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20e453f698
* feat(cli): add `litellm-proxy run -- <agent>` to wrap coding agents through the proxy Wraps Claude Code, Codex, OpenCode, and any other coding agent so all of its LLM traffic routes through a LiteLLM proxy, with the agent-vault style of "just works" DX: one `run -- <agent>` command, auto SSO login when interactive, env-key "agent mode" for containers/CI, and a fail-fast key check against the proxy so bad credentials error immediately instead of deep inside the agent. The wrapped binary is detected by name to pick the right variables. Claude Code gets ANTHROPIC_BASE_URL (the bare proxy root, so it appends /v1/messages) and ANTHROPIC_AUTH_TOKEN, with any stray ANTHROPIC_API_KEY cleared so the proxy token wins. Codex and OpenCode get OPENAI_BASE_URL (proxy + /v1) and OPENAI_API_KEY. Unrecognized commands get both sets so they work either way. `litellm-proxy claude-code` remains as a shortcut for `run -- claude`. The core logic is split into dependency-injected helpers (agent_profile, build_agent_env, verify_proxy_key, run_agent) so env wiring, the preflight, and the launch handoff are unit-tested without monkeypatching, alongside CliRunner tests for auth resolution, agent mode, and auto-login. Mutation-tested the env profiles, preflight, and agent-mode branch to confirm the tests fail when the behavior is broken. https://claude.ai/code/session_0154VpLXW7mMvk5wfbgPRJa6 * Make each coding agent its own litellm-proxy command Replace the `run -- <agent>` interface and the `claude-code` shortcut with top-level commands generated per known agent, so launching is just `litellm-proxy claude`, `litellm-proxy codex`, or `litellm-proxy opencode`, with everything after the agent name forwarded straight to it. This drops the ceremony of `run --` and cuts typing. The `--model`/`--small-fast-model` wrapper flags are gone; pass the agent's own model flag instead, or export the model env vars (the wrapper preserves what you already have set), which keeps the surface minimal and avoids intercepting flags the agent owns. Rename the module to agents.py to match. * fix(cli): route `litellm-proxy codex` through the proxy via a custom provider Codex ignores OPENAI_BASE_URL (it always dials api.openai.com over the Responses WebSocket transport), so the OpenAI env profile alone left `litellm-proxy codex` talking to OpenAI directly instead of the proxy. Point Codex at the proxy with a custom provider passed as `-c` config overrides, and force the HTTP/SSE Responses transport with supports_websockets=false since the proxy does not speak the Responses WebSocket protocol. The provider reads its key from OPENAI_API_KEY, which the agent env already exports. The overrides are injected ahead of the user's args so they precede Codex's subcommand. Claude Code and OpenCode are unaffected; they honor the exported env vars. Adds regression tests for the per-agent launch args and the injection ordering. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * Rename litellm-proxy CLI command to lite The proxy management CLI was invoked as litellm-proxy, which is a lot to type for an everyday command. Rename the console script entry point to lite and update the in-CLI usage examples, help text, error messages and docs to match. * fix(sso): stop CLI auth success page from hanging on "Closing..." The CLI opens the SSO success page with webbrowser.open, so the tab is not script-opened and the browser refuses window.close(). The countdown would end on "Closing..." and the tab would sit there forever. Drop the countdown and just show "You can now close this window and return to your terminal." from the start, while still attempting window.close() once so the tab auto-closes in the rare case the browser allows it. Add a regression test asserting the manual-close instruction is always present and the misleading countdown/"Closing..." text is gone. * fix(cli): reattach controlling terminal after SSO login, keep litellm-proxy alias When the first `lite claude` has to log in via browser SSO, completing the login could leave stdin detached from the terminal, so a TUI agent like Claude Code would start in non-interactive mode and exit with "Input must be provided". The wrapper now reopens the controlling terminal onto stdin just before handoff when the session started interactively; piped or redirected input is detected up front and left alone, so agent-mode and non-interactive use are unchanged. Also keep the `litellm-proxy` console script as an alias for `lite` so existing scripts and CI that invoke `litellm-proxy` keep working; both names map to the same CLI. * feat(install): make the curl installer need only curl, not a pre-existing Python The installer now lets uv provision a managed Python 3.13 when no suitable interpreter is found, instead of aborting. The minimum is also bumped from 3.9 to 3.10 to match the package's requires-python (>=3.10), so a system Python 3.9 is no longer selected only for uv tool install to reject it. * feat(cli): add thin litellm[cli] install path (install-cli.sh + brew) for the lite CLI On a developer laptop the `lite` CLI only needs `lite login` and running coding agents through a proxy, but the sole install path was `litellm[proxy]`, which drags in the whole server tree (fastapi, uvicorn, boto3, polars, cryptography, litellm-enterprise). The CLI's heavy imports are all guarded, so it runs on the base SDK plus just rich, pyyaml and requests. Add a `cli` extra carrying exactly those three, a `scripts/install-cli.sh` curl one-liner that installs `litellm[cli]`, and a `BerriAI/homebrew-litellm` tap formula with a release runbook under `packaging/homebrew/`. The installer passes no `--python`, so uv honours litellm's requires-python and provisions a managed interpreter, skipping a too-old (3.9) or too-new (3.14+) system Python instead of failing to resolve. A pyproject thin-contract test asserts the `cli` extra keeps the deps the CLI imports and never leaks a server-only dependency from `proxy`, so the laptop install cannot silently re-bloat * fix(install): let uv pick the Python via --python-preference system Both installers detected a system Python with a floor-only check and forced it with `uv tool install --python <interp>`. On a host whose only Python is outside litellm's requires-python (a too-old 3.9 or, increasingly, a too-new 3.14) that forced an incompatible interpreter and the resolve failed. Drop the detection and pass `--python-preference system`: uv reuses a compatible system Python when present and downloads a managed one otherwise, always honouring requires-python * test(router): filter aiohttp unclosed-session gc noise in test_async_fallbacks test_async_fallbacks asserts the last three captured log records are the router's fallback messages. Under the litellm_router_testing job (pytest -k router -n 4) many router tests share the module-level in_memory_llm_clients_cache (max 200, ttl 3600s). Older cached OpenAI/Azure clients get evicted while their aiohttp ClientSession is still open, and when the gc reclaims them aiohttp emits "Unclosed client session"/"Unclosed connector" through the asyncio logger. Those records land in caplog mid-test and push the expected router logs out of the last-three window, so the assertion flips to failing non-deterministically. These warnings are async cleanup noise, not router debug logs, so filter them out exactly like the existing leaked-task warnings before asserting order. The assertion on the three router fallback messages is unchanged. --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com>
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
import asyncio
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import os
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import sys
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import time
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import traceback
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import pytest
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import asyncio
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import logging
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import litellm
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from litellm import Router
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# this tests debug logs from litellm router and litellm proxy server
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from litellm._logging import verbose_logger, verbose_proxy_logger, verbose_router_logger
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from litellm.llms.custom_httpx.async_client_cleanup import close_litellm_async_clients
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# this tests debug logs from litellm router and litellm proxy server
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def test_async_fallbacks(caplog):
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# THIS IS A PROD TEST - DO NOT DELETE THIS. Used for testing if litellm proxy verbose logs are human readable
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litellm.set_verbose = False
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litellm.success_callback = []
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litellm.failure_callback = []
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verbose_router_logger.setLevel(level=logging.INFO)
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verbose_logger.setLevel(logging.CRITICAL + 1)
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verbose_proxy_logger.setLevel(logging.CRITICAL + 1)
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model_list = [
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{
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"model_name": "azure/gpt-3.5-turbo",
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"litellm_params": {
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"model": "azure/gpt-4.1-mini",
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"api_key": os.getenv("AZURE_AI_API_KEY"),
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"api_version": os.getenv("AZURE_API_VERSION"),
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"api_base": os.getenv("AZURE_AI_API_BASE"),
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"mock_response": "Hello world",
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},
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"tpm": 240000,
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"rpm": 1800,
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},
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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"api_key": "bad-key",
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},
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"tpm": 1000000,
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"rpm": 9000,
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},
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]
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router = Router(
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model_list=model_list,
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fallbacks=[{"gpt-3.5-turbo": ["azure/gpt-3.5-turbo"]}],
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num_retries=1,
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)
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user_message = "Hello, how are you?"
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messages = [{"content": user_message, "role": "user"}]
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async def _make_request():
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try:
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await router.acompletion(
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model="gpt-3.5-turbo", messages=messages, max_tokens=1
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)
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router.reset()
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except litellm.Timeout:
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pass
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except Exception as e:
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pytest.fail(f"An exception occurred: {e}")
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finally:
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router.reset()
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# Close cached aiohttp/httpx clients before the event loop ends
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# to prevent "Unclosed client session" / "Unclosed connector" warnings.
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await close_litellm_async_clients()
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asyncio.run(_make_request())
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captured_logs = [rec.message for rec in caplog.records]
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# on circle ci the captured logs get async cleanup noise from the gc (leaked
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# task warnings, plus aiohttp "Unclosed client session"/"Unclosed connector"
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# warnings from cached clients other router tests evicted) - filter it out
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captured_logs = [
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log
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for log in captured_logs
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if "Task exception was never retrieved" not in log
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and "Task was destroyed but it is pending" not in log
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and "get_available_deployment" not in log
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and "in the Langfuse queue" not in log
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and "Unclosed client session" not in log
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and "Unclosed connector" not in log
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]
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print("\n Captured caplog records - ", captured_logs)
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# Define the expected log messages
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# - error request, falling back notice, success notice
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expected_logs = [
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"Falling back to model_group = azure/gpt-3.5-turbo",
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"litellm.acompletion(model=azure/gpt-4.1-mini)\x1b[32m 200 OK\x1b[0m",
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"Successful fallback b/w models.",
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]
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# Assert that the captured logs match the expected log messages
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assert captured_logs[-3:] == expected_logs
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