From 459c2936f9cdfab51ccfe365a3cdfa603e105ef2 Mon Sep 17 00:00:00 2001
From: azul <fede.garza.ramirez@gmail.com>
Date: Wed, 5 Aug 2026 14:27:12 -0700
Subject: [PATCH] Revert "feat: add LiteLLM support via litellm: model prefix"

---
 pyproject.toml       |  3 ---
 tests/test_live.py   | 26 --------------------------
 timecopilot/agent.py | 32 +-------------------------------
 3 files changed, 1 insertion(+), 60 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6f32ee0f..8fda773f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -122,9 +122,6 @@ distributed = [
   "pyspark<4.1",
   "ray==2.48",
 ]
-litellm = [
-  "litellm>=1.80.0,<1.87.0",
-]
 
 [project.scripts]
 timecopilot = "timecopilot._cli:main"
diff --git a/tests/test_live.py b/tests/test_live.py
index b402d5ca..01d2201f 100644
--- a/tests/test_live.py
+++ b/tests/test_live.py
@@ -153,32 +153,6 @@ async def test_async_is_queryable():
     print(answer.output)
 
 
-@pytest.mark.live
-@pytest.mark.flaky(reruns=3, reruns_delay=2)
-def test_litellm_forecast():
-    h = 2
-    df = generate_series(
-        n_series=1,
-        freq="D",
-        min_length=30,
-        static_as_categorical=False,
-        with_trend=True,
-    )
-    tc = TimeCopilot(
-        llm="litellm:openai/gpt-4o-mini",
-        forecasters=[
-            ZeroModel(),
-        ],
-    )
-    result = tc.forecast(
-        df=df,
-        query=f"Please forecast the series with a horizon of {h} and frequency D.",
-    )
-    assert len(result.fcst_df) == h
-    assert result.features_df is not None
-    assert result.eval_df is not None
-
-
 @pytest.mark.live
 @pytest.mark.asyncio
 @pytest.mark.flaky(reruns=3, reruns_delay=2)
diff --git a/timecopilot/agent.py b/timecopilot/agent.py
index 0e14203f..f24e0535 100644
--- a/timecopilot/agent.py
+++ b/timecopilot/agent.py
@@ -8,7 +8,6 @@
 from pydantic_ai import Agent, ModelRetry, RunContext
 from pydantic_ai.agent import AgentRunResult
 from pydantic_ai.models import Model
-from pydantic_ai.models.openai import OpenAIModel
 from rich.console import Console
 from rich.panel import Panel
 from rich.table import Table
@@ -35,35 +34,6 @@
 
 from .forecaster import Forecaster, TimeCopilotForecaster
 from .models.adapters.sktime import SKTimeAdapter
-
-
-def _resolve_llm(llm: str | Model) -> str | Model:
-    """Resolve LLM string, adding LiteLLM support for 100+ providers.
-
-    If the string starts with 'litellm:', creates a pydantic-ai OpenAI model
-    backed by litellm SDK (no proxy needed). LiteLLM reads provider API keys
-    from environment variables (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.).
-
-    Examples:
-        --llm litellm:anthropic/claude-sonnet-4-6
-        --llm litellm:groq/llama-3.3-70b-versatile
-        --llm litellm:deepseek/deepseek-chat
-    """
-    if not (isinstance(llm, str) and llm.startswith("litellm:")):
-        return llm
-
-    model_name = llm[len("litellm:"):]
-    try:
-        import litellm
-
-        litellm.drop_params = True
-        client = litellm.AsyncOpenAI()
-    except ImportError:
-        raise ImportError(
-            "litellm package required for litellm: models. "
-            "Install with: pip install 'timecopilot[litellm]'"
-        )
-    return OpenAIModel(model_name, openai_client=client)
 from .models.prophet import Prophet
 from .models.stats import (
     ADIDA,
@@ -571,7 +541,7 @@ def __init__(
                 "model is not allowed to be passed as a keyword argument"
                 "use `llm` instead"
             )
-        self.llm = _resolve_llm(llm)
+        self.llm = llm
 
         self.forecasting_agent = Agent(
             deps_type=ExperimentDataset,