feat: Add Alateya, Clan, Eonarch agents + fix gateway-router connection
## Agents Added - Alateya: R&D, biotech, innovations - Clan (Spirit): Community spirit agent - Eonarch: Consciousness evolution agent ## Changes - docker-compose.node1.yml: Added tokens for all 3 new agents - gateway-bot/http_api.py: Added configs and webhook endpoints - gateway-bot/clan_prompt.txt: New prompt file - gateway-bot/eonarch_prompt.txt: New prompt file ## Fixes - Fixed ROUTER_URL from :9102 to :8000 (internal container port) - All 9 Telegram agents now working ## Documentation - Created PROJECT-MASTER-INDEX.md - single entry point - Added various status documents and scripts Tokens configured: - Helion, NUTRA, Agromatrix (existing) - Alateya, Clan, Eonarch (new) - Druid, GreenFood, DAARWIZZ (configured)
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Apple
@@ -8,7 +8,7 @@ DAARION Memory Service - FastAPI Application
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"""
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from contextlib import asynccontextmanager
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from typing import List, Optional
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from fastapi import Depends
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from fastapi import Depends, BackgroundTasks
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from uuid import UUID
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import structlog
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from fastapi import FastAPI, HTTPException, Query
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@@ -573,6 +573,323 @@ async def delete_fact(
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raise HTTPException(status_code=500, detail=str(e))
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# ============================================================================
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# AGENT MEMORY (Gateway compatibility endpoint)
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# ============================================================================
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class AgentMemoryRequest(BaseModel):
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"""Request format from Gateway for saving chat history"""
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agent_id: str
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team_id: Optional[str] = None
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channel_id: Optional[str] = None
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user_id: str
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# Support both formats: new (content) and gateway (body_text)
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content: Optional[str] = None
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body_text: Optional[str] = None
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role: str = "user" # user, assistant, system
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# Support both formats: metadata and body_json
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metadata: Optional[dict] = None
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body_json: Optional[dict] = None
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context: Optional[str] = None
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scope: Optional[str] = None
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kind: Optional[str] = None # "message", "event", etc.
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def get_content(self) -> str:
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"""Get content from either field"""
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return self.content or self.body_text or ""
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def get_metadata(self) -> dict:
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"""Get metadata from either field"""
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return self.metadata or self.body_json or {}
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@app.post("/agents/{agent_id}/memory")
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async def save_agent_memory(agent_id: str, request: AgentMemoryRequest, background_tasks: BackgroundTasks):
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"""
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Save chat turn to memory with full ingestion pipeline:
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1. Save to PostgreSQL (facts table)
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2. Create embedding via Cohere and save to Qdrant
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3. Update Knowledge Graph in Neo4j
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"""
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try:
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from datetime import datetime
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from uuid import uuid4
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# Create a unique key for this conversation event
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timestamp = datetime.utcnow().isoformat()
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message_id = str(uuid4())
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fact_key = f"chat_event:{request.channel_id}:{timestamp}"
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# Store as a fact with JSON payload
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content = request.get_content()
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metadata = request.get_metadata()
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# Skip empty messages
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if not content or content.startswith("[Photo:"):
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logger.debug("skipping_empty_or_photo_message", content=content[:50] if content else "")
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return {"status": "ok", "event_id": None, "indexed": False}
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# Determine role from kind/body_json if not explicitly set
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role = request.role
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if request.body_json and request.body_json.get("type") == "agent_response":
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role = "assistant"
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event_data = {
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"message_id": message_id,
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"agent_id": agent_id,
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"team_id": request.team_id,
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"channel_id": request.channel_id,
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"user_id": request.user_id,
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"role": role,
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"content": content,
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"metadata": metadata,
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"scope": request.scope,
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"kind": request.kind,
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"timestamp": timestamp
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}
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# 1. Save to PostgreSQL (isolated by agent_id)
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await db.ensure_facts_table()
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result = await db.upsert_fact(
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user_id=request.user_id,
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fact_key=fact_key,
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fact_value_json=event_data,
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team_id=request.team_id,
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agent_id=agent_id # Agent isolation
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)
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logger.info("agent_memory_saved",
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agent_id=agent_id,
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user_id=request.user_id,
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role=role,
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channel_id=request.channel_id,
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content_len=len(content))
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# 2. Index in Qdrant (async background task)
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background_tasks.add_task(
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index_message_in_qdrant,
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message_id=message_id,
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content=content,
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agent_id=agent_id,
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user_id=request.user_id,
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channel_id=request.channel_id,
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role=role,
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timestamp=timestamp
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)
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# 3. Update Neo4j graph (async background task)
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background_tasks.add_task(
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update_neo4j_graph,
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message_id=message_id,
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content=content,
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agent_id=agent_id,
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user_id=request.user_id,
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channel_id=request.channel_id,
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role=role
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)
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return {
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"status": "ok",
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"event_id": result.get("fact_id") if result else None,
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"message_id": message_id,
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"indexed": True
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}
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except Exception as e:
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logger.error("agent_memory_save_failed", error=str(e), agent_id=agent_id)
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raise HTTPException(status_code=500, detail=str(e))
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async def index_message_in_qdrant(
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message_id: str,
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content: str,
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agent_id: str,
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user_id: str,
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channel_id: str,
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role: str,
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timestamp: str
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):
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"""Index message in Qdrant for semantic search (isolated by agent_id)"""
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try:
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from .embedding import get_document_embeddings
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from qdrant_client.http import models as qmodels
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# Skip very short messages
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if len(content) < 10:
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return
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# Generate embedding
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embeddings = await get_document_embeddings([content])
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if not embeddings or not embeddings[0]:
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logger.warning("embedding_failed", message_id=message_id)
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return
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vector = embeddings[0]
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# Use agent-specific collection (isolation!)
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collection_name = f"{agent_id}_messages"
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# Ensure collection exists
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try:
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vector_store.client.get_collection(collection_name)
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except Exception:
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# Create collection if not exists
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vector_store.client.create_collection(
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collection_name=collection_name,
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vectors_config=qmodels.VectorParams(
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size=len(vector),
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distance=qmodels.Distance.COSINE
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)
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)
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logger.info("created_collection", collection=collection_name)
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# Save to agent-specific Qdrant collection
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vector_store.client.upsert(
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collection_name=collection_name,
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points=[
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qmodels.PointStruct(
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id=message_id,
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vector=vector,
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payload={
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"message_id": message_id,
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"agent_id": agent_id,
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"user_id": user_id,
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"channel_id": channel_id,
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"role": role,
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"content": content,
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"timestamp": timestamp,
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"type": "chat_message"
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}
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)
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]
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)
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logger.info("message_indexed_qdrant",
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message_id=message_id,
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collection=collection_name,
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content_len=len(content),
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vector_dim=len(vector))
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except Exception as e:
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logger.error("qdrant_indexing_failed", error=str(e), message_id=message_id)
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async def update_neo4j_graph(
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message_id: str,
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content: str,
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agent_id: str,
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user_id: str,
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channel_id: str,
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role: str
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):
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"""Update Knowledge Graph in Neo4j (with agent isolation)"""
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try:
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import httpx
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import os
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neo4j_url = os.getenv("NEO4J_HTTP_URL", "http://neo4j:7474")
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neo4j_user = os.getenv("NEO4J_USER", "neo4j")
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neo4j_password = os.getenv("NEO4J_PASSWORD", "DaarionNeo4j2026!")
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# Create/update User node and Message relationship
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# IMPORTANT: agent_id is added to relationships for filtering
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cypher = """
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MERGE (u:User {user_id: $user_id})
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ON CREATE SET u.created_at = datetime()
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ON MATCH SET u.last_seen = datetime()
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MERGE (ch:Channel {channel_id: $channel_id})
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ON CREATE SET ch.created_at = datetime()
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MERGE (a:Agent {agent_id: $agent_id})
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ON CREATE SET a.created_at = datetime()
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MERGE (u)-[p:PARTICIPATES_IN {agent_id: $agent_id}]->(ch)
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ON CREATE SET p.first_seen = datetime()
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ON MATCH SET p.last_seen = datetime()
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CREATE (m:Message {
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message_id: $message_id,
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role: $role,
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content_preview: $content_preview,
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agent_id: $agent_id,
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created_at: datetime()
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})
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CREATE (u)-[:SENT {agent_id: $agent_id}]->(m)
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CREATE (m)-[:IN_CHANNEL {agent_id: $agent_id}]->(ch)
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CREATE (m)-[:HANDLED_BY]->(a)
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RETURN m.message_id as id
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"""
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.post(
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f"{neo4j_url}/db/neo4j/tx/commit",
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auth=(neo4j_user, neo4j_password),
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json={
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"statements": [{
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"statement": cypher,
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"parameters": {
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"user_id": user_id,
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"channel_id": channel_id,
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"message_id": message_id,
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"role": role,
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"content_preview": content[:200] if content else "",
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"agent_id": agent_id
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}
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}]
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}
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)
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if response.status_code == 200:
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logger.info("neo4j_graph_updated", message_id=message_id, user_id=user_id, agent_id=agent_id)
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else:
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logger.warning("neo4j_update_failed",
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status=response.status_code,
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response=response.text[:200])
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except Exception as e:
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logger.error("neo4j_update_error", error=str(e), message_id=message_id)
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@app.get("/agents/{agent_id}/memory")
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async def get_agent_memory(
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agent_id: str,
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user_id: str = Query(...),
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channel_id: Optional[str] = None,
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limit: int = Query(default=20, le=100)
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):
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"""
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Get recent chat events for an agent/user (isolated by agent_id).
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"""
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import json as json_lib
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try:
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# Query facts filtered by agent_id (database-level filtering)
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facts = await db.list_facts(user_id=user_id, agent_id=agent_id, limit=limit)
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# Filter for chat events from this channel
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events = []
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for fact in facts:
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if fact.get("fact_key", "").startswith("chat_event:"):
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# Handle fact_value_json being string or dict
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event_data = fact.get("fact_value_json", {})
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if isinstance(event_data, str):
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try:
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event_data = json_lib.loads(event_data)
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except:
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event_data = {}
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if not isinstance(event_data, dict):
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event_data = {}
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if channel_id is None or event_data.get("channel_id") == channel_id:
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events.append(event_data)
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return {"events": events[:limit]}
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except Exception as e:
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logger.error("agent_memory_get_failed", error=str(e), agent_id=agent_id)
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raise HTTPException(status_code=500, detail=str(e))
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# ============================================================================
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# ADMIN
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# ============================================================================
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