33488fd634
Backend: - FastAPI WebSocket /ws endpoint with streaming via LangGraph astream - LangGraph Supervisor connecting 3 mock agents (order_lookup, order_actions, fallback) - YAML Agent Registry with Pydantic validation and immutable configs - PostgresSaver checkpoint persistence via langgraph-checkpoint-postgres - Session TTL with 30-min sliding window and interrupt extension - LLM provider abstraction (Anthropic/OpenAI/Google) - Token usage + cost tracking callback handler - Input validation: message size cap, thread_id format, content length - Security: no hardcoded defaults, startup API key validation, no input reflection Frontend: - React 19 + TypeScript + Vite chat UI - WebSocket hook with reconnect + exponential backoff - Streaming token display with agent attribution - Interrupt approval/reject UI for write operations - Collapsible tool call viewer Testing: - 87 unit tests, 87% coverage (exceeds 80% requirement) - Ruff lint + format clean Infrastructure: - Docker Compose (PostgreSQL 16 + backend) - pyproject.toml with full dependency management
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
"""LangGraph Supervisor construction -- connects registry, agents, LLM, and persistence."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import TYPE_CHECKING
|
|
|
|
from langgraph.prebuilt import create_react_agent
|
|
from langgraph_supervisor import create_supervisor
|
|
|
|
from app.agents import get_tools_by_names
|
|
|
|
if TYPE_CHECKING:
|
|
from langchain_core.language_models import BaseChatModel
|
|
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
|
from langgraph.graph.state import CompiledStateGraph
|
|
|
|
from app.registry import AgentRegistry
|
|
|
|
SUPERVISOR_PROMPT = (
|
|
"You are a customer support supervisor. "
|
|
"Route customer requests to the appropriate agent based on their description. "
|
|
"For order status and tracking queries, use the order_lookup agent. "
|
|
"For order modifications like cancellations, use the order_actions agent. "
|
|
"For anything else, use the fallback agent."
|
|
)
|
|
|
|
|
|
def build_agent_nodes(
|
|
registry: AgentRegistry,
|
|
llm: BaseChatModel,
|
|
) -> list:
|
|
"""Create LangGraph react agent nodes from registry configurations."""
|
|
agent_nodes = []
|
|
for agent_config in registry.list_agents():
|
|
tools = get_tools_by_names(agent_config.tools)
|
|
|
|
system_prompt = (
|
|
f"You are the {agent_config.name} agent. "
|
|
f"Personality: {agent_config.personality.tone}. "
|
|
f"{agent_config.description} "
|
|
f"Permission level: {agent_config.permission}."
|
|
)
|
|
|
|
agent_node = create_react_agent(
|
|
model=llm,
|
|
tools=tools,
|
|
name=agent_config.name,
|
|
prompt=system_prompt,
|
|
)
|
|
agent_nodes.append(agent_node)
|
|
|
|
return agent_nodes
|
|
|
|
|
|
def build_graph(
|
|
registry: AgentRegistry,
|
|
llm: BaseChatModel,
|
|
checkpointer: AsyncPostgresSaver,
|
|
) -> CompiledStateGraph:
|
|
"""Build and compile the LangGraph supervisor graph."""
|
|
agent_nodes = build_agent_nodes(registry, llm)
|
|
|
|
workflow = create_supervisor(
|
|
agent_nodes,
|
|
model=llm,
|
|
prompt=SUPERVISOR_PROMPT,
|
|
output_mode="full_history",
|
|
)
|
|
|
|
return workflow.compile(checkpointer=checkpointer)
|