli-to-api agent-cli-to-api codex

Cloudflare Tunnel

CODEX_GATEWAY_TOKEN=devtoken uv run agent-cli-to-api codex
cloudflared tunnel --url http://127.0.0.1:8000

For advanced env vars, see .env.example and codex_gateway/config.py.

API

  • GET /healthz
  • GET /debug/config (effective runtime config; requires auth if CODEX_GATEWAY_TOKEN is set)
  • GET /v1/models
  • POST /v1/embeddings (proxies to OpenAI embeddings; requires OPENAI_API_KEY or ~/.codex/auth.json with OPENAI_API_KEY)
  • POST /v1/chat/completions (supports stream)
  • POST /v1/messages (Anthropic Messages-compatible; supports stream)
  • POST /v1/messages/count_tokens (Anthropic-compatible; currently heuristic token counting)

Tip: any OpenAI SDK that supports base_url should work by pointing it at this server. Tip: Claude Code can point ANTHROPIC_BASE_URL at this server and use ANTHROPIC_AUTH_TOKEN for gateway auth.

Auth note: include Authorization: Bearer <token> only when you set CODEX_GATEWAY_TOKEN on the gateway.

Example (non-stream)

curl -s http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -d '{
    "model":"gpt-5.4",
    "messages":[{"role":"user","content":"总结一下这个仓库结构"}],
    "reasoning": {"effort":"low"},
    "stream": false
  }'

Example (embeddings)

curl -s http://127.0.0.1:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -d '{
    "model":"text-embedding-3-small",
    "input":"hello world"
  }'

Example (stream)

curl -N http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -H "X-Codex-Session-Id: 0f3d5b6f-2a3b-4d78-9f50-123456789abc" \
  -d '{
    "model":"gpt-5-codex",
    "messages":[{"role":"user","content":"用一句话解释这个项目的目的"}],
    "stream": true
  }'

Example (Anthropic Messages)

curl -s http://127.0.0.1:8000/v1/messages \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model":"claude-sonnet-4-6",
    "max_tokens": 256,
    "messages":[
      {"role":"user","content":"用一句话解释这个项目的作用"}
    ]
  }'

Example (Anthropic count_tokens)

curl -s http://127.0.0.1:8000/v1/messages/count_tokens \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model":"claude-sonnet-4-6",
    "messages":[
      {"role":"user","content":"hello"}
    ]
  }'

Example (vision / screenshot)

When CODEX_LOG_MODE=full (or CODEX_LOG_EVENTS=1), the gateway logs image[0] ext=... bytes=... and decoded_images=N so you can confirm images are being received/decoded.

python - <<'PY' > /tmp/payload.json
import base64, json
img_b64 = base64.b64encode(open("screenshot.png","rb").read()).decode()
print(json.dumps({
  "model": "gpt-5-codex",
  "stream": False,
  "messages": [{
    "role": "user",
    "content": [
      {"type": "text", "text": "读取图片里的文字，只输出文字本身"},
      {"type": "image_url", "image_url": {"url": "data:image/png;base64," + img_b64}},
    ],
  }],
}))
PY

curl -s http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -d @/tmp/payload.json

PDF input uses OpenAI-style type: "file" parts:

python - <<'PY' > /tmp/pdf-payload.json
import base64, json
pdf_b64 = base64.b64encode(open("label.pdf","rb").read()).decode()
print(json.dumps({
  "model": "gpt-5.4",
  "stream": False,
  "messages": [{
    "role": "user",
    "content": [
      {"type": "file", "file": {"filename": "label.pdf", "file_data": pdf_b64}},
      {"type": "text", "text": "Check these rules and summarize the key constraints."},
    ],
  }],
}))
PY

curl -s http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer devtoken" \
  -d @/tmp/pdf-payload.json

OpenAI SDK examples

Python:

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="devtoken")
resp = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Hi"}],
)
print(resp.choices[0].message.content)

TypeScript:

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://127.0.0.1:8000/v1",
  apiKey: process.env.CODEX_GATEWAY_TOKEN ?? "devtoken",
});

const resp = await client.chat.completions.create({
  model: "gpt-5.4",
  messages: [{ role: "user", content: "Hi" }],
});

console.log(resp.choices[0].message.content);

Security notes

You are exposing an agent that can read files and run commands depending on CODEX_SANDBOX. Keep it private by default, use a token, and run in an isolated environment when deploying.

Logging & Performance Diagnosis

The gateway provides detailed timing logs to help diagnose latency:

INFO  claude-oauth request: url=https://api.example.com/v1/messages model=xxx auth_ms=0 prepare_ms=0
INFO  claude-oauth response: status=200 api_latency_ms=2886 parse_ms=0 total_ms=2887
Metric Description
auth_ms Time to load/refresh credentials
prepare_ms Time to build request payload
api_latency_ms Upstream API response time (main bottleneck)
parse_ms Time to parse response
total_ms Total gateway processing time

If api_latency_ms ≈ total_ms, the latency is entirely from the upstream API (not the gateway).

Log modes

CODEX_LOG_MODE=summary  # one line per request (default)
CODEX_LOG_MODE=qa       # show Q (question) and A (answer)
CODEX_LOG_MODE=full     # full prompt + response

Performance notes (important)

If your normal ~/.codex/config.toml has many mcp_servers.* entries, Codex will start them for every codex exec call and include their tool schemas in the prompt. This can add seconds of startup time and 10k+ prompt tokens per request.

For an HTTP gateway, it's usually best to run Codex with a minimal config (no MCP servers).

By default the gateway uses your system ~/.codex (so auth stays in sync). If you want a minimal, isolated config (no MCP servers), set CODEX_CLI_HOME to a gateway-local directory. On first run it will try to copy ~/.codex/auth.json into that directory (so you don't have to).

If you want to set it up manually or customize it:

export CODEX_CLI_HOME=$PWD/.codex-gateway-home
mkdir -p "$CODEX_CLI_HOME/.codex"
cp ~/.codex/auth.json "$CODEX_CLI_HOME/.codex/auth.json"   # or set CODEX_API_KEY instead
cat > "$CODEX_CLI_HOME/.codex/config.toml" <<'EOF'
model = "gpt-5.4"
model_reasoning_effort = "low"

[projects."/path/to/your/workspace"]
trust_level = "trusted"
EOF

Advanced setup (optional)

Use .env

cp .env.example .env
uv run agent-cli-to-api codex --env-file .env

Tip: you can also opt-in to loading .env from the current directory with --auto-env.

Auto-start on macOS (launchd)

This installs a user LaunchAgent and keeps the gateway running after reboot.

chmod +x scripts/install_launchd.sh
scripts/install_launchd.sh --provider codex --host 127.0.0.1 --port 8000

Optional env/token:

scripts/install_launchd.sh --env-file "$PWD/.env" --token devtoken

Uninstall:

scripts/install_launchd.sh --uninstall

Logs:

  • ~/Library/Logs/com.codex-api.gateway.out.log
  • ~/Library/Logs/com.codex-api.gateway.err.log

Note: uv must be on your PATH (e.g. /opt/homebrew/bin/uv).

Prettier terminal logs (optional)

Enable colored logs (Rich handler):

export CODEX_RICH_LOGS=1
uv run agent-cli-to-api codex

Render assistant output as Markdown in the terminal (best-effort; prints a separate block to stderr):

export CODEX_LOG_RENDER_MARKDOWN=1
uv run agent-cli-to-api codex

Log request curl commands (useful for replay/debug):

export CODEX_LOG_REQUEST_CURL=1
uv run agent-cli-to-api codex

Keywords (SEO)

OpenAI-compatible API, chat completions, SSE streaming, agent gateway, CLI to API proxy, Codex CLI, Cursor Agent, Claude Code, Gemini CLI.