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Add sync LLM client for LangGraph compatibility

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The main structure is in place. For production, the async
event loop issues need resolution but core architecture is solid.
This commit is contained in:
Mark Randall Havens
2026-03-12 20:17:19 +00:00
parent babf0b593a
commit 9692c89214
2 changed files with 171 additions and 18 deletions
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opus_orchestrator/langgraph_workflow.py
+29 -18
View File
@@ -31,6 +31,7 @@ from opus_orchestrator.agents.fiction import (
)
from opus_orchestrator.config import AgentConfig
from opus_orchestrator.frameworks import get_framework_prompt, StoryFramework
from opus_orchestrator.utils.llm_sync import LLMClient
# ============== STATE SCHEMA ==============
@@ -200,9 +201,22 @@ class OpusGraph:
self.voice = VoiceAgent(self.agent_config)
self.editor = EditorAgent(self.agent_config)
# Create async event loop for LLM calls
self._loop = None
# Build graph
self.graph = self._build_graph()
def _get_loop(self):
"""Get or create event loop."""
import asyncio
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop
def _build_graph(self) -> StateGraph:
"""Build the LangGraph."""
@@ -292,6 +306,11 @@ class OpusGraph:
return "iterate"
def _run_async(self, coro):
"""Run async coroutine properly."""
loop = self._get_loop()
return loop.run_until_complete(coro)
# ============== NODE IMPLEMENTATIONS ==============
def node_seed(self, state: OpusGraphState) -> OpusGraphState:
@@ -306,7 +325,6 @@ class OpusGraph:
"""Stage 1: One sentence summary."""
print("\n📝 STAGE 1: One sentence...")
import asyncio
framework_prompt = get_framework_prompt(StoryFramework(self.framework))
user_prompt = f"""Create ONE SENTENCE that captures this entire story.
@@ -320,7 +338,7 @@ Requirements:
Seed: {state.seed_concept}
"""
result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
result = self._run_async(self.architect.call_llm(framework_prompt, user_prompt))
state.prewriting.one_sentence = result.strip()
state.messages.append(f"One sentence: {state.prewriting.one_sentence[:80]}...")
@@ -333,7 +351,6 @@ Seed: {state.seed_concept}
"""Stage 2: One paragraph outline."""
print("📝 STAGE 2: One paragraph...")
import asyncio
framework_prompt = get_framework_prompt(StoryFramework(self.framework))
user_prompt = f"""Expand to ONE PARAGRAPH (4-8 sentences):
@@ -351,7 +368,7 @@ Include:
One sentence: {state.prewriting.one_sentence}
"""
result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
result = self._run_async(self.architect.call_llm(framework_prompt, user_prompt))
state.prewriting.one_paragraph = result.strip()
state.messages.append("One paragraph outline complete")
@@ -364,8 +381,7 @@ One sentence: {state.prewriting.one_sentence}
"""Stage 3: Character sheets."""
print("📝 STAGE 3: Character sheets...")
import asyncio
result = asyncio.run(self.character_lead.execute(
result = self._run_async(self.character_lead.execute(
{"characters": [], "raw_content": state.prewriting.one_paragraph},
{},
))
@@ -385,7 +401,6 @@ One sentence: {state.prewriting.one_sentence}
"""Stage 4: Four page outline."""
print("📝 STAGE 4: Four-page outline...")
import asyncio
framework_prompt = get_framework_prompt(StoryFramework(self.framework))
user_prompt = f"""Create a detailed outline (4 pages worth):
@@ -395,7 +410,7 @@ Outline: {state.prewriting.one_paragraph}
Characters: {', '.join(c.name for c in state.prewriting.characters)}
"""
result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
result = self._run_async(self.architect.call_llm(framework_prompt, user_prompt))
state.prewriting.outline_sections = [s.strip() for s in result.split("\n\n") if s.strip()]
state.messages.append("Four-page outline complete")
@@ -408,8 +423,7 @@ Characters: {', '.join(c.name for c in state.prewriting.characters)}
"""Stage 5: Detailed character charts."""
print("📝 STAGE 5: Character charts...")
import asyncio
result = asyncio.run(self.character_lead.execute(
result = self._run_async(self.character_lead.execute(
{"characters": [], "raw_content": state.prewriting.one_paragraph},
{},
))
@@ -429,7 +443,6 @@ Characters: {', '.join(c.name for c in state.prewriting.characters)}
"""Stage 6: Scene list."""
print("📝 STAGE 6: Scene list...")
import asyncio
framework_prompt = get_framework_prompt(StoryFramework(self.framework))
num_scenes = max(10, self.target_word_count // 1500)
@@ -439,7 +452,7 @@ Characters: {', '.join(c.name for c in state.prewriting.characters)}
For each: name, description, POV character, location, purpose.
"""
result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
result = self._run_async(self.architect.call_llm(framework_prompt, user_prompt))
scenes = self._parse_scenes(result)
state.prewriting.scene_list = scenes
@@ -469,13 +482,12 @@ For each: name, description, POV character, location, purpose.
"""Stage 7: Scene descriptions."""
print("📝 STAGE 7: Scene descriptions...")
import asyncio
user_prompt = f"""Describe key scenes:
{chr(10).join(f"- {s.name}: {s.description}" for s in state.prewriting.scene_list[:10])}
"""
result = asyncio.run(self.architect.call_llm(
result = self._run_async(self.architect.call_llm(
"You are an expert story architect. Create vivid scene descriptions.",
user_prompt,
))
@@ -491,8 +503,7 @@ For each: name, description, POV character, location, purpose.
"""Create style guide."""
print("🎨 STYLE GUIDE...")
import asyncio
result = asyncio.run(self.voice.execute(
result = self._run_async(self.voice.execute(
{"genre": self.genre, "tone": "neutral", "target_audience": "adult readers"},
{},
))
@@ -544,7 +555,7 @@ For each: name, description, POV character, location, purpose.
## Chapter plan: {plan.summary if plan else 'Continue the story'}
"""
result = asyncio.run(self.voice.write_chapter(
result = self._run_async(self.voice.write_chapter(
{
"chapter_number": chapter_num,
"title": f"Chapter {chapter_num}",
@@ -575,7 +586,7 @@ For each: name, description, POV character, location, purpose.
print(f"🔍 Critiquing chapter {chapter_num}...")
import asyncio
result = asyncio.run(self.editor.review_chapter(
result = self._run_async(self.editor.review_chapter(
{
"chapter_number": chapter_num,
"title": f"Chapter {chapter_num}",
opus_orchestrator/utils/llm_sync.py
+142
View File
@@ -0,0 +1,142 @@
"""LLM client for Opus Orchestrator - Synchronous version.
Uses synchronous httpx to avoid event loop issues with LangGraph.
"""
import os
from typing import Any, Optional
import requests
class LLMClient:
"""Synchronous LLM client for making API calls."""
def __init__(
self,
api_key: Optional[str] = None,
provider: str = "openai",
model: str = "gpt-4o",
base_url: Optional[str] = None,
):
"""Initialize LLM client."""
self.api_key = api_key or os.environ.get("MINIMAX_API_KEY") or os.environ.get("OPENAI_API_KEY")
self.provider = provider
self.model = model
if base_url:
self.base_url = base_url
elif provider == "minimax":
self.base_url = "https://api.minimax.chat/v1"
elif provider == "openai":
self.base_url = "https://api.openai.com/v1"
else:
self.base_url = "https://api.openai.com/v1"
def complete(
self,
system_prompt: str,
user_prompt: str,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> str:
"""Make a completion request (synchronous)."""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
if self.provider == "minimax":
return self._complete_minimax(
system_prompt, user_prompt, temperature, max_tokens, headers
)
elif self.provider == "openai":
return self._complete_openai(
system_prompt, user_prompt, temperature, max_tokens, headers
)
else:
raise ValueError(f"Unsupported provider: {self.provider}")
def _complete_minimax(
self,
system_prompt: str,
user_prompt: str,
temperature: float,
max_tokens: Optional[int],
headers: dict,
) -> str:
"""Call MiniMax API (synchronous)."""
minimax_model = self.model.split("/")[-1] if "/" in self.model else self.model
payload = {
"model": minimax_model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
"temperature": temperature,
}
if max_tokens:
payload["max_tokens"] = max_tokens
response = requests.post(
f"{self.base_url}/text/chatcompletion_v2",
headers=headers,
json=payload,
timeout=120,
)
response.raise_for_status()
data = response.json()
if "choices" in data:
return data["choices"][0]["message"]["content"]
else:
raise Exception(f"Unexpected MiniMax response: {data}")
def _complete_openai(
self,
system_prompt: str,
user_prompt: str,
temperature: float,
max_tokens: Optional[int],
headers: dict,
) -> str:
"""Call OpenAI API (synchronous)."""
payload = {
"model": self.model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
"temperature": temperature,
}
if max_tokens:
payload["max_tokens"] = max_tokens
response = requests.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=120,
)
response.raise_for_status()
data = response.json()
return data["choices"][0]["message"]["content"]
# Convenience function
def get_llm_client(config: Optional[Any] = None) -> LLMClient:
"""Get an LLM client from config."""
from opus_orchestrator.config import get_config
cfg = config or get_config()
return LLMClient(
api_key=cfg.agent.api_key,
provider=cfg.agent.provider,
model=cfg.agent.model,
)