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Replace custom provider abstraction with litellm

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Drops the src/providers/ package (base class, AnthropicProvider,
OpenAIProvider, factory) in favour of a single src/llm.py that calls
litellm.completion() directly. litellm handles provider routing,
authentication, and SDK differences for 100+ providers without any
code we need to maintain.

Changes:
- Delete src/providers/ entirely
- Add src/llm.py — one complete() function wrapping litellm
- src/agent/core.py: EmailAgent takes model: str instead of LLMProvider
- src/config.py: ai_provider + api key fields → single ai_model string
  in litellm format (e.g. "anthropic/claude-opus-4-6")
- main.py: remove provider factory wiring; pass config.ai_model to agent
- .env.example: simplify AI section, show litellm model string examples
- pyproject.toml: replace anthropic + openai deps with litellm>=1.0.0
- uv.lock: regenerated

https://claude.ai/code/session_01HaUFs7SaLD5SoiuGCY27Tw
This commit is contained in:
Claude
2026-02-21 07:35:37 +00:00
parent 1ba42f9497
commit 431847a8b7
11 changed files with 972 additions and 226 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/agent/core.py
+7 -9
View File
@@ -7,7 +7,7 @@ from datetime import datetime, timezone
from ..models.conversation import ConversationState, ChatMessage
from ..models.registration import BookingDay, RegistrationData
from ..providers.base import LLMProvider, LLMMessage
from .. import llm
from ..knowledge_base.loader import KnowledgeBase
from ..storage.json_store import ConversationStore, normalize_email, _diff_registrations
from ..notifications.notifier import AdminNotifier
@@ -28,12 +28,12 @@ class EmailAgent:
def __init__(
self,
provider: LLMProvider,
model: str,
kb: KnowledgeBase,
store: ConversationStore,
notifier: AdminNotifier,
) -> None:
self._provider = provider
self._model = model
self._kb = kb
self._store = store
self._notifier = notifier
@@ -97,11 +97,10 @@ class EmailAgent:
def _handle_registration(self, state: ConversationState) -> str:
"""Drive the in-progress registration conversation."""
system = build_system_prompt(self._kb, state)
llm_messages = [LLMMessage(role=m.role, content=m.content) for m in state.messages]
try:
response = self._provider.complete(system=system, messages=llm_messages)
parsed = self._parse_llm_response(response.content)
content = llm.complete(self._model, system, state.messages)
parsed = self._parse_llm_response(content)
except Exception:
logger.exception("LLM call failed for %s", state.conversation_id)
return self._fallback_message(state)
@@ -140,11 +139,10 @@ class EmailAgent:
def _handle_post_completion(self, state: ConversationState) -> str:
"""Handle messages received after a registration is already complete."""
system = build_system_prompt(self._kb, state)
llm_messages = [LLMMessage(role=m.role, content=m.content) for m in state.messages]
try:
response = self._provider.complete(system=system, messages=llm_messages)
parsed = self._parse_llm_response(response.content)
content = llm.complete(self._model, system, state.messages)
parsed = self._parse_llm_response(content)
except Exception:
logger.exception("LLM call failed (post-completion) for %s", state.conversation_id)
return self._fallback_message(state)
src/config.py
+4 -9
View File
@@ -13,11 +13,9 @@ except ImportError:
@dataclass
class Config:
# AI Provider
ai_provider: str = "anthropic" # "anthropic" or "openai"
ai_model: str = ""
anthropic_api_key: str = ""
openai_api_key: str = ""
# AI model — litellm format, e.g. "anthropic/claude-opus-4-6" or "openai/gpt-4o".
# The matching API key must be set as an env var (ANTHROPIC_API_KEY, OPENAI_API_KEY, …).
ai_model: str = "anthropic/claude-opus-4-6"
# Email — IMAP (receiving)
imap_host: str = ""
@@ -48,10 +46,7 @@ class Config:
@classmethod
def from_env(cls) -> "Config":
return cls(
ai_provider=os.getenv("AI_PROVIDER", "anthropic"),
ai_model=os.getenv("AI_MODEL", ""),
anthropic_api_key=os.getenv("ANTHROPIC_API_KEY", ""),
openai_api_key=os.getenv("OPENAI_API_KEY", ""),
ai_model=os.getenv("AI_MODEL", "anthropic/claude-opus-4-6"),
imap_host=os.getenv("IMAP_HOST", ""),
imap_port=int(os.getenv("IMAP_PORT", "993")),
imap_username=os.getenv("IMAP_USERNAME", ""),
src/llm.py
+22
View File
@@ -0,0 +1,22 @@
"""LLM completion via litellm — supports any provider with a single call."""
import litellm
def complete(model: str, system: str, messages: list) -> str:
"""Call any LLM and return the response text.
Args:
model: litellm model string, e.g. "anthropic/claude-opus-4-6" or
"openai/gpt-4o". The matching API key must be set as an
environment variable (ANTHROPIC_API_KEY, OPENAI_API_KEY, …).
system: System prompt text.
messages: List of objects with .role and .content attributes.
Returns:
The model's reply as a plain string.
"""
api_messages = [{"role": "system", "content": system}]
api_messages += [{"role": m.role, "content": m.content} for m in messages]
response = litellm.completion(model=model, messages=api_messages, max_tokens=2048)
return response.choices[0].message.content
src/providers/__init__.py
-34
View File
@@ -1,34 +0,0 @@
"""LLM provider registry."""
from .base import LLMProvider, LLMMessage, LLMResponse
from .anthropic_provider import AnthropicProvider
from .openai_provider import OpenAIProvider
__all__ = [
"LLMProvider",
"LLMMessage",
"LLMResponse",
"AnthropicProvider",
"OpenAIProvider",
"create_provider",
]
def create_provider(provider: str, api_key: str, model: str = "") -> LLMProvider:
"""Instantiate the correct LLMProvider by name.
Args:
provider: "anthropic" or "openai"
api_key: API key for the chosen provider.
model: Optional model name override.
Returns:
Configured LLMProvider instance.
"""
if provider == "anthropic":
return AnthropicProvider(api_key=api_key, model=model)
if provider == "openai":
return OpenAIProvider(api_key=api_key, model=model)
raise ValueError(
f"Unknown AI provider: '{provider}'. Supported values: 'anthropic', 'openai'."
)
src/providers/anthropic_provider.py
-37
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@@ -1,37 +0,0 @@
"""Anthropic (Claude) LLM provider."""
from .base import LLMProvider, LLMMessage, LLMResponse
class AnthropicProvider(LLMProvider):
DEFAULT_MODEL = "claude-opus-4-6"
def __init__(self, api_key: str, model: str = "") -> None:
try:
import anthropic
except ImportError as exc:
raise ImportError(
"Install the 'anthropic' package to use the Anthropic provider: "
"pip install anthropic"
) from exc
self._client = anthropic.Anthropic(api_key=api_key)
self._model = model or self.DEFAULT_MODEL
def complete(self, system: str, messages: list) -> LLMResponse:
api_messages = [
{"role": m.role, "content": m.content}
for m in messages
if m.role in ("user", "assistant")
]
response = self._client.messages.create(
model=self._model,
max_tokens=2048,
system=system,
messages=api_messages,
)
return LLMResponse(content=response.content[0].text)
@property
def model_name(self) -> str:
return self._model
src/providers/base.py
-36
View File
@@ -1,36 +0,0 @@
"""Abstract base class for LLM providers."""
from abc import ABC, abstractmethod
from dataclasses import dataclass
@dataclass
class LLMMessage:
role: str # "user" or "assistant"
content: str
@dataclass
class LLMResponse:
content: str
class LLMProvider(ABC):
"""Uniform interface for any LLM backend."""
@abstractmethod
def complete(self, system: str, messages: list) -> LLMResponse:
"""Generate a completion.
Args:
system: System prompt text.
messages: List of LLMMessage objects (user/assistant turns).
Returns:
LLMResponse with the model's text output.
"""
@property
@abstractmethod
def model_name(self) -> str:
"""Human-readable model identifier."""
src/providers/openai_provider.py
-37
View File
@@ -1,37 +0,0 @@
"""OpenAI (GPT) LLM provider."""
from .base import LLMProvider, LLMMessage, LLMResponse
class OpenAIProvider(LLMProvider):
DEFAULT_MODEL = "gpt-4o"
def __init__(self, api_key: str, model: str = "") -> None:
try:
from openai import OpenAI
except ImportError as exc:
raise ImportError(
"Install the 'openai' package to use the OpenAI provider: "
"pip install openai"
) from exc
self._client = OpenAI(api_key=api_key)
self._model = model or self.DEFAULT_MODEL
def complete(self, system: str, messages: list) -> LLMResponse:
api_messages = [{"role": "system", "content": system}]
api_messages.extend(
{"role": m.role, "content": m.content}
for m in messages
if m.role in ("user", "assistant")
)
response = self._client.chat.completions.create(
model=self._model,
messages=api_messages,
max_tokens=2048,
)
return LLMResponse(content=response.choices[0].message.content)
@property
def model_name(self) -> str:
return self._model