Feature/unified agent endpoint

pyproject.toml

@@ -14,6 +14,7 @@ dependencies = [
     "requests>=2.0, <3",
     "types-requests>=2.0, <3",
     "mcp>=1.6.0, <2; python_version >= '3.10'",
+
 ]
 classifiers = [
     "Typing :: Typed",

src/agents/agent_onboarding.py

@@ -0,0 +1,83 @@
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from fastapi import APIRouter, Request
+from agents import Agent, Runner
+from datetime import datetime
+import json
+import httpx
+
+router = APIRouter()
+
+# Define the onboarding agent
+onboarding_agent = Agent(
+    name="OnboardingAgent",
+    instructions="""
+You are an onboarding assistant helping new influencers introduce themselves.
+Your job is to:
+1. Gently ask about their interests, content goals, and style.
+2. Summarize their early profile with a few soft fields.
+3. Optionally ask a clarifying follow-up question if needed.
+
+Return your response in the following format:
+{
+  "output_type": "soft_profile",
+  "contains_image": false,
+  "details": {
+    "interests": ["wellness", "fitness"],
+    "preferred_style": "authentic, relaxed",
+    "content_goals": "collaborations, brand storytelling",
+    "next_question": "Would you consider doing live sessions?"
+  }
+}
+Only reply in this format.
+"""
+)
+
+@router.post("/onboard")
+async def onboard_influencer(request: Request):
+    data = await request.json()
+    user_input = data.get("input", "")
+    user_id = data.get("user_id", "anonymous")
+    webhook_url = data.get("webhook_url")
+    debug_info = {}
+
+    result = await Runner.run(onboarding_agent, input=user_input)
+
+    try:
+        parsed_output = json.loads(result.final_output)
+        output_type = parsed_output.get("output_type")
+        output_details = parsed_output.get("details")
+        contains_image = parsed_output.get("contains_image", False)
+
+        if not output_type or not output_details:
+            raise ValueError("Missing required output keys")
+    except Exception as e:
+        parsed_output = None
+        output_type = "raw_text"
+        output_details = result.final_output
+        contains_image = False
+        debug_info["validation_error"] = str(e)
+        debug_info["raw_output"] = result.final_output
+
+    session = {
+        "agent_type": "onboarding",
+        "user_id": user_id,
+        "output_type": output_type,
+        "contains_image": contains_image,
+        "output_details": output_details,
+        "created_at": datetime.utcnow().isoformat(),
+    }
+
+    if debug_info:
+        session["debug_info"] = debug_info
+
+    if webhook_url:
+        async with httpx.AsyncClient() as client:
+            try:
+                await client.post(webhook_url, json=session)
+            except Exception as e:
+                session["webhook_error"] = str(e)
+
+    return session

src/agents/agent_profilebuilder.py

@@ -0,0 +1,100 @@
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from fastapi import APIRouter, Request
+from agents import Agent, Runner
+from agents.tool import WebSearchTool
+from datetime import datetime
+import json
+import httpx
+
+router = APIRouter()
+
+# Predefined Bubble webhook URL
+WEBHOOK_URL = "https://helpmeaiai.bubbleapps.io/version-test/api/1.1/wf/openai_profilebuilder_return"
+
+# ProfileBuilder agent skeleton; tools will be set per-request for dynamic locale/fallback
+profile_builder_agent = Agent(
+    name="ProfileBuilderAgent",
+    instructions="""
+You are a profile builder assistant with web search capability.
+
+You will receive a set of key-value inputs (e.g., profile_uuid, handle URL, etc.).
+Your job:
+1. Use the provided fields (including fallback follower count if given).
+2. If a locale is provided, use it to tailor the web search tool's user_location.
+3. Perform web searches and reasoning to determine follower_count, posting_style, industry, engagement_rate, and any notable public context.
+4. Summarize this into JSON as follows:
+{
+  "output_type": "structured_profile",
+  "contains_image": false,
+  "details": {
+    "profile_uuid": "...",
+    "summary": "Concise profile summary...",
+    "prompt_snippet": { "tone": "...", "goal": "...", "platform": "..." },
+    "follower_count": 12345,
+    "posting_style": "...",
+    "industry": "...",
+    "engagement_rate": "...",
+    "additional_context": "..."
+  }
+}
+Only return JSON with exactly these fields—no markdown or commentary.
+""",
+    tools=[]
+)
+
+@router.post("/profilebuilder")
+async def build_profile(request: Request):
+    data = await request.json()
+    # Extract core identifiers and optional fallbacks
+    profile_uuid = data.pop("profile_uuid", None)
+    provided_fc = data.pop("provided_follower_count", None)
+    locale_text = data.pop("locale", None)
+
+    # Build tool list dynamically based on locale
+    user_loc = {"type": "approximate", "region": locale_text} if locale_text else None
+    tools = [WebSearchTool(user_location=user_loc, search_context_size="low")]
+    profile_builder_agent.tools = tools
+
+    # Flatten remaining inputs into prompt lines
+    prompt_lines = []
+    for key, val in data.items():
+        if val not in (None, "", "null"):
+            prompt_lines.append(f"{key}: {val}")
+    if provided_fc is not None:
+        prompt_lines.append(f"Provided follower count: {provided_fc}")
+
+    # Construct the agent prompt
+    agent_input = f"Profile UUID: {profile_uuid}\n" + "\n".join(prompt_lines)
+
+    # Invoke the agent
+    result = await Runner.run(profile_builder_agent, input=agent_input)
+
+    # Clean markdown fences
+    output = result.final_output.strip()
+    if output.startswith("```") and output.endswith("```"):
+        output = output.split("\n", 1)[-1].rsplit("\n", 1)[0]
+
+    # Parse agent JSON response
+    try:
+        parsed = json.loads(output)
+        details = parsed.get("details", {})
+    except Exception:
+        details = {}
+
+    # Build profile_data payload dynamically
+    profile_data = {"profile_uuid": profile_uuid}
+    for k, v in details.items():
+        profile_data[k] = v
+    profile_data["created_at"] = datetime.utcnow().isoformat()
+
+    # Post to Bubble webhook
+    async with httpx.AsyncClient() as client:
+        try:
+            await client.post(WEBHOOK_URL, json=profile_data)
+        except Exception as e:
+            profile_data["webhook_error"] = str(e)
+
+    return profile_data

src/agents/agent_server.py

@@ -0,0 +1,185 @@
+# File: src/agents/agent_server.py
+
+import sys
+import os
+from dotenv import load_dotenv
+
+# 1) Load environment variables from .env
+load_dotenv()
+
+# 2) Ensure src/ is on the Python path so “util” is importable
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import parse_obj_as, ValidationError
+import json
+from datetime import datetime
+import httpx
+
+# 3) Core SDK imports
+from agents import Agent, Runner, tool
+
+# 4) Pydantic schemas and service handlers
+from util.schemas import Inbound               # Union of NewTask, NewMessage
+from util.services import handle_new_task, handle_new_message
+
+# ───────────────────────────────────────────────────────────
+# 5) Agent definitions (Phase 1: keep here for simplicity)
+# ───────────────────────────────────────────────────────────
+# Manager: routes requests or asks for clarifications
+manager_agent = Agent(
+    name="Manager",
+    instructions="""
+You are an intelligent router for user requests.
+Decide the intent behind the message: strategy, content, repurpose, feedback.
+If you are unsure or need more info, ask a clarifying question instead of routing.
+Respond in strict JSON like:
+{ "route_to": "strategy", "reason": "User wants a campaign plan" }
+"""
+)
+
+# Strategy: builds a 7‑day social campaign plan
+strategy_agent = Agent(
+    name="StrategyAgent",
+    instructions="""
+You create clear, actionable 7-day social media campaign strategies.
+If user input is unclear or missing platform, audience, or tone — ask for clarification.
+Respond in structured JSON like:
+{
+  "output_type": "strategy_plan",
+  "contains_image": false,
+  "details": {
+    "days": [
+      { "title": "...", "theme": "...", "cta": "..." }
+    ]
+  }
+}
+Only return JSON in this format.
+""",
+    tools=[]
+)
+
+# Content: writes social post variants
+content_agent = Agent(
+    name="ContentAgent",
+    instructions="""
+You write engaging, brand-aligned social content.
+If user input lacks platform or goal, ask for clarification.
+Return post drafts in this JSON format:
+{
+  "output_type": "content_variants",
+  "contains_image": false,
+  "details": {
+    "variants": [
+      {
+        "platform": "Instagram",
+        "caption": "...",
+        "hook": "...",
+        "cta": "..."
+      }
+    ]
+  }
+}
+Only respond in this format.
+""",
+    tools=[]
+)
+
+# Repurpose: converts posts into new formats
+repurpose_agent = Agent(
+    name="RepurposeAgent",
+    instructions="""
+You convert existing posts into new formats for different platforms.
+Respond using this format:
+{
+  "output_type": "repurposed_posts",
+  "contains_image": false,
+  "details": {
+    "original": "...",
+    "repurposed": [
+      {
+        "platform": "...",
+        "caption": "...",
+        "format": "..."
+      }
+    ]
+  }
+}
+""",
+    tools=[]
+)
+
+# Feedback: critiques content and suggests improvements
+feedback_agent = Agent(
+    name="FeedbackAgent",
+    instructions="""
+You evaluate content and offer improvements.
+Respond in this structured format:
+{
+  "output_type": "content_feedback",
+  "contains_image": false,
+  "details": {
+    "original": "...",
+    "feedback": "...",
+    "suggested_edit": "..."
+  }
+}
+""",
+    tools=[]
+)
+
+# Map Manager’s routing keys to Agent instances
+AGENT_MAP = {
+    "strategy":  strategy_agent,
+    "content":   content_agent,
+    "repurpose": repurpose_agent,
+    "feedback":  feedback_agent,
+}
+# ───────────────────────────────────────────────────────────
+
+# 6) Instantiate FastAPI
+app = FastAPI()
+
+# 7) CORS middleware (adjust allow_origins as needed)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 8) Include your existing agent routers
+from .agent_onboarding import router as onboarding_router
+from .agent_profilebuilder import router as profilebuilder_router
+
+app.include_router(onboarding_router)
+app.include_router(profilebuilder_router)
+
+# 9) Unified /agent endpoint
+@app.post("/agent")
+async def agent_endpoint(request: Request):
+    """
+    Handles all client calls:
+      - action = "new_task"
+      - action = "new_message"
+      - future actions as you add them to Inbound
+    """
+    body = await request.json()
+    try:
+        payload = parse_obj_as(Inbound, body)
+    except ValidationError as e:
+        raise HTTPException(status_code=400, detail=e.errors())
+
+    if payload.action == "new_task":
+        return await handle_new_task(payload)
+
+    elif payload.action == "new_message":
+        return await handle_new_message(payload)
+
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported action: {payload.action}"
+        )

src/agents/util/schemas.py

@@ -0,0 +1,17 @@
+from typing import Literal, Optional, Dict, Union
+from pydantic import BaseModel, Field
+
+class NewTask(BaseModel):
+    action: Literal["new_task"]
+    task_type: str
+    user_prompt: str
+    params: Dict = Field(default_factory=dict)
+    first_agent: Optional[str] = "auto"
+
+class NewMessage(BaseModel):
+    action: Literal["new_message"]
+    task_id: str
+    message: str
+    agent_session_id: Optional[str] = None
+
+Inbound = Union[NewTask, NewMessage]