From 9f910269d0de25f85cda5b745c600b9e138c04c2 Mon Sep 17 00:00:00 2001
From: RheagalFire <arishalam121@gmail.com>
Date: Fri, 12 Jun 2026 01:24:50 +0530
Subject: [PATCH 1/2] feat: add LiteLLM support via litellm: model prefix

---
 pyproject.toml       |  3 +++
 timecopilot/agent.py | 30 +++++++++++++++++++++++++++++-
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index b9682d22..b523f474 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -121,6 +121,9 @@ distributed = [
   "pyspark<4.1",
   "ray==2.48",
 ]
+litellm = [
+  "litellm>=1.80.0,<1.87.0",
+]
 
 [project.scripts]
 timecopilot = "timecopilot._cli:main"
diff --git a/timecopilot/agent.py b/timecopilot/agent.py
index f24e0535..d690d23d 100644
--- a/timecopilot/agent.py
+++ b/timecopilot/agent.py
@@ -8,6 +8,7 @@
 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
@@ -34,6 +35,33 @@
 
 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
+    pointed at a running LiteLLM proxy. Set LITELLM_BASE_URL to your proxy
+    address (default: http://localhost:4000/v1).
+
+    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
+
+    import os
+
+    from openai import AsyncOpenAI
+
+    model_name = llm[len("litellm:"):]
+    base_url = os.environ.get("LITELLM_BASE_URL", "http://localhost:4000/v1")
+    api_key = os.environ.get("LITELLM_API_KEY", "sk-unused")
+
+    client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+    return OpenAIModel(model_name, openai_client=client)
 from .models.prophet import Prophet
 from .models.stats import (
     ADIDA,
@@ -541,7 +569,7 @@ def __init__(
                 "model is not allowed to be passed as a keyword argument"
                 "use `llm` instead"
             )
-        self.llm = llm
+        self.llm = _resolve_llm(llm)
 
         self.forecasting_agent = Agent(
             deps_type=ExperimentDataset,

From efc016bc26cce7f0bbdd9f016d1ab1c542bd74f7 Mon Sep 17 00:00:00 2001
From: RheagalFire <arishalam121@gmail.com>
Date: Fri, 12 Jun 2026 01:29:15 +0530
Subject: [PATCH 2/2] fix: use litellm SDK directly instead of proxy

---
 timecopilot/agent.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/timecopilot/agent.py b/timecopilot/agent.py
index d690d23d..0e14203f 100644
--- a/timecopilot/agent.py
+++ b/timecopilot/agent.py
@@ -41,8 +41,8 @@ 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
-    pointed at a running LiteLLM proxy. Set LITELLM_BASE_URL to your proxy
-    address (default: http://localhost:4000/v1).
+    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
@@ -52,15 +52,17 @@ def _resolve_llm(llm: str | Model) -> str | Model:
     if not (isinstance(llm, str) and llm.startswith("litellm:")):
         return llm
 
-    import os
-
-    from openai import AsyncOpenAI
-
     model_name = llm[len("litellm:"):]
-    base_url = os.environ.get("LITELLM_BASE_URL", "http://localhost:4000/v1")
-    api_key = os.environ.get("LITELLM_API_KEY", "sk-unused")
-
-    client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+    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 (