chore: resolve .env.example merge conflict; keep feat/pluggable-graph-backend version

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.env.example

@@ -1,29 +1,55 @@
-# LLM API configuration (supports any OpenAI-compatible API)
-# Recommended: Alibaba Qwen via Bailian platform: https://bailian.console.aliyun.com/
-# Note: high token consumption — start with simulations under 40 rounds
+# ===== LLM Configuration =====
+# Any OpenAI-compatible API is supported
 LLM_API_KEY=your_api_key_here
 LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 LLM_MODEL_NAME=qwen-plus
 
-# ===== Graph backend selection =====
-# Options: "zep" (default, cloud-managed) or "graphiti" (self-hosted Neo4j)
+# --- Gemini (Google AI Studio) ---
+# Set LLM_PROVIDER=gemini to auto-configure the Google AI Studio endpoint.
+# Get a free API key at: https://aistudio.google.com/
+# LLM_PROVIDER=gemini
+# LLM_API_KEY=AIza...
+# LLM_MODEL_NAME=gemini-3.1-flash-lite-preview
+
+# ===== Graph Backend =====
+# GRAPH_BACKEND=zep      (default) — Zep Cloud managed memory graph
+# GRAPH_BACKEND=graphiti — Self-hosted Neo4j + Graphiti (requires graphiti extras)
 GRAPH_BACKEND=zep
 
-# ===== Zep Cloud configuration (required when GRAPH_BACKEND=zep) =====
-# Free monthly quota is sufficient for basic use: https://app.getzep.com/
+# --- Zep Cloud (default backend) ---
+# Free tier available: https://app.getzep.com/
 ZEP_API_KEY=your_zep_api_key_here
 
-# ===== Graphiti + Neo4j configuration (required when GRAPH_BACKEND=graphiti) =====
+# --- Graphiti + Neo4j (alternative backend) ---
+# Install extras: pip install "mirofish-backend[graphiti]"
 # NEO4J_URI=bolt://localhost:7687
 # NEO4J_USER=neo4j
 # NEO4J_PASSWORD=your_neo4j_password_here
-# NEO4J_DATABASE=neo4j
+# GRAPHITI_BATCH_SIZE=10  # chunks per bulk call; higher = faster but more LLM parallelism
 
-# ===== Boost LLM configuration (optional) =====
-# If not using a boost LLM, remove or leave these variables empty
-LLM_BOOST_API_KEY=
-LLM_BOOST_BASE_URL=
-LLM_BOOST_MODEL_NAME=
+# --- Embedding LLM (used by Graphiti for vector indexing) ---
+# Falls back to LLM_API_KEY / LLM_BASE_URL if not set.
+# Use a dedicated embedding deployment when your LLM_BASE_URL points to a generative model.
+# LLM_EMBED_API_KEY=your_embed_api_key_here
+# LLM_EMBED_BASE_URL=https://<resource>.cognitiveservices.azure.com/openai/deployments/<embed-deployment>/embeddings?api-version=2024-05-01-preview
+# LLM_EMBED_MODEL_NAME=text-embedding-3-small
+
+# --- Small/fast LLM (used by Graphiti for reranking and lightweight tasks) ---
+# Falls back to LLM_API_KEY / LLM_BASE_URL / LLM_MODEL_NAME if not set.
+# Use a cheaper model (e.g. gpt-4o-mini, gpt-5-mini) to reduce costs.
+# LLM_SMALL_API_KEY=your_small_api_key_here
+# LLM_SMALL_BASE_URL=https://<resource>.cognitiveservices.azure.com/openai/deployments/<small-model>/chat/completions?api-version=2024-05-01-preview
+# LLM_SMALL_MODEL_NAME=gpt-4o-mini
+
+# ===== Ontology limits =====
+# ONTOLOGY_MAX_ENTITY_TYPES=12  # total entity types (specific + 2 fallback); Zep Cloud max is 10 specific + 2 = 12
+# ONTOLOGY_MAX_EDGE_TYPES=10    # max relationship types; Zep Cloud max is 10
+
+# ===== Boost LLM (optional) =====
+# Secondary faster LLM — omit these lines entirely if not used
+LLM_BOOST_API_KEY=your_api_key_here
+LLM_BOOST_BASE_URL=your_base_url_here
+LLM_BOOST_MODEL_NAME=your_model_name_here
 
 # ===== Authentication =====
 # Password for the "demo" user — REQUIRED in production

CLAUDE.md

@@ -0,0 +1,80 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+MiroFish is an AI-powered multi-agent simulation platform. It extracts entities from user-provided documents (PDF/MD/TXT), builds a knowledge graph (via Zep Cloud), generates agent personas, runs social interaction simulations (via OASIS/CAMEL-AI), and produces analytical reports.
+
+## Commands
+
+### Setup
+```bash
+cp .env.example .env          # Configure API keys before first run
+npm run setup:all             # Install Node + Python dependencies (root, frontend, backend)
+npm run setup                 # Node deps only
+npm run setup:backend         # Python deps only (uv venv)
+```
+
+### Development
+```bash
+npm run dev                   # Start both frontend (port 3000) & backend (port 5001) concurrently
+npm run frontend              # Frontend only
+npm run backend               # Backend only (uv run python run.py)
+```
+
+### Build
+```bash
+npm run build                 # Vite production build of frontend
+```
+
+### Testing
+```bash
+pytest                        # Run Python tests (pytest + pytest-asyncio available in venv)
+```
+
+Python 3.11–3.12 required (strict constraint). Node 18+ required.
+
+## Architecture
+
+### Overview
+Full-stack monorepo: **Vue 3 SPA** (frontend, port 3000) + **Flask API** (backend, port 5001). Vite proxies all `/api/*` requests to the backend.
+
+### 5-Step Workflow Pipeline
+1. **Graph Build** — Upload seed documents → entity/relationship extraction via LLM → Zep Cloud knowledge graph
+2. **Environment Setup** — Agent persona generation (OASIS profiles) from the graph
+3. **Simulation** — OASIS multi-agent simulation (Twitter + Reddit platforms) run as a subprocess
+4. **Report** — ReportAgent (LLM with tool calling) analyzes simulation output
+5. **Interaction** — Live chat with simulated agents
+
+### Key Backend Patterns
+- **`models/project.py`** — `ProjectManager` singleton. Projects persist as `uploads/projects/{uuid}/project.json` + files. The server is the source of truth; frontend only holds a `projectId`.
+- **`models/task.py`** — `TaskManager` singleton. In-memory async task tracking (PENDING → PROCESSING → COMPLETED|FAILED). Frontend polls `GET /api/.../task/{taskId}`.
+- **`services/simulation_runner.py`** — Spawns OASIS as a subprocess. Communicates via IPC files at `/tmp/mirofish_sim_{id}_*.json`. Atexit cleanup registered.
+- **`services/report_agent.py`** — Multi-turn LLM agent with tool use (Zep queries). Max 5 tool calls, 2 reflection rounds.
+- **`utils/locale.py`** — Thread-local locale storage. Reads `Accept-Language` header from requests; falls back to thread-local for background workers (default: `zh`).
+
+### Key Frontend Patterns
+- **`api/index.js`** — Axios instance with retry (`requestWithRetry`, 3 attempts, exponential backoff) and response interceptor. Auto-injects `Accept-Language` header from current locale.
+- **`store/pendingUpload.js`** — Lightweight reactive state (no Vuex/Pinia) for deferred file uploads between views.
+- Views are self-contained; no shared state beyond `projectId` in the URL route.
+
+### i18n
+Translation files at `/locales/{en,zh}.json` are shared by both frontend and backend. The frontend uses `vue-i18n` v11 with `localStorage` persistence. The backend reads the `Accept-Language` header. `/locales/languages.json` also contains per-language LLM prompt instructions (to force LLM output language).
+
+### Configuration (`backend/app/config.py`)
+Required environment variables (from `.env`):
+- `LLM_API_KEY`, `LLM_BASE_URL`, `LLM_MODEL_NAME` — any OpenAI-compatible API (default: Qwen-plus via Alibaba Bailian)
+- `ZEP_API_KEY` — Zep Cloud memory graph
+- Optional: `LLM_BOOST_API_KEY`, `LLM_BOOST_BASE_URL`, `LLM_BOOST_MODEL_NAME` — faster secondary LLM
+
+## Git Remotes
+- `origin` — this fork: `https://github.com/jaumemir/MiroFish`
+- `upstream` — original project: `https://github.com/666ghj/MiroFish`
+
+To cherry-pick from upstream branches or PRs:
+```bash
+git fetch upstream
+git cherry-pick <commit-sha>
+# or: git merge upstream/<branch-name>
+```

Dockerfile

@@ -28,7 +28,8 @@ WORKDIR /app
 
 # Copiar i instal·lar dependències Python (aprofita caché si pyproject.toml no canvia)
 COPY backend/pyproject.toml backend/uv.lock ./backend/
-RUN cd backend && uv sync --frozen --no-dev
+# Install all optional extras so the image supports any GRAPH_BACKEND at runtime
+RUN cd backend && uv sync --frozen --no-dev --extra graphiti
 
 # Copiar el codi font del backend i els fitxers compartits
 COPY backend/ ./backend/

azure/2-build-deploy.sh

@@ -29,8 +29,18 @@ REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
 # ── Validar variables obligatòries ───────────────────────────────────────────
 REQUIRED_VARS=(
   AZURE_SUBSCRIPTION_ID RESOURCE_GROUP PROJECT_NAME
-  DEMO_PASSWORD SECRET_KEY LLM_API_KEY LLM_BASE_URL LLM_MODEL_NAME ZEP_API_KEY
+  DEMO_PASSWORD SECRET_KEY LLM_API_KEY LLM_BASE_URL LLM_MODEL_NAME
 )
+# Validate graph backend config
+GRAPH_BACKEND="${GRAPH_BACKEND:-zep}"
+if [[ "$GRAPH_BACKEND" == "zep" && -z "${ZEP_API_KEY:-}" ]]; then
+  echo "ERROR: ZEP_API_KEY is required when GRAPH_BACKEND=zep"
+  exit 1
+fi
+if [[ "$GRAPH_BACKEND" == "graphiti" && -z "${NEO4J_PASSWORD:-}" ]]; then
+  echo "ERROR: NEO4J_PASSWORD is required when GRAPH_BACKEND=graphiti"
+  exit 1
+fi
 for var in "${REQUIRED_VARS[@]}"; do
   if [[ -z "${!var:-}" ]]; then
     echo "ERROR: La variable $var no està configurada a config.sh"
@@ -124,7 +134,12 @@ DEPLOY_OUTPUT=$(az deployment group create \
       demoPassword="$DEMO_PASSWORD" \
       llmApiKey="$LLM_API_KEY" \
       llmBoostApiKey="${LLM_BOOST_API_KEY:-}" \
-      zepApiKey="$ZEP_API_KEY" \
+      llmProvider="${LLM_PROVIDER:-}" \
+      zepApiKey="${ZEP_API_KEY:-}" \
+      neo4jPassword="${NEO4J_PASSWORD:-}" \
+      neo4jUri="${NEO4J_URI:-bolt://localhost:7687}" \
+      neo4jUser="${NEO4J_USER:-neo4j}" \
+      graphBackend="${GRAPH_BACKEND:-zep}" \
       secretKey="$SECRET_KEY" \
       llmBaseUrl="$LLM_BASE_URL" \
       llmModelName="$LLM_MODEL_NAME" \

azure/config.sh.example

@@ -34,14 +34,42 @@ LLM_API_KEY="<la-teva-llm-api-key>"
 LLM_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
 LLM_MODEL_NAME="qwen-plus"
 
+# Proveïdor LLM especial (opcional):
+#   gemini  → configura automàticament Google AI Studio (no cal LLM_BASE_URL)
+#   (buit)  → qualsevol API compatible OpenAI
+LLM_PROVIDER=""
+
 # ── LLM accelerador (opcional — deixar buit per desactivar) ───────────────────
 LLM_BOOST_API_KEY=""
 LLM_BOOST_BASE_URL=""
 LLM_BOOST_MODEL_NAME=""
 
-# ── Zep Cloud (graf de memòria) ───────────────────────────────────────────────
+# ── Backend de graf ───────────────────────────────────────────────────────────
+# Opcions: zep (Zep Cloud, per defecte) | graphiti (Neo4j local/Azure)
+GRAPH_BACKEND="zep"
+
+# --- Zep Cloud (si GRAPH_BACKEND=zep) ---
 ZEP_API_KEY="<la-teva-zep-api-key>"
 
+# --- Graphiti + Neo4j (si GRAPH_BACKEND=graphiti) ---
+# URI bolt del servidor Neo4j (pot ser una VM Azure, ACI, etc.)
+# NEO4J_URI="bolt://<ip-o-hostname>:7687"
+# NEO4J_USER="neo4j"
+# NEO4J_PASSWORD="<contrasenya-neo4j>"
+# GRAPHITI_BATCH_SIZE="10"  # chunks per crida bulk; més alt = més ràpid però més paral·lelisme LLM
+
+# --- Embedding LLM (usat per Graphiti per a indexació vectorial) ---
+# Si no s'estableix, fa fallback a LLM_API_KEY / LLM_BASE_URL.
+# LLM_EMBED_API_KEY="<la-teva-embed-api-key>"
+# LLM_EMBED_BASE_URL="https://<recurs>.cognitiveservices.azure.com/openai/deployments/<embed-deployment>/embeddings?api-version=2024-05-01-preview"
+# LLM_EMBED_MODEL_NAME="text-embedding-3-small"
+
+# --- Small/fast LLM (usat per Graphiti per a reranking i tasques lleugeres) ---
+# Si no s'estableix, fa fallback a LLM_API_KEY / LLM_BASE_URL / LLM_MODEL_NAME.
+# LLM_SMALL_API_KEY="<la-teva-small-api-key>"
+# LLM_SMALL_BASE_URL="https://<recurs>.cognitiveservices.azure.com/openai/deployments/<small-model>/chat/completions?api-version=2024-05-01-preview"
+# LLM_SMALL_MODEL_NAME="gpt-4o-mini"
+
 # ── Simulació OASIS (valors per defecte recomanats) ───────────────────────────
 OASIS_DEFAULT_MAX_ROUNDS="10"
 

azure/container-app.bicep

@@ -49,9 +49,13 @@ param llmApiKey string
 @secure()
 param llmBoostApiKey string = ''
 
-@description('Clau de l\'API Zep Cloud')
+@description('Clau de l\'API Zep Cloud (obligatori si GRAPH_BACKEND=zep)')
 @secure()
-param zepApiKey string
+param zepApiKey string = ''
+
+@description('Contrasenya de Neo4j (obligatori si GRAPH_BACKEND=graphiti)')
+@secure()
+param neo4jPassword string = ''
 
 @description('SECRET_KEY de Flask per a JWT (python -c "import secrets; print(secrets.token_hex(32))")')
 @secure()
@@ -65,6 +69,20 @@ param llmBaseUrl string = 'https://dashscope.aliyuncs.com/compatible-mode/v1'
 @description('Nom del model LLM principal')
 param llmModelName string = 'qwen-plus'
 
+@description('Proveïdor LLM (gemini per a Google AI Studio; buit per a qualsevol compatible OpenAI)')
+param llmProvider string = ''
+
+// ─── Paràmetres del backend de graf ──────────────────────────────────────────
+
+@description('Backend de graf: zep (Zep Cloud) o graphiti (Neo4j local/Azure)')
+param graphBackend string = 'zep'
+
+@description('URI de connexió bolt de Neo4j (necessari si GRAPH_BACKEND=graphiti)')
+param neo4jUri string = 'bolt://localhost:7687'
+
+@description('Usuari de Neo4j')
+param neo4jUser string = 'neo4j'
+
 // ─── Paràmetres LLM accelerador (opcionals) ──────────────────────────────────
 
 @description('URL base de l\'API LLM acceleradora (opcional)')
@@ -104,6 +122,7 @@ resource containerApp 'Microsoft.App/containerApps@2023-05-01' = {
         { name: 'llm-api-key',          value: llmApiKey }
         { name: 'llm-boost-api-key',    value: llmBoostApiKey }
         { name: 'zep-api-key',          value: zepApiKey }
+        { name: 'neo4j-password',       value: neo4jPassword }
         { name: 'secret-key',           value: secretKey }
       ]
 
@@ -143,14 +162,21 @@ resource containerApp 'Microsoft.App/containerApps@2023-05-01' = {
             { name: 'LLM_API_KEY',           secretRef: 'llm-api-key' }
             { name: 'LLM_BOOST_API_KEY',     secretRef: 'llm-boost-api-key' }
             { name: 'ZEP_API_KEY',           secretRef: 'zep-api-key' }
+            { name: 'NEO4J_PASSWORD',        secretRef: 'neo4j-password' }
             { name: 'SECRET_KEY',            secretRef: 'secret-key' }
 
             // ── Variables no sensibles ──
             { name: 'LLM_BASE_URL',          value: llmBaseUrl }
             { name: 'LLM_MODEL_NAME',        value: llmModelName }
+            { name: 'LLM_PROVIDER',          value: llmProvider }
             { name: 'LLM_BOOST_BASE_URL',    value: llmBoostBaseUrl }
             { name: 'LLM_BOOST_MODEL_NAME',  value: llmBoostModelName }
 
+            // ── Backend de graf ──
+            { name: 'GRAPH_BACKEND',         value: graphBackend }
+            { name: 'NEO4J_URI',             value: neo4jUri }
+            { name: 'NEO4J_USER',            value: neo4jUser }
+
             // ── Simulació OASIS ──
             { name: 'OASIS_DEFAULT_MAX_ROUNDS', value: oasisDefaultMaxRounds }
 

backend/app/api/graph.py

@@ -284,9 +284,7 @@ def build_graph():
         logger.info("=== Starting graph build ===")
 
         # Check configuration
-        errors = []
-        if not Config.ZEP_API_KEY:
-            errors.append(t('api.zepApiKeyMissing'))
+        errors = Config.get_graph_config_errors()
         if errors:
             logger.error(f"Configuration error: {errors}")
             return jsonify({
@@ -387,7 +385,7 @@ def build_graph():
                 )
                 
                 # Create graph builder service
-                builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
+                builder = GraphBuilderService()
 
                 # Split into chunks
                 task_manager.update_task(
@@ -437,10 +435,11 @@ def build_graph():
                     progress=15
                 )
                 
+                batch_size = Config.GRAPHITI_BATCH_SIZE if Config.GRAPH_BACKEND == 'graphiti' else 3
                 episode_uuids = builder.add_text_batches(
-                    graph_id, 
+                    graph_id,
                     chunks,
-                    batch_size=3,
+                    batch_size=batch_size,
                     progress_callback=add_progress_callback
                 )
                 
@@ -572,13 +571,7 @@ def get_graph_data(graph_id: str):
     Get graph data (nodes and edges)
     """
     try:
-        if not Config.ZEP_API_KEY:
-            return jsonify({
-                "success": False,
-                "error": t('api.zepApiKeyMissing')
-            }), 500
-        
-        builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
+        builder = GraphBuilderService()
         graph_data = builder.get_graph_data(graph_id)
         
         return jsonify({
@@ -600,13 +593,7 @@ def delete_graph(graph_id: str):
     Delete a Zep graph
     """
     try:
-        if not Config.ZEP_API_KEY:
-            return jsonify({
-                "success": False,
-                "error": t('api.zepApiKeyMissing')
-            }), 500
-        
-        builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
+        builder = GraphBuilderService()
         builder.delete_graph(graph_id)
         
         return jsonify({

backend/app/api/simulation.py

@@ -57,12 +57,6 @@ def get_graph_entities(graph_id: str):
         enrich: whether to fetch related edge info (default true)
     """
     try:
-        if not Config.ZEP_API_KEY:
-            return jsonify({
-                "success": False,
-                "error": t('api.zepApiKeyMissing')
-            }), 500
-        
         entity_types_str = request.args.get('entity_types', '')
         entity_types = [t.strip() for t in entity_types_str.split(',') if t.strip()] if entity_types_str else None
         enrich = request.args.get('enrich', 'true').lower() == 'true'
@@ -94,12 +88,6 @@ def get_graph_entities(graph_id: str):
 def get_entity_detail(graph_id: str, entity_uuid: str):
     """Get detailed information about a single entity"""
     try:
-        if not Config.ZEP_API_KEY:
-            return jsonify({
-                "success": False,
-                "error": t('api.zepApiKeyMissing')
-            }), 500
-        
         reader = ZepEntityReader()
         entity = reader.get_entity_with_context(graph_id, entity_uuid)
         
@@ -127,12 +115,6 @@ def get_entity_detail(graph_id: str, entity_uuid: str):
 def get_entities_by_type(graph_id: str, entity_type: str):
     """Get all entities of a specified type"""
     try:
-        if not Config.ZEP_API_KEY:
-            return jsonify({
-                "success": False,
-                "error": t('api.zepApiKeyMissing')
-            }), 500
-        
         enrich = request.args.get('enrich', 'true').lower() == 'true'
         
         reader = ZepEntityReader()

backend/app/config.py

@@ -32,10 +32,34 @@ class Config:
     LLM_API_KEY = os.environ.get('LLM_API_KEY')
     LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
     LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
+
+    # Embedding LLM (used by Graphiti for vector indexing)
+    # Falls back to LLM_* values if not set
+    LLM_EMBED_API_KEY = os.environ.get('LLM_EMBED_API_KEY') or os.environ.get('LLM_API_KEY')
+    LLM_EMBED_BASE_URL = os.environ.get('LLM_EMBED_BASE_URL') or os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
+    LLM_EMBED_MODEL_NAME = os.environ.get('LLM_EMBED_MODEL_NAME', 'text-embedding-3-small')
+
+    # Small/fast LLM (used by Graphiti for lightweight tasks like reranking)
+    # Falls back to LLM_* values if not set
+    LLM_SMALL_API_KEY = os.environ.get('LLM_SMALL_API_KEY') or os.environ.get('LLM_API_KEY')
+    LLM_SMALL_BASE_URL = os.environ.get('LLM_SMALL_BASE_URL') or os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
+    LLM_SMALL_MODEL_NAME = os.environ.get('LLM_SMALL_MODEL_NAME') or os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
     
-    # Zep settings
+    # Graph backend: "zep" (default, cloud) o "graphiti" (Neo4j local)
+    GRAPH_BACKEND = os.environ.get('GRAPH_BACKEND', 'zep')
+
+    # Zep Cloud
     ZEP_API_KEY = os.environ.get('ZEP_API_KEY')
 
+    # Graphiti + Neo4j
+    NEO4J_URI = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
+    NEO4J_USER = os.environ.get('NEO4J_USER', 'neo4j')
+    NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD')
+    GRAPHITI_BATCH_SIZE = int(os.environ.get('GRAPHITI_BATCH_SIZE', '10'))
+
+    # LLM provider ("" = OpenAI-compatible per defecte, "gemini" = Google AI Studio)
+    LLM_PROVIDER = os.environ.get('LLM_PROVIDER', '')
+
     # File upload settings
     MAX_CONTENT_LENGTH = 50 * 1024 * 1024  # 50MB
     UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), '../uploads')
@@ -45,6 +69,10 @@ class Config:
     DEFAULT_CHUNK_SIZE = 500  # default chunk size
     DEFAULT_CHUNK_OVERLAP = 50  # default overlap size
 
+    # Ontology generation limits
+    ONTOLOGY_MAX_ENTITY_TYPES = int(os.environ.get('ONTOLOGY_MAX_ENTITY_TYPES', '12'))
+    ONTOLOGY_MAX_EDGE_TYPES = int(os.environ.get('ONTOLOGY_MAX_EDGE_TYPES', '10'))
+
     # OASIS simulation settings
     OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10'))
     OASIS_SIMULATION_DATA_DIR = os.path.join(os.path.dirname(__file__), '../uploads/simulations')
@@ -64,13 +92,25 @@ class Config:
     REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2'))
     REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5'))
     
+    @classmethod
+    def get_graph_config_errors(cls) -> list:
+        errors = []
+        if cls.GRAPH_BACKEND == 'zep':
+            if not cls.ZEP_API_KEY:
+                errors.append("ZEP_API_KEY is not configured (required when GRAPH_BACKEND=zep)")
+        elif cls.GRAPH_BACKEND == 'graphiti':
+            if not cls.NEO4J_PASSWORD:
+                errors.append("NEO4J_PASSWORD is not configured (required when GRAPH_BACKEND=graphiti)")
+        else:
+            errors.append(f"Unknown GRAPH_BACKEND value: '{cls.GRAPH_BACKEND}'. Use 'zep' or 'graphiti'.")
+        return errors
+
     @classmethod
     def validate(cls):
         """Validate required configuration"""
         errors = []
         if not cls.LLM_API_KEY:
             errors.append("LLM_API_KEY is not configured")
-        if not cls.ZEP_API_KEY:
-            errors.append("ZEP_API_KEY is not configured")
+        errors.extend(cls.get_graph_config_errors())
         return errors
 

backend/app/graph/__init__.py

@@ -0,0 +1,3 @@
+from .factory import get_graph_backend
+
+__all__ = ["get_graph_backend"]

backend/app/graph/base.py

@@ -0,0 +1,38 @@
+"""Abstract graph backend interface."""
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
+
+class GraphBackend(ABC):
+    @abstractmethod
+    def create_graph(self, graph_id: str, name: str, description: str = "") -> None: ...
+
+    @abstractmethod
+    def set_ontology(self, graph_ids: List[str], entities: Dict[str, Any], edges: Dict[str, Any]) -> None: ...
+
+    @abstractmethod
+    def add_batch(self, graph_id: str, episodes: List[Any]) -> List[str]: ...
+
+    @abstractmethod
+    def get_episode(self, uuid_: str) -> Any: ...
+
+    @abstractmethod
+    def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]: ...
+
+    @abstractmethod
+    def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]: ...
+
+    @abstractmethod
+    def get_node(self, uuid_: str) -> Dict[str, Any]: ...
+
+    @abstractmethod
+    def get_node_edges(self, node_uuid: str) -> List[Dict[str, Any]]: ...
+
+    @abstractmethod
+    def search(self, graph_id: str, query: str, limit: int = 10, scope: str = "edges") -> Dict[str, Any]: ...
+
+    @abstractmethod
+    def add_text(self, graph_id: str, data: str) -> None: ...
+
+    @abstractmethod
+    def delete_graph(self, graph_id: str) -> None: ...

backend/app/graph/factory.py

@@ -0,0 +1,39 @@
+"""Graph backend factory — returns singleton based on GRAPH_BACKEND env var."""
+from typing import Optional
+
+from .base import GraphBackend
+from ..utils.logger import get_logger
+
+logger = get_logger('mirofish.graph.factory')
+
+_backend_instance: Optional[GraphBackend] = None
+
+
+def get_graph_backend() -> GraphBackend:
+    """Return the configured graph backend singleton."""
+    global _backend_instance
+    if _backend_instance is not None:
+        return _backend_instance
+
+    from ..config import Config
+    backend_type = Config.GRAPH_BACKEND
+    logger.info(f"Initializing graph backend: {backend_type}")
+
+    if backend_type == "zep":
+        from .zep_backend import ZepBackend
+        _backend_instance = ZepBackend()
+    elif backend_type == "graphiti":
+        from .graphiti_backend import GraphitiBackend
+        _backend_instance = GraphitiBackend()
+    else:
+        raise ValueError(
+            f"Unknown GRAPH_BACKEND='{backend_type}'. Valid values: 'zep', 'graphiti'."
+        )
+
+    return _backend_instance
+
+
+def reset_graph_backend() -> None:
+    """Reset singleton (useful for testing)."""
+    global _backend_instance
+    _backend_instance = None

backend/app/graph/graphiti_backend.py

@@ -0,0 +1,470 @@
+"""Graphiti + Neo4j implementation of GraphBackend."""
+import asyncio
+import json
+import threading
+import typing
+import uuid as uuid_mod
+from typing import Any, Dict, List, Optional
+
+from .base import GraphBackend
+from ..config import Config
+from ..utils.logger import get_logger
+from ..utils.llm_client import parse_azure_url
+
+
+def _neo4j_val(v: Any) -> Any:
+    """Convert Neo4j native types to JSON-serializable Python types."""
+    if v is None:
+        return None
+    t = type(v).__name__
+    if t in ('DateTime', 'Date', 'Time', 'LocalDateTime', 'LocalTime', 'Duration'):
+        return str(v)
+    if isinstance(v, (list, tuple)):
+        return [_neo4j_val(i) for i in v]
+    if isinstance(v, dict):
+        return {k: _neo4j_val(vv) for k, vv in v.items()}
+    return v
+
+
+def _flatten_attributes(attrs: dict) -> dict:
+    """Flatten entity attribute dicts so every value is a Neo4j-safe primitive.
+
+    Graphiti extracts entity attributes via a Pydantic model, but the raw LLM
+    response sometimes wraps each value in a nested dict (e.g. {"value": "CTTI"}).
+    Neo4j only accepts primitive types or arrays thereof, so we coerce any
+    dict value to its string representation. Lists of primitives are kept as-is
+    because Neo4j supports array properties.
+    """
+    result = {}
+    for k, v in attrs.items():
+        if v is None:
+            continue
+        if isinstance(v, dict):
+            # Unwrap {"value": "..."} pattern emitted by some LLMs; fall back to str()
+            result[k] = v.get("value") or v.get("text") or str(v)
+        else:
+            result[k] = v
+    return result
+
+
+def _neo4j_props(node_or_rel: Any) -> Dict[str, Any]:
+    """Return a JSON-safe dict of a Neo4j node or relationship's properties."""
+    return {k: _neo4j_val(v) for k, v in dict(node_or_rel).items()}
+
+logger = get_logger('mirofish.graph.graphiti')
+
+
+def _make_azure_generic_client(config, client):
+    """Return an OpenAIGenericClient subclass that uses max_completion_tokens
+    instead of max_tokens — required by gpt-5 / o-series models on Azure."""
+    from graphiti_core.llm_client.openai_generic_client import OpenAIGenericClient
+    import openai as _openai
+    from graphiti_core.llm_client.errors import RateLimitError as _RateLimitError
+    from pydantic import BaseModel as _BaseModel
+
+    class _AzureGenericClient(OpenAIGenericClient):
+        async def _generate_response(self, messages, response_model=None, max_tokens=None, model_size=None):
+            from openai.types.chat import ChatCompletionMessageParam
+            if max_tokens is None:
+                max_tokens = self.max_tokens
+            openai_messages: list[ChatCompletionMessageParam] = []
+            for m in messages:
+                if m.role == 'user':
+                    openai_messages.append({'role': 'user', 'content': m.content})
+                elif m.role == 'system':
+                    openai_messages.append({'role': 'system', 'content': m.content})
+            response_format: dict[str, Any] = {'type': 'json_object'}
+            if response_model is not None:
+                schema_name = getattr(response_model, '__name__', 'structured_response')
+                response_format = {
+                    'type': 'json_schema',
+                    'json_schema': {
+                        'name': schema_name,
+                        'schema': response_model.model_json_schema(),
+                    },
+                }
+            try:
+                response = await self.client.chat.completions.create(
+                    model=self.model,
+                    messages=openai_messages,
+                    temperature=self.temperature,
+                    max_completion_tokens=max_tokens,
+                    response_format=response_format,
+                )
+                return json.loads(response.choices[0].message.content or '{}')
+            except _openai.RateLimitError as e:
+                raise _RateLimitError from e
+
+    return _AzureGenericClient(config=config, client=client)
+
+
+def _run_async(coro, timeout=300):
+    """Run an async coroutine from a sync context using a dedicated thread loop."""
+    loop = _get_event_loop()
+    future = asyncio.run_coroutine_threadsafe(coro, loop)
+    return future.result(timeout=timeout)
+
+
+_loop: Optional[asyncio.AbstractEventLoop] = None
+_loop_thread: Optional[threading.Thread] = None
+_loop_lock = threading.Lock()
+
+
+def _get_event_loop() -> asyncio.AbstractEventLoop:
+    global _loop, _loop_thread
+    with _loop_lock:
+        if _loop is None or not _loop.is_running():
+            _loop = asyncio.new_event_loop()
+            _loop_thread = threading.Thread(target=_loop.run_forever, daemon=True)
+            _loop_thread.start()
+    return _loop
+
+
+class GraphitiBackend(GraphBackend):
+    def __init__(
+        self,
+        uri: Optional[str] = None,
+        user: Optional[str] = None,
+        password: Optional[str] = None,
+    ):
+        self._uri = uri or Config.NEO4J_URI
+        self._user = user or Config.NEO4J_USER
+        self._password = password or Config.NEO4J_PASSWORD
+        if not self._password:
+            raise ValueError("NEO4J_PASSWORD is not configured")
+        self._entity_types: Dict[str, Any] = {}
+        self._edge_types: Dict[str, Any] = {}
+        self._entity_defs: Dict[str, Any] = {}
+        self._edge_defs: Dict[str, Any] = {}
+        self._client = self._build_client()
+
+    def _build_client(self):
+        from graphiti_core import Graphiti
+        from graphiti_core.llm_client.openai_generic_client import OpenAIGenericClient
+        from graphiti_core.llm_client.config import LLMConfig
+        from graphiti_core.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
+        from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
+        from openai import AsyncOpenAI
+
+        llm_base_url, llm_query = parse_azure_url(Config.LLM_BASE_URL)
+        small_base_url, small_query = parse_azure_url(Config.LLM_SMALL_BASE_URL)
+        embed_base_url, embed_query = parse_azure_url(Config.LLM_EMBED_BASE_URL)
+
+        # Pre-built async clients so api-version is passed as default_query (Azure requirement)
+        async_llm_client = AsyncOpenAI(
+            api_key=Config.LLM_API_KEY,
+            base_url=llm_base_url,
+            default_query=llm_query or None,
+        )
+        async_small_client = AsyncOpenAI(
+            api_key=Config.LLM_SMALL_API_KEY,
+            base_url=small_base_url,
+            default_query=small_query or None,
+        )
+        async_embed_client = AsyncOpenAI(
+            api_key=Config.LLM_EMBED_API_KEY,
+            base_url=embed_base_url,
+            default_query=embed_query or None,
+        )
+
+        llm_config = LLMConfig(
+            api_key=Config.LLM_API_KEY,
+            model=Config.LLM_MODEL_NAME,
+            small_model=Config.LLM_SMALL_MODEL_NAME,
+            base_url=llm_base_url,
+        )
+        llm_client = _make_azure_generic_client(config=llm_config, client=async_llm_client)
+        embedder = OpenAIEmbedder(
+            config=OpenAIEmbedderConfig(
+                api_key=Config.LLM_EMBED_API_KEY,
+                base_url=embed_base_url,
+                embedding_model=Config.LLM_EMBED_MODEL_NAME,
+            ),
+            client=async_embed_client,
+        )
+        cross_encoder = OpenAIRerankerClient(config=llm_config, client=async_small_client)
+        client = Graphiti(
+            uri=self._uri,
+            user=self._user,
+            password=self._password,
+            llm_client=llm_client,
+            embedder=embedder,
+            cross_encoder=cross_encoder,
+        )
+        self._patch_extract_entity_attributes()
+        return client
+
+    @staticmethod
+    def _patch_extract_entity_attributes() -> None:
+        """Monkey-patch graphiti's _extract_entity_attributes to sanitize LLM output.
+
+        Some LLMs return attribute values as nested dicts ({"value": "CTTI"}) instead
+        of plain strings. Neo4j rejects these with TypeError. We intercept the raw
+        llm_response dict before it is stored in node.attributes and flatten it.
+        """
+        import graphiti_core.utils.maintenance.node_operations as _node_ops
+
+        original = _node_ops._extract_entity_attributes
+
+        async def _patched(llm_client, node, episode, previous_episodes, entity_type):
+            result = await original(llm_client, node, episode, previous_episodes, entity_type)
+            # result is a dict — flatten any dict-valued attributes
+            return _flatten_attributes(result) if result else result
+
+        _node_ops._extract_entity_attributes = _patched
+
+    def create_graph(self, graph_id: str, name: str, description: str = "") -> None:
+        logger.info(f"Graphiti graph namespace ready: {graph_id}")
+
+    def set_ontology(self, graph_ids: List[str], entities: Dict[str, Any], edges: Dict[str, Any]) -> None:
+        from pydantic import BaseModel as _BaseModel, Field as _Field
+
+        def _make_model(name: str, type_def: Any) -> Any:
+            if isinstance(type_def, dict):
+                doc = type_def.get("description", "")
+                attrs_defs = type_def.get("attributes", [])
+            else:
+                doc = getattr(type_def, "__doc__", "") or ""
+                attrs_defs = []
+
+            annotations: Dict[str, Any] = {}
+            fields: Dict[str, Any] = {"__doc__": doc, "__annotations__": annotations}
+            for attr in attrs_defs:
+                attr_name = attr.get("name", "")
+                attr_desc = attr.get("description", attr_name)
+                if not attr_name:
+                    continue
+                annotations[attr_name] = Optional[str]
+                fields[attr_name] = _Field(default=None, description=attr_desc)
+
+            return type(name, (_BaseModel,), fields)
+
+        self._entity_types: Dict[str, Any] = {
+            name: _make_model(name, td) for name, td in (entities or {}).items()
+        }
+        self._edge_types: Dict[str, Any] = {
+            name: _make_model(name, td) for name, td in (edges or {}).items()
+        }
+        # Keep a separate plain dict for use in extraction instructions
+        self._entity_defs: Dict[str, Any] = dict(entities or {})
+        self._edge_defs: Dict[str, Any] = dict(edges or {})
+        if self._entity_types:
+            logger.info(f"Graphiti entity types: {list(self._entity_types.keys())}")
+        if self._edge_types:
+            logger.info(f"Graphiti edge types: {list(self._edge_types.keys())}")
+
+    def _build_extraction_instructions(self) -> Optional[str]:
+        """Return custom instructions that constrain extraction to ontology types and attributes."""
+        entity_defs = self._entity_defs or {}
+        edge_defs = self._edge_defs or {}
+        if not entity_defs and not edge_defs:
+            return None
+
+        parts = []
+
+        if entity_defs:
+            entity_lines = []
+            for name, td in entity_defs.items():
+                desc = td.get("description", "") if isinstance(td, dict) else ""
+                attrs = td.get("attributes", []) if isinstance(td, dict) else []
+                if attrs:
+                    attr_str = ", ".join(
+                        f"{a['name']} ({a.get('description', a['name'])})"
+                        for a in attrs if a.get("name")
+                    )
+                    entity_lines.append(f"  - {name}: {desc} [attributes: {attr_str}]")
+                else:
+                    entity_lines.append(f"  - {name}: {desc}")
+            parts.append(
+                "Only classify entities using these types (use 'Entity' only if none fits):\n"
+                + "\n".join(entity_lines)
+                + "\nFor each entity, extract values for the listed attributes when present in the text."
+            )
+
+        if edge_defs:
+            edge_names = list(edge_defs.keys())
+            parts.append(
+                f"Only use these relationship types: {', '.join(edge_names)}. "
+                "Do not invent new relationship type names."
+            )
+
+        return "\n\n".join(parts)
+
+    def add_batch(self, graph_id: str, episodes: List[Any]) -> List[str]:
+        from graphiti_core.nodes import EpisodeType
+        from datetime import datetime, timezone
+        import time as _time
+
+        entity_types = self._entity_types or None
+        edge_types = self._edge_types or None
+        instructions = self._build_extraction_instructions()
+        ids = []
+
+        for ep in episodes:
+            data = ep["data"] if isinstance(ep, dict) else ep.data
+            ep_id = str(uuid_mod.uuid4())
+            ids.append(ep_id)
+
+            last_exc = None
+            for attempt in range(3):
+                try:
+                    _run_async(
+                        self._client.add_episode(
+                            name=ep_id,
+                            episode_body=data,
+                            source_description="MiroFish document chunk",
+                            reference_time=datetime.now(timezone.utc),
+                            source=EpisodeType.text,
+                            group_id=graph_id,
+                            entity_types=entity_types,
+                            edge_types=edge_types,
+                            custom_extraction_instructions=instructions,
+                        ),
+                        timeout=300,
+                    )
+                    last_exc = None
+                    break
+                except Exception as exc:
+                    last_exc = exc
+                    # "node not found" race condition — wait and retry
+                    if "not found" in str(exc).lower() and attempt < 2:
+                        logger.warning(f"Episode {ep_id} attempt {attempt + 1} failed ({exc}), retrying...")
+                        _time.sleep(2 * (attempt + 1))
+                    else:
+                        raise
+
+            if last_exc:
+                raise last_exc
+
+        return ids
+
+    def get_episode(self, uuid_: str) -> Any:
+        class _FakeEpisode:
+            processed = True
+        return _FakeEpisode()
+
+    def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]:
+        results = _run_async(
+            self._client.driver.execute_query(
+                "MATCH (n:Entity {group_id: $gid}) RETURN n",
+                params={"gid": graph_id},
+            )
+        )
+        nodes = []
+        for record in results.records:
+            n = record["n"]
+            nodes.append({
+                "uuid": n.get("uuid", n.element_id),
+                "name": n.get("name", ""),
+                "labels": list(n.labels),
+                "summary": n.get("summary", ""),
+                "attributes": _neo4j_props(n),
+                "created_at": str(n.get("created_at", "")),
+            })
+        return nodes
+
+    def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]:
+        results = _run_async(
+            self._client.driver.execute_query(
+                "MATCH (s)-[r]->(t) WHERE r.group_id = $gid RETURN s, r, t",
+                params={"gid": graph_id},
+            )
+        )
+        edges = []
+        for record in results.records:
+            r = record["r"]
+            edges.append({
+                "uuid": r.get("uuid", r.element_id),
+                "name": r.get("name", type(r).__name__),
+                "fact": r.get("fact", ""),
+                "source_node_uuid": record["s"].get("uuid", ""),
+                "target_node_uuid": record["t"].get("uuid", ""),
+                "fact_type": r.get("fact_type", ""),
+                "attributes": _neo4j_props(r),
+                "created_at": str(r.get("created_at", "")),
+                "valid_at": str(r.get("valid_at", "")),
+                "invalid_at": str(r.get("invalid_at", "")),
+                "expired_at": str(r.get("expired_at", "")),
+                "episodes": [],
+            })
+        return edges
+
+    def get_node(self, uuid_: str) -> Dict[str, Any]:
+        results = _run_async(
+            self._client.driver.execute_query(
+                "MATCH (n {uuid: $uuid}) RETURN n LIMIT 1",
+                params={"uuid": uuid_},
+            )
+        )
+        if not results.records:
+            return {}
+        n = results.records[0]["n"]
+        return {
+            "uuid": n.get("uuid", ""),
+            "name": n.get("name", ""),
+            "labels": list(n.labels),
+            "summary": n.get("summary", ""),
+            "attributes": _neo4j_props(n),
+        }
+
+    def get_node_edges(self, node_uuid: str) -> List[Dict[str, Any]]:
+        results = _run_async(
+            self._client.driver.execute_query(
+                "MATCH (n {uuid: $uuid})-[r]->(t) RETURN r, t "
+                "UNION MATCH (s)-[r]->(n {uuid: $uuid}) RETURN r, s as t",
+                params={"uuid": node_uuid},
+            )
+        )
+        edges = []
+        for record in results.records:
+            r = record["r"]
+            edges.append({
+                "uuid": r.get("uuid", r.element_id),
+                "name": r.get("name", ""),
+                "fact": r.get("fact", ""),
+                "source_node_uuid": r.get("source_node_uuid", node_uuid),
+                "target_node_uuid": r.get("target_node_uuid", ""),
+            })
+        return edges
+
+    def search(self, graph_id: str, query: str, limit: int = 10, scope: str = "edges") -> Dict[str, Any]:
+        results = _run_async(
+            self._client.search(query=query, group_ids=[graph_id], num_results=limit)
+        )
+        edges = [
+            {
+                "uuid": getattr(r, "uuid", ""),
+                "name": getattr(r, "name", ""),
+                "fact": getattr(r, "fact", ""),
+                "source_node_uuid": getattr(r, "source_node_uuid", ""),
+                "target_node_uuid": getattr(r, "target_node_uuid", ""),
+            }
+            for r in (results or [])
+        ]
+        return {"edges": edges, "nodes": []}
+
+    def add_text(self, graph_id: str, data: str) -> None:
+        from graphiti_core.nodes import EpisodeType
+        from datetime import datetime, timezone
+        ep_id = str(uuid_mod.uuid4())
+        _run_async(
+            self._client.add_episode(
+                name=ep_id,
+                episode_body=data,
+                source_description="MiroFish document chunk",
+                reference_time=datetime.now(timezone.utc),
+                source=EpisodeType.text,
+                group_id=graph_id,
+                entity_types=self._entity_types or None,
+                edge_types=self._edge_types or None,
+                custom_extraction_instructions=self._build_extraction_instructions(),
+            )
+        )
+
+    def delete_graph(self, graph_id: str) -> None:
+        _run_async(
+            self._client.driver.execute_query(
+                "MATCH (n {group_id: $gid}) DETACH DELETE n",
+                params={"gid": graph_id},
+            )
+        )

backend/app/graph/zep_backend.py

@@ -0,0 +1,151 @@
+"""Zep Cloud implementation of GraphBackend."""
+import time
+from typing import Any, Dict, List, Optional
+
+from zep_cloud.client import Zep
+from zep_cloud import InternalServerError
+
+from .base import GraphBackend
+from ..config import Config
+from ..utils.logger import get_logger
+
+logger = get_logger('mirofish.graph.zep')
+
+_PAGE_SIZE = 100
+_MAX_ITEMS = 2000
+_MAX_RETRIES = 3
+_RETRY_DELAY = 2.0
+
+
+def _fetch_page_with_retry(api_call, *args, max_retries=_MAX_RETRIES, retry_delay=_RETRY_DELAY, **kwargs):
+    for attempt in range(max_retries):
+        try:
+            return api_call(*args, **kwargs) or []
+        except (ConnectionError, TimeoutError, OSError, InternalServerError):
+            if attempt == max_retries - 1:
+                raise
+            time.sleep(retry_delay * (2 ** attempt))
+    return []
+
+
+def _fetch_all(list_fn, graph_id: str, cursor_key: str = "uuid_cursor") -> List[Any]:
+    results, cursor = [], None
+    while True:
+        kwargs = {"limit": _PAGE_SIZE}
+        if cursor:
+            kwargs[cursor_key] = cursor
+        batch = _fetch_page_with_retry(list_fn, graph_id, **kwargs)
+        results.extend(batch)
+        if not batch or len(batch) < _PAGE_SIZE or len(results) >= _MAX_ITEMS:
+            break
+        cursor = batch[-1].uuid_
+    return results
+
+
+class ZepBackend(GraphBackend):
+    def __init__(self, api_key: Optional[str] = None):
+        self.api_key = api_key or Config.ZEP_API_KEY
+        if not self.api_key:
+            raise ValueError("ZEP_API_KEY is not configured")
+        self._client = Zep(api_key=self.api_key)
+
+    def create_graph(self, graph_id: str, name: str, description: str = "") -> None:
+        self._client.graph.create(graph_id=graph_id, name=name, description=description)
+
+    def set_ontology(self, graph_ids: List[str], entities: Dict[str, Any], edges: Dict[str, Any]) -> None:
+        self._client.graph.set_ontology(graph_ids=graph_ids, entities=entities, edges=edges)
+
+    def add_batch(self, graph_id: str, episodes: List[Any]) -> List[str]:
+        from zep_cloud import EpisodeData
+        ep_objects = [
+            EpisodeData(data=ep["data"], type=ep.get("type", "text"))
+            if isinstance(ep, dict) else ep
+            for ep in episodes
+        ]
+        result = self._client.graph.add_batch(graph_id=graph_id, episodes=ep_objects)
+        return [ep.uuid_ for ep in (result or [])]
+
+    def get_episode(self, uuid_: str) -> Any:
+        return self._client.graph.episode.get(uuid_=uuid_)
+
+    def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]:
+        nodes = _fetch_all(self._client.graph.node.get_by_graph_id, graph_id)
+        return [
+            {
+                "uuid": getattr(n, "uuid_", None) or getattr(n, "uuid", None),
+                "name": getattr(n, "name", ""),
+                "labels": list(getattr(n, "labels", []) or []),
+                "summary": getattr(n, "summary", ""),
+                "attributes": dict(getattr(n, "attributes", {}) or {}),
+                "created_at": str(getattr(n, "created_at", "")),
+            }
+            for n in nodes
+        ]
+
+    def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]:
+        edges = _fetch_all(self._client.graph.edge.get_by_graph_id, graph_id)
+        return [
+            {
+                "uuid": getattr(e, "uuid_", None) or getattr(e, "uuid", None),
+                "name": getattr(e, "name", ""),
+                "fact": getattr(e, "fact", ""),
+                "source_node_uuid": getattr(e, "source_node_uuid", None),
+                "target_node_uuid": getattr(e, "target_node_uuid", None),
+                "fact_type": getattr(e, "fact_type", None),
+                "attributes": dict(getattr(e, "attributes", {}) or {}),
+                "created_at": str(getattr(e, "created_at", "")),
+                "valid_at": str(getattr(e, "valid_at", "")),
+                "invalid_at": str(getattr(e, "invalid_at", "")),
+                "expired_at": str(getattr(e, "expired_at", "")),
+                "episodes": list(getattr(e, "episodes", []) or []),
+            }
+            for e in edges
+        ]
+
+    def get_node(self, uuid_: str) -> Dict[str, Any]:
+        n = self._client.graph.node.get(uuid_=uuid_)
+        return {
+            "uuid": getattr(n, "uuid_", None) or getattr(n, "uuid", None),
+            "name": getattr(n, "name", ""),
+            "labels": list(getattr(n, "labels", []) or []),
+            "summary": getattr(n, "summary", ""),
+            "attributes": dict(getattr(n, "attributes", {}) or {}),
+        }
+
+    def get_node_edges(self, node_uuid: str) -> List[Dict[str, Any]]:
+        for attempt in range(_MAX_RETRIES):
+            try:
+                edges = self._client.graph.node.get_entity_edges(node_uuid=node_uuid) or []
+                return [
+                    {
+                        "uuid": getattr(e, "uuid_", None) or getattr(e, "uuid", None),
+                        "name": getattr(e, "name", ""),
+                        "fact": getattr(e, "fact", ""),
+                        "source_node_uuid": getattr(e, "source_node_uuid", None),
+                        "target_node_uuid": getattr(e, "target_node_uuid", None),
+                    }
+                    for e in edges
+                ]
+            except (ConnectionError, TimeoutError, OSError, InternalServerError):
+                if attempt == _MAX_RETRIES - 1:
+                    raise
+                time.sleep(_RETRY_DELAY * (2 ** attempt))
+        return []
+
+    def search(self, graph_id: str, query: str, limit: int = 10, scope: str = "edges") -> Dict[str, Any]:
+        result = self._client.graph.search(
+            graph_id=graph_id,
+            query=query,
+            limit=limit,
+            scope=scope,
+            reranker="cross_encoder",
+        )
+        edges = getattr(result, "edges", []) or []
+        nodes = getattr(result, "nodes", []) or []
+        return {"edges": edges, "nodes": nodes}
+
+    def add_text(self, graph_id: str, data: str) -> None:
+        self._client.graph.add(graph_id=graph_id, type="text", data=data)
+
+    def delete_graph(self, graph_id: str) -> None:
+        self._client.graph.delete(graph_id=graph_id)

backend/app/services/graph_builder.py

@@ -10,12 +10,9 @@ import threading
 from typing import Dict, Any, List, Optional, Callable
 from dataclasses import dataclass
 
-from zep_cloud.client import Zep
-from zep_cloud import EpisodeData, EntityEdgeSourceTarget
-
 from ..config import Config
+from ..graph import get_graph_backend
 from ..models.task import TaskManager, TaskStatus
-from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
 from .text_processor import TextProcessor
 from ..utils.locale import t, get_locale, set_locale
 
@@ -43,12 +40,8 @@ class GraphBuilderService:
     Responsible for calling the Zep API to build the knowledge graph.
     """
 
-    def __init__(self, api_key: Optional[str] = None):
-        self.api_key = api_key or Config.ZEP_API_KEY
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY is not configured")
-        
-        self.client = Zep(api_key=self.api_key)
+    def __init__(self):
+        self._graph = get_graph_backend()
         self.task_manager = TaskManager()
     
     def build_graph_async(
@@ -191,19 +184,36 @@ class GraphBuilderService:
             self.task_manager.fail_task(task_id, error_msg)
     
     def create_graph(self, name: str) -> str:
-        """Create a Zep graph (public method)"""
+        """Create a graph (public method)"""
         graph_id = f"mirofish_{uuid.uuid4().hex[:16]}"
-        
-        self.client.graph.create(
+        self._graph.create_graph(
             graph_id=graph_id,
             name=name,
             description="MiroFish Social Simulation Graph"
         )
-        
         return graph_id
     
     def set_ontology(self, graph_id: str, ontology: Dict[str, Any]):
         """Set graph ontology (public method)"""
+        from ..config import Config
+        if Config.GRAPH_BACKEND != "zep":
+            entities = {
+                e["name"]: {
+                    "description": e.get("description", ""),
+                    "attributes": e.get("attributes", []),
+                }
+                for e in ontology.get("entity_types", [])
+            }
+            edges = {
+                e["name"]: {
+                    "description": e.get("description", ""),
+                    "attributes": e.get("attributes", []),
+                }
+                for e in ontology.get("edge_types", [])
+            }
+            self._graph.set_ontology(graph_ids=[graph_id], entities=entities, edges=edges)
+            return
+
         import warnings
         from typing import Optional
         from pydantic import Field
@@ -217,60 +227,51 @@ class GraphBuilderService:
         RESERVED_NAMES = {'uuid', 'name', 'group_id', 'name_embedding', 'summary', 'created_at'}
 
         def safe_attr_name(attr_name: str) -> str:
-            """Convert reserved names to safe attribute names"""
             if attr_name.lower() in RESERVED_NAMES:
                 return f"entity_{attr_name}"
             return attr_name
-        
+
         # Dynamically create entity types
         entity_types = {}
         for entity_def in ontology.get("entity_types", []):
             name = entity_def["name"]
             description = entity_def.get("description", f"A {name} entity.")
 
-            # Build attribute dict and type annotations (required by Pydantic v2)
             attrs = {"__doc__": description}
             annotations = {}
 
             for attr_def in entity_def.get("attributes", []):
-                attr_name = safe_attr_name(attr_def["name"])  # Use safe name
+                attr_name = safe_attr_name(attr_def["name"])
                 attr_desc = attr_def.get("description", attr_name)
-                # Zep API requires Field description — this is mandatory
                 attrs[attr_name] = Field(description=attr_desc, default=None)
-                annotations[attr_name] = Optional[EntityText]  # Type annotation
+                annotations[attr_name] = Optional[EntityText]
 
             attrs["__annotations__"] = annotations
-
-            # Dynamically create class
             entity_class = type(name, (EntityModel,), attrs)
             entity_class.__doc__ = description
             entity_types[name] = entity_class
-        
+
         # Dynamically create edge types
         edge_definitions = {}
         for edge_def in ontology.get("edge_types", []):
             name = edge_def["name"]
             description = edge_def.get("description", f"A {name} relationship.")
 
-            # Build attribute dict and type annotations
             attrs = {"__doc__": description}
             annotations = {}
 
             for attr_def in edge_def.get("attributes", []):
-                attr_name = safe_attr_name(attr_def["name"])  # Use safe name
+                attr_name = safe_attr_name(attr_def["name"])
                 attr_desc = attr_def.get("description", attr_name)
-                # Zep API requires Field description — this is mandatory
                 attrs[attr_name] = Field(description=attr_desc, default=None)
-                annotations[attr_name] = Optional[str]  # Edge attributes use str type
+                annotations[attr_name] = Optional[str]
 
             attrs["__annotations__"] = annotations
-
-            # Dynamically create class
             class_name = ''.join(word.capitalize() for word in name.split('_'))
             edge_class = type(class_name, (EdgeModel,), attrs)
             edge_class.__doc__ = description
-            
-            # Build source_targets
+
+            from zep_cloud import EntityEdgeSourceTarget
             source_targets = []
             for st in edge_def.get("source_targets", []):
                 source_targets.append(
@@ -279,13 +280,12 @@ class GraphBuilderService:
                         target=st.get("target", "Entity")
                     )
                 )
-            
+
             if source_targets:
                 edge_definitions[name] = (edge_class, source_targets)
-        
-        # Call Zep API to set ontology
+
         if entity_types or edge_definitions:
-            self.client.graph.set_ontology(
+            self._graph.set_ontology(
                 graph_ids=[graph_id],
                 entities=entity_types if entity_types else None,
                 edges=edge_definitions if edge_definitions else None,
@@ -314,25 +314,14 @@ class GraphBuilderService:
                     progress
                 )
             
-            # Build episode data
             episodes = [
-                EpisodeData(data=chunk, type="text")
+                {"data": chunk, "type": "text"}
                 for chunk in batch_chunks
             ]
-            
-            # Send to Zep
+
             try:
-                batch_result = self.client.graph.add_batch(
-                    graph_id=graph_id,
-                    episodes=episodes
-                )
-                
-                # Collect returned episode UUIDs
-                if batch_result and isinstance(batch_result, list):
-                    for ep in batch_result:
-                        ep_uuid = getattr(ep, 'uuid_', None) or getattr(ep, 'uuid', None)
-                        if ep_uuid:
-                            episode_uuids.append(ep_uuid)
+                returned_uuids = self._graph.add_batch(graph_id=graph_id, episodes=episodes)
+                episode_uuids.extend(returned_uuids)
                 
                 # Avoid sending requests too quickly
                 time.sleep(1)
@@ -376,7 +365,7 @@ class GraphBuilderService:
             # Check processing status of each episode
             for ep_uuid in list(pending_episodes):
                 try:
-                    episode = self.client.graph.episode.get(uuid_=ep_uuid)
+                    episode = self._graph.get_episode(ep_uuid)
                     is_processed = getattr(episode, 'processed', False)
 
                     if is_processed:
@@ -402,19 +391,14 @@ class GraphBuilderService:
     
     def _get_graph_info(self, graph_id: str) -> GraphInfo:
         """Retrieve graph info"""
-        # Fetch nodes (paginated)
-        nodes = fetch_all_nodes(self.client, graph_id)
+        nodes = self._graph.get_all_nodes(graph_id)
+        edges = self._graph.get_all_edges(graph_id)
 
-        # Fetch edges (paginated)
-        edges = fetch_all_edges(self.client, graph_id)
-
-        # Count entity types
         entity_types = set()
         for node in nodes:
-            if node.labels:
-                for label in node.labels:
-                    if label not in ["Entity", "Node"]:
-                        entity_types.add(label)
+            for label in node.get("labels", []):
+                if label not in ["Entity", "Node"]:
+                    entity_types.add(label)
 
         return GraphInfo(
             graph_id=graph_id,
@@ -424,83 +408,25 @@ class GraphBuilderService:
         )
     
     def get_graph_data(self, graph_id: str) -> Dict[str, Any]:
-        """
-        Retrieve full graph data (with detailed information).
+        """Retrieve full graph data (nodes + edges with timestamps and attributes)."""
+        nodes = self._graph.get_all_nodes(graph_id)
+        edges = self._graph.get_all_edges(graph_id)
 
-        Args:
-            graph_id: graph ID
+        node_map = {n["uuid"]: n.get("name", "") for n in nodes}
 
-        Returns:
-            Dictionary containing nodes and edges with timestamps, attributes, and other details
-        """
-        nodes = fetch_all_nodes(self.client, graph_id)
-        edges = fetch_all_edges(self.client, graph_id)
-
-        # Build node map for looking up node names
-        node_map = {}
-        for node in nodes:
-            node_map[node.uuid_] = node.name or ""
-
-        nodes_data = []
-        for node in nodes:
-            # Get creation timestamp
-            created_at = getattr(node, 'created_at', None)
-            if created_at:
-                created_at = str(created_at)
-            
-            nodes_data.append({
-                "uuid": node.uuid_,
-                "name": node.name,
-                "labels": node.labels or [],
-                "summary": node.summary or "",
-                "attributes": node.attributes or {},
-                "created_at": created_at,
-            })
-        
-        edges_data = []
-        for edge in edges:
-            # Get timestamps
-            created_at = getattr(edge, 'created_at', None)
-            valid_at = getattr(edge, 'valid_at', None)
-            invalid_at = getattr(edge, 'invalid_at', None)
-            expired_at = getattr(edge, 'expired_at', None)
-
-            # Get episodes
-            episodes = getattr(edge, 'episodes', None) or getattr(edge, 'episode_ids', None)
-            if episodes and not isinstance(episodes, list):
-                episodes = [str(episodes)]
-            elif episodes:
-                episodes = [str(e) for e in episodes]
-
-            # Get fact_type
-            fact_type = getattr(edge, 'fact_type', None) or edge.name or ""
-            
-            edges_data.append({
-                "uuid": edge.uuid_,
-                "name": edge.name or "",
-                "fact": edge.fact or "",
-                "fact_type": fact_type,
-                "source_node_uuid": edge.source_node_uuid,
-                "target_node_uuid": edge.target_node_uuid,
-                "source_node_name": node_map.get(edge.source_node_uuid, ""),
-                "target_node_name": node_map.get(edge.target_node_uuid, ""),
-                "attributes": edge.attributes or {},
-                "created_at": str(created_at) if created_at else None,
-                "valid_at": str(valid_at) if valid_at else None,
-                "invalid_at": str(invalid_at) if invalid_at else None,
-                "expired_at": str(expired_at) if expired_at else None,
-                "episodes": episodes or [],
-            })
-        
         return {
             "graph_id": graph_id,
-            "nodes": nodes_data,
-            "edges": edges_data,
-            "node_count": len(nodes_data),
-            "edge_count": len(edges_data),
+            "nodes": nodes,
+            "edges": [
+                {**e, "source_node_name": node_map.get(e.get("source_node_uuid", ""), ""),
+                       "target_node_name": node_map.get(e.get("target_node_uuid", ""), "")}
+                for e in edges
+            ],
+            "node_count": len(nodes),
+            "edge_count": len(edges),
         }
     
     def delete_graph(self, graph_id: str):
         """Delete graph"""
-        self.client.graph.delete(graph_id=graph_id)
+        self._graph.delete_graph(graph_id)
 

backend/app/services/oasis_profile_generator.py

@@ -22,6 +22,7 @@ from zep_cloud.client import Zep
 from ..config import Config
 from ..utils.logger import get_logger
 from ..utils.locale import get_language_instruction, get_locale, set_locale, t
+from ..utils.llm_client import parse_azure_url
 from .zep_entity_reader import EntityNode, ZepEntityReader
 
 logger = get_logger('mirofish.oasis_profile')
@@ -190,15 +191,17 @@ class OasisProfileGenerator:
         graph_id: Optional[str] = None
     ):
         self.api_key = api_key or Config.LLM_API_KEY
-        self.base_url = base_url or Config.LLM_BASE_URL
+        raw_url = base_url or Config.LLM_BASE_URL
         self.model_name = model_name or Config.LLM_MODEL_NAME
-        
+
         if not self.api_key:
             raise ValueError("LLM_API_KEY is not configured")
 
+        self.base_url, _default_query = parse_azure_url(raw_url)
         self.client = OpenAI(
             api_key=self.api_key,
-            base_url=self.base_url
+            base_url=self.base_url,
+            default_query=_default_query if _default_query else None
         )
 
         # Zep client for enriching context via retrieval

backend/app/services/ontology_generator.py

@@ -8,7 +8,8 @@ import logging
 import re
 from typing import Dict, Any, List, Optional
 from ..utils.llm_client import LLMClient
-from ..utils.locale import get_language_instruction
+from ..utils.locale import get_language_instruction, t
+from ..config import Config
 
 logger = logging.getLogger(__name__)
 
@@ -62,29 +63,29 @@ Please output JSON format with the following structure:
 {
     "entity_types": [
         {
-            "name": "Entity type name (English, PascalCase)",
-            "description": "Brief description (English, max 100 characters)",
+            "name": "Entity type name (PascalCase, in the language specified by the language instruction)",
+            "description": "Brief description (in the language specified by the language instruction, max 100 characters)",
             "attributes": [
                 {
-                    "name": "Attribute name (English, snake_case)",
+                    "name": "Attribute name (snake_case, in the language specified by the language instruction)",
                     "type": "text",
-                    "description": "Attribute description"
+                    "description": "Attribute description (in the language specified by the language instruction)"
                 }
             ],
-            "examples": ["Example entity 1", "Example entity 2"]
+            "examples": ["Example entity 1 (in the language specified by the language instruction)", "Example entity 2"]
         }
     ],
     "edge_types": [
         {
-            "name": "Relationship type name (English, UPPER_SNAKE_CASE)",
-            "description": "Brief description (English, max 100 characters)",
+            "name": "Relationship type name (UPPER_SNAKE_CASE, in the language specified by the language instruction)",
+            "description": "Brief description (in the language specified by the language instruction, max 100 characters)",
             "source_targets": [
                 {"source": "Source entity type", "target": "Target entity type"}
             ],
             "attributes": []
         }
     ],
-    "analysis_summary": "Brief analysis summary of the text content"
+    "analysis_summary": "Brief analysis summary of the text content (in the language specified by the language instruction)"
 }
 ```
 
@@ -92,20 +93,21 @@ Please output JSON format with the following structure:
 
 ### 1. Entity Type Design — Must Be Strictly Followed
 
-**Quantity requirement: exactly 10 entity types**
+**Quantity requirement: see the mandatory rules in the user message**
 
 **Hierarchy requirement (must include both specific types and fallback types)**:
 
-Your 10 entity types must include the following levels:
+Your entity types must include the following levels:
 
 A. **Fallback types (required, placed as the last 2 in the list)**:
    - `Person`: Fallback type for any individual person. Use this when a person does not fit any other more specific person type.
    - `Organization`: Fallback type for any organization. Use this when an organization does not fit any other more specific organization type.
 
-B. **Specific types (8 types, designed based on text content)**:
+B. **Specific types (designed based on text content)**:
    - Design more specific types for the main roles that appear in the text
-   - Example: if the text involves an academic event, you might have `Student`, `Professor`, `University`
-   - Example: if the text involves a business event, you might have `Company`, `CEO`, `Employee`
+   - Example: if the text involves an academic event, you might have `Student`, `Professor`, `University`, `ResearchGroup`, `Alumni`, etc.
+   - Example: if the text involves a business event, you might have `Company`, `CEO`, `Employee`, `Investor`, `Regulator`, etc.
+   - Ensure broad coverage of all actor categories present in the text
 
 **Why fallback types are needed**:
 - Various people appear in text, such as "primary and secondary school teachers", "passersby", "some netizen"
@@ -119,9 +121,10 @@ B. **Specific types (8 types, designed based on text content)**:
 
 ### 2. Relationship Type Design
 
-- Quantity: 6-10
+- Quantity: see the mandatory rules in the user message
 - Relationships should reflect real connections in social media interactions
 - Ensure the source_targets in relationships cover the entity types you have defined
+- Aim for rich coverage: include hierarchical, collaborative, adversarial, and informational relationships
 
 ### 3. Attribute Design
 
@@ -129,47 +132,23 @@ B. **Specific types (8 types, designed based on text content)**:
 - **Note**: Attribute names must not use `name`, `uuid`, `group_id`, `created_at`, `summary` (these are system reserved words)
 - Recommended: `full_name`, `title`, `role`, `position`, `location`, `description`, etc.
 
-## Entity Type Reference
+## Entity and Relationship Type Reference
 
-**Individual types (specific)**:
-- Student: student
-- Professor: professor/scholar
-- Journalist: journalist
-- Celebrity: celebrity/influencer
-- Executive: corporate executive
-- Official: government official
-- Lawyer: lawyer
-- Doctor: doctor
+Use the language specified in the language instruction for ALL names. Keep PascalCase for entity names and UPPER_SNAKE_CASE for relationship names, but use words from the target language.
 
-**Individual types (fallback)**:
-- Person: any individual (use when not fitting the specific types above)
+**Individual type examples** (translate to target language):
+- A person who is a student → StudentName in target language, PascalCase
+- A person who is a journalist → JournalistName in target language, PascalCase
+- Fallback for any individual → PersonName in target language, PascalCase
 
-**Organization types (specific)**:
-- University: university/college
-- Company: company/enterprise
-- GovernmentAgency: government agency
-- MediaOutlet: media organization
-- Hospital: hospital
-- School: primary/secondary school
-- NGO: non-governmental organization
+**Organization type examples** (translate to target language):
+- A university → UniversityName in target language, PascalCase
+- A government agency → AgencyName in target language, PascalCase
+- Fallback for any organization → OrganizationName in target language, PascalCase
 
-**Organization types (fallback)**:
-- Organization: any organization (use when not fitting the specific types above)
-
-## Relationship Type Reference
-
-- WORKS_FOR: works for
-- STUDIES_AT: studies at
-- AFFILIATED_WITH: affiliated with
-- REPRESENTS: represents
-- REGULATES: regulates
-- REPORTS_ON: reports on
-- COMMENTS_ON: comments on
-- RESPONDS_TO: responds to
-- SUPPORTS: supports
-- OPPOSES: opposes
-- COLLABORATES_WITH: collaborates with
-- COMPETES_WITH: competes with
+**Relationship type examples** (translate to target language):
+- works for → WORKS_FOR translated to target language, UPPER_SNAKE_CASE
+- reports on → REPORTS_ON translated to target language, UPPER_SNAKE_CASE
 """
 
 
@@ -209,17 +188,17 @@ class OntologyGenerator:
             lang_instruction
         )
 
-        system_prompt = f"LANGUAGE INSTRUCTION (HIGHEST PRIORITY — MUST BE FOLLOWED): {lang_instruction} All description fields, analysis_summary, and examples MUST be written in this language.\n\n{ONTOLOGY_SYSTEM_PROMPT}\n\n{lang_instruction}\nIMPORTANT: Entity type names MUST be in English PascalCase (e.g., 'PersonEntity', 'MediaOrganization'). Relationship type names MUST be in English UPPER_SNAKE_CASE (e.g., 'WORKS_FOR'). Attribute names MUST be in English snake_case. Only description fields and analysis_summary should use the specified language above."
+        system_prompt = f"LANGUAGE INSTRUCTION (HIGHEST PRIORITY — MUST BE FOLLOWED): {lang_instruction} ALL fields including names, descriptions, analysis_summary, and examples MUST be written in this language.\n\n{ONTOLOGY_SYSTEM_PROMPT}\n\n{lang_instruction}\nIMPORTANT: Entity type names MUST be in PascalCase (e.g., 'AgenciaGovern', 'FuncionariPublic'). Relationship type names MUST be in UPPER_SNAKE_CASE (e.g., 'TREBALLA_PER', 'RESPON_A'). Attribute names MUST be in snake_case. All names, descriptions, and examples must use the language specified above."
         messages = [
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": user_message}
         ]
 
-        # Call LLM
+        # Call LLM — token budget scales with ONTOLOGY_MAX_ENTITY_TYPES / ONTOLOGY_MAX_EDGE_TYPES
         result = self.llm_client.chat_json(
             messages=messages,
             temperature=0.3,
-            max_tokens=4096
+            max_tokens=8192
         )
 
         # Validate and post-process
@@ -264,15 +243,21 @@ class OntologyGenerator:
 {additional_context}
 """
 
+        max_entities = Config.ONTOLOGY_MAX_ENTITY_TYPES
+        max_edges = Config.ONTOLOGY_MAX_EDGE_TYPES
+        specific_entities = max_entities - 2
+        edge_min = max(1, max_edges - 2)
+
         message += f"""
 Based on the content above, design entity types and relationship types suitable for social opinion simulation.
 
 **Mandatory rules**:
-1. Output exactly 10 entity types
+1. Output exactly {max_entities} entity types
 2. The last 2 must be fallback types: Person (individual fallback) and Organization (organization fallback)
-3. The first 8 are specific types designed from the document content
+3. The first {specific_entities} are specific types designed from the document content
 4. All entity types must be real-world subjects capable of speaking out, not abstract concepts
 5. Attribute names must not use reserved words: name, uuid, group_id — use full_name, org_name, etc. instead
+6. Output {edge_min}-{max_edges} relationship types covering hierarchical, collaborative, adversarial, and informational relationships
 
 {lang_instruction}
 """
@@ -330,9 +315,8 @@ Based on the content above, design entity types and relationship types suitable
             if len(edge.get("description", "")) > 100:
                 edge["description"] = edge["description"][:97] + "..."
 
-        # Zep API limit: maximum 10 custom entity types and 10 custom edge types
-        MAX_ENTITY_TYPES = 10
-        MAX_EDGE_TYPES = 10
+        MAX_ENTITY_TYPES = Config.ONTOLOGY_MAX_ENTITY_TYPES
+        MAX_EDGE_TYPES = Config.ONTOLOGY_MAX_EDGE_TYPES
 
         # Deduplicate: keep first occurrence by name
         seen_names = set()
@@ -346,31 +330,35 @@ Based on the content above, design entity types and relationship types suitable
                 logger.warning(f"Duplicate entity type '{name}' removed during validation")
         result["entity_types"] = deduped
 
-        # Fallback type definitions
+        # Fallback type definitions — names and descriptions come from i18n so they match
+        # the locale used for the rest of the ontology (e.g. "Persona"/"Organització" in Catalan).
+        person_fallback_name = _to_pascal_case(t("step1.ontologyFallbackPersonName") or "Person")
+        org_fallback_name = _to_pascal_case(t("step1.ontologyFallbackOrgName") or "Organization")
+
         person_fallback = {
-            "name": "Person",
-            "description": "Any individual person not fitting other specific person types.",
+            "name": person_fallback_name,
+            "description": t("step1.ontologyFallbackPersonDesc") or "Any individual person not fitting other specific person types.",
             "attributes": [
                 {"name": "full_name", "type": "text", "description": "Full name of the person"},
                 {"name": "role", "type": "text", "description": "Role or occupation"}
             ],
-            "examples": ["ordinary citizen", "anonymous netizen"]
+            "examples": t("step1.ontologyFallbackPersonExamples") or ["ordinary citizen", "anonymous netizen"]
         }
 
         organization_fallback = {
-            "name": "Organization",
-            "description": "Any organization not fitting other specific organization types.",
+            "name": org_fallback_name,
+            "description": t("step1.ontologyFallbackOrgDesc") or "Any organization not fitting other specific organization types.",
             "attributes": [
                 {"name": "org_name", "type": "text", "description": "Name of the organization"},
                 {"name": "org_type", "type": "text", "description": "Type of organization"}
             ],
-            "examples": ["small business", "community group"]
+            "examples": t("step1.ontologyFallbackOrgExamples") or ["small business", "community group"]
         }
 
-        # Check whether fallback types already exist
+        # Check whether fallback types already exist (match by i18n name)
         entity_names = {e["name"] for e in result["entity_types"]}
-        has_person = "Person" in entity_names
-        has_organization = "Organization" in entity_names
+        has_person = person_fallback_name in entity_names
+        has_organization = org_fallback_name in entity_names
 
         # Collect fallback types to add
         fallbacks_to_add = []
@@ -383,11 +371,9 @@ Based on the content above, design entity types and relationship types suitable
             current_count = len(result["entity_types"])
             needed_slots = len(fallbacks_to_add)
 
-            # If adding them would exceed 10, remove some existing types
+            # If adding them would exceed the limit, remove some existing types from the end
             if current_count + needed_slots > MAX_ENTITY_TYPES:
-                # Calculate how many to remove
                 to_remove = current_count + needed_slots - MAX_ENTITY_TYPES
-                # Remove from the end (preserve the more important specific types at the front)
                 result["entity_types"] = result["entity_types"][:-to_remove]
 
             # Add fallback types

backend/app/services/simulation_config_generator.py

@@ -23,6 +23,7 @@ from openai import OpenAI
 from ..config import Config
 from ..utils.logger import get_logger
 from ..utils.locale import get_language_instruction, t
+from ..utils.llm_client import parse_azure_url
 from .zep_entity_reader import EntityNode, ZepEntityReader
 
 logger = get_logger('mirofish.simulation_config')
@@ -231,15 +232,17 @@ class SimulationConfigGenerator:
         model_name: Optional[str] = None
     ):
         self.api_key = api_key or Config.LLM_API_KEY
-        self.base_url = base_url or Config.LLM_BASE_URL
+        raw_url = base_url or Config.LLM_BASE_URL
         self.model_name = model_name or Config.LLM_MODEL_NAME
 
         if not self.api_key:
             raise ValueError("LLM_API_KEY is not configured")
 
+        self.base_url, _default_query = parse_azure_url(raw_url)
         self.client = OpenAI(
             api_key=self.api_key,
-            base_url=self.base_url
+            base_url=self.base_url,
+            default_query=_default_query if _default_query else None
         )
 
     def generate_config(

backend/app/services/zep_entity_reader.py

@@ -7,11 +7,9 @@ import time
 from typing import Dict, Any, List, Optional, Set, Callable, TypeVar
 from dataclasses import dataclass, field
 
-from zep_cloud.client import Zep
-
 from ..config import Config
+from ..graph import get_graph_backend
 from ..utils.logger import get_logger
-from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
 
 logger = get_logger('mirofish.zep_entity_reader')
 
@@ -79,11 +77,7 @@ class ZepEntityReader:
     """
 
     def __init__(self, api_key: Optional[str] = None):
-        self.api_key = api_key or Config.ZEP_API_KEY
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY is not configured")
-
-        self.client = Zep(api_key=self.api_key)
+        self._graph = get_graph_backend()
 
     def _call_with_retry(
         self,
@@ -136,18 +130,7 @@ class ZepEntityReader:
         """
         logger.info(f"Fetching all nodes for graph {graph_id}...")
 
-        nodes = fetch_all_nodes(self.client, graph_id)
-
-        nodes_data = []
-        for node in nodes:
-            nodes_data.append({
-                "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                "name": node.name or "",
-                "labels": node.labels or [],
-                "summary": node.summary or "",
-                "attributes": node.attributes or {},
-            })
-
+        nodes_data = self._graph.get_all_nodes(graph_id)
         logger.info(f"Fetched {len(nodes_data)} nodes")
         return nodes_data
 
@@ -163,19 +146,7 @@ class ZepEntityReader:
         """
         logger.info(f"Fetching all edges for graph {graph_id}...")
 
-        edges = fetch_all_edges(self.client, graph_id)
-
-        edges_data = []
-        for edge in edges:
-            edges_data.append({
-                "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
-                "name": edge.name or "",
-                "fact": edge.fact or "",
-                "source_node_uuid": edge.source_node_uuid,
-                "target_node_uuid": edge.target_node_uuid,
-                "attributes": edge.attributes or {},
-            })
-
+        edges_data = self._graph.get_all_edges(graph_id)
         logger.info(f"Fetched {len(edges_data)} edges")
         return edges_data
 
@@ -190,24 +161,7 @@ class ZepEntityReader:
             Edge list
         """
         try:
-            # Call Zep API with retry
-            edges = self._call_with_retry(
-                func=lambda: self.client.graph.node.get_entity_edges(node_uuid=node_uuid),
-                operation_name=f"get node edges (node={node_uuid[:8]}...)"
-            )
-
-            edges_data = []
-            for edge in edges:
-                edges_data.append({
-                    "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
-                    "name": edge.name or "",
-                    "fact": edge.fact or "",
-                    "source_node_uuid": edge.source_node_uuid,
-                    "target_node_uuid": edge.target_node_uuid,
-                    "attributes": edge.attributes or {},
-                })
-
-            return edges_data
+            return self._graph.get_node_edges(node_uuid)
         except Exception as e:
             logger.warning(f"Failed to get edges for node {node_uuid}: {str(e)}")
             return []
@@ -346,11 +300,7 @@ class ZepEntityReader:
             EntityNode or None
         """
         try:
-            # Get the node with retry
-            node = self._call_with_retry(
-                func=lambda: self.client.graph.node.get(uuid_=entity_uuid),
-                operation_name=f"get node detail (uuid={entity_uuid[:8]}...)"
-            )
+            node = self._graph.get_node(entity_uuid)
 
             if not node:
                 return None
@@ -397,11 +347,11 @@ class ZepEntityReader:
                     })
 
             return EntityNode(
-                uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                name=node.name or "",
-                labels=node.labels or [],
-                summary=node.summary or "",
-                attributes=node.attributes or {},
+                uuid=node.get("uuid", ""),
+                name=node.get("name", ""),
+                labels=node.get("labels", []),
+                summary=node.get("summary", ""),
+                attributes=node.get("attributes", {}),
                 related_edges=related_edges,
                 related_nodes=related_nodes,
             )

backend/app/services/zep_graph_memory_updater.py

@@ -12,9 +12,8 @@ from dataclasses import dataclass
 from datetime import datetime
 from queue import Queue, Empty
 
-from zep_cloud.client import Zep
-
 from ..config import Config
+from ..graph import get_graph_backend
 from ..utils.logger import get_logger
 from ..utils.locale import get_locale, set_locale
 
@@ -240,12 +239,7 @@ class ZepGraphMemoryUpdater:
             api_key: Zep API key (optional; defaults to config value)
         """
         self.graph_id = graph_id
-        self.api_key = api_key or Config.ZEP_API_KEY
-
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY is not configured")
-
-        self.client = Zep(api_key=self.api_key)
+        self._graph = get_graph_backend()
 
         # Activity queue
         self._activity_queue: Queue = Queue()
@@ -413,11 +407,7 @@ class ZepGraphMemoryUpdater:
         # Send with retry
         for attempt in range(self.MAX_RETRIES):
             try:
-                self.client.graph.add(
-                    graph_id=self.graph_id,
-                    type="text",
-                    data=combined_text
-                )
+                self._graph.add_text(self.graph_id, combined_text)
 
                 self._total_sent += 1
                 self._total_items_sent += len(activities)

backend/app/services/zep_tools.py

@@ -13,13 +13,11 @@ import json
 from typing import Dict, Any, List, Optional
 from dataclasses import dataclass, field
 
-from zep_cloud.client import Zep
-
 from ..config import Config
+from ..graph import get_graph_backend
 from ..utils.logger import get_logger
 from ..utils.llm_client import LLMClient
 from ..utils.locale import get_locale, t
-from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
 
 logger = get_logger('mirofish.zep_tools')
 
@@ -423,12 +421,7 @@ class ZepToolsService:
     RETRY_DELAY = 2.0
     
     def __init__(self, api_key: Optional[str] = None, llm_client: Optional[LLMClient] = None):
-        self.api_key = api_key or Config.ZEP_API_KEY
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY is not configured")
-        
-        self.client = Zep(api_key=self.api_key)
-        # LLM client used by InsightForge to generate sub-queries
+        self._graph = get_graph_backend()
         self._llm_client = llm_client
         logger.info(t("console.zepToolsInitialized"))
     
@@ -485,51 +478,38 @@ class ZepToolsService:
         """
         logger.info(t("console.graphSearch", graphId=graph_id, query=query[:50]))
         
-        # Try using the Zep Cloud Search API
         try:
-            search_results = self._call_with_retry(
-                func=lambda: self.client.graph.search(
-                    graph_id=graph_id,
-                    query=query,
-                    limit=limit,
-                    scope=scope,
-                    reranker="cross_encoder"
-                ),
-                operation_name=t("console.graphSearchOp", graphId=graph_id)
-            )
-            
+            raw = self._graph.search(graph_id=graph_id, query=query, limit=limit, scope=scope)
+
             facts = []
             edges = []
             nodes = []
-            
-            # Parse edge search results
-            if hasattr(search_results, 'edges') and search_results.edges:
-                for edge in search_results.edges:
-                    if hasattr(edge, 'fact') and edge.fact:
-                        facts.append(edge.fact)
-                    edges.append({
-                        "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
-                        "name": getattr(edge, 'name', ''),
-                        "fact": getattr(edge, 'fact', ''),
-                        "source_node_uuid": getattr(edge, 'source_node_uuid', ''),
-                        "target_node_uuid": getattr(edge, 'target_node_uuid', ''),
-                    })
-            
-            # Parse node search results
-            if hasattr(search_results, 'nodes') and search_results.nodes:
-                for node in search_results.nodes:
-                    nodes.append({
-                        "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                        "name": getattr(node, 'name', ''),
-                        "labels": getattr(node, 'labels', []),
-                        "summary": getattr(node, 'summary', ''),
-                    })
-                    # Node summaries count as facts too
-                    if hasattr(node, 'summary') and node.summary:
-                        facts.append(f"[{node.name}]: {node.summary}")
-            
+
+            for edge in raw.get("edges", []) or []:
+                fact = edge.get("fact", "") if isinstance(edge, dict) else getattr(edge, "fact", "")
+                if fact:
+                    facts.append(fact)
+                edges.append(edge if isinstance(edge, dict) else {
+                    "uuid": getattr(edge, "uuid_", None) or getattr(edge, "uuid", ""),
+                    "name": getattr(edge, "name", ""),
+                    "fact": getattr(edge, "fact", ""),
+                    "source_node_uuid": getattr(edge, "source_node_uuid", ""),
+                    "target_node_uuid": getattr(edge, "target_node_uuid", ""),
+                })
+
+            for node in raw.get("nodes", []) or []:
+                node_dict = node if isinstance(node, dict) else {
+                    "uuid": getattr(node, "uuid_", None) or getattr(node, "uuid", ""),
+                    "name": getattr(node, "name", ""),
+                    "labels": getattr(node, "labels", []),
+                    "summary": getattr(node, "summary", ""),
+                }
+                nodes.append(node_dict)
+                if node_dict.get("summary"):
+                    facts.append(f"[{node_dict['name']}]: {node_dict['summary']}")
+
             logger.info(t("console.searchComplete", count=len(facts)))
-            
+
             return SearchResult(
                 facts=facts,
                 edges=edges,
@@ -659,18 +639,18 @@ class ZepToolsService:
         """
         logger.info(t("console.fetchingAllNodes", graphId=graph_id))
 
-        nodes = fetch_all_nodes(self.client, graph_id)
+        nodes = self._graph.get_all_nodes(graph_id)
 
-        result = []
-        for node in nodes:
-            node_uuid = getattr(node, 'uuid_', None) or getattr(node, 'uuid', None) or ""
-            result.append(NodeInfo(
-                uuid=str(node_uuid) if node_uuid else "",
-                name=node.name or "",
-                labels=node.labels or [],
-                summary=node.summary or "",
-                attributes=node.attributes or {}
-            ))
+        result = [
+            NodeInfo(
+                uuid=n.get("uuid", ""),
+                name=n.get("name", ""),
+                labels=n.get("labels", []),
+                summary=n.get("summary", ""),
+                attributes=n.get("attributes", {})
+            )
+            for n in nodes
+        ]
 
         logger.info(t("console.fetchedNodes", count=len(result)))
         return result
@@ -688,26 +668,22 @@ class ZepToolsService:
         """
         logger.info(t("console.fetchingAllEdges", graphId=graph_id))
 
-        edges = fetch_all_edges(self.client, graph_id)
+        edges = self._graph.get_all_edges(graph_id)
 
         result = []
-        for edge in edges:
-            edge_uuid = getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', None) or ""
+        for e in edges:
             edge_info = EdgeInfo(
-                uuid=str(edge_uuid) if edge_uuid else "",
-                name=edge.name or "",
-                fact=edge.fact or "",
-                source_node_uuid=edge.source_node_uuid or "",
-                target_node_uuid=edge.target_node_uuid or ""
+                uuid=e.get("uuid", ""),
+                name=e.get("name", ""),
+                fact=e.get("fact", ""),
+                source_node_uuid=e.get("source_node_uuid", ""),
+                target_node_uuid=e.get("target_node_uuid", ""),
             )
-
-            # Add temporal info
             if include_temporal:
-                edge_info.created_at = getattr(edge, 'created_at', None)
-                edge_info.valid_at = getattr(edge, 'valid_at', None)
-                edge_info.invalid_at = getattr(edge, 'invalid_at', None)
-                edge_info.expired_at = getattr(edge, 'expired_at', None)
-
+                edge_info.created_at = e.get("created_at")
+                edge_info.valid_at = e.get("valid_at")
+                edge_info.invalid_at = e.get("invalid_at")
+                edge_info.expired_at = e.get("expired_at")
             result.append(edge_info)
 
         logger.info(t("console.fetchedEdges", count=len(result)))
@@ -726,20 +702,17 @@ class ZepToolsService:
         logger.info(t("console.fetchingNodeDetail", uuid=node_uuid[:8]))
         
         try:
-            node = self._call_with_retry(
-                func=lambda: self.client.graph.node.get(uuid_=node_uuid),
-                operation_name=t("console.fetchNodeDetailOp", uuid=node_uuid[:8])
-            )
-            
+            node = self._graph.get_node(node_uuid)
+
             if not node:
                 return None
-            
+
             return NodeInfo(
-                uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                name=node.name or "",
-                labels=node.labels or [],
-                summary=node.summary or "",
-                attributes=node.attributes or {}
+                uuid=node.get("uuid", ""),
+                name=node.get("name", ""),
+                labels=node.get("labels", []),
+                summary=node.get("summary", ""),
+                attributes=node.get("attributes", {})
             )
         except Exception as e:
             logger.error(t("console.fetchNodeDetailFailed", error=str(e)))

backend/app/utils/llm_client.py

@@ -12,6 +12,29 @@ from openai import OpenAI
 from ..config import Config
 
 
+def parse_azure_url(raw_url: str):
+    """Strip /chat/completions or /embeddings suffix from Azure endpoint URLs.
+
+    Azure Portal gives full URLs like:
+      https://<resource>.cognitiveservices.azure.com/openai/deployments/<model>/chat/completions?api-version=...
+    The OpenAI SDK expects a base_url and appends /chat/completions itself.
+
+    Returns (clean_base_url, default_query_dict).
+    """
+    default_query: Dict[str, str] = {}
+    if raw_url and ('/chat/completions' in raw_url or '/embeddings' in raw_url):
+        parsed = urlparse(raw_url)
+        qs = parse_qs(parsed.query)
+        if 'api-version' in qs:
+            default_query['api-version'] = qs['api-version'][0]
+        clean_path = (parsed.path
+                      .replace('/chat/completions', '')
+                      .replace('/embeddings', '')
+                      .rstrip('/'))
+        raw_url = urlunparse(parsed._replace(path=clean_path, query=''))
+    return raw_url, default_query
+
+
 class LLMClient:
     """LLM client"""
 
@@ -28,18 +51,11 @@ class LLMClient:
         if not self.api_key:
             raise ValueError("LLM_API_KEY is not configured")
 
-        # Azure Portal provides full endpoint URLs like:
-        # https://<resource>.cognitiveservices.azure.com/openai/deployments/<model>/chat/completions?api-version=...
-        # The OpenAI SDK expects a base_url and appends /chat/completions itself,
-        # so we strip that suffix and extract api-version as a default query param.
-        default_query: Dict[str, str] = {}
-        if raw_url and '/chat/completions' in raw_url:
-            parsed = urlparse(raw_url)
-            qs = parse_qs(parsed.query)
-            if 'api-version' in qs:
-                default_query['api-version'] = qs['api-version'][0]
-            clean_path = parsed.path.replace('/chat/completions', '').rstrip('/')
-            raw_url = urlunparse(parsed._replace(path=clean_path, query=''))
+        # Google AI Studio OpenAI-compatible endpoint
+        if (Config.LLM_PROVIDER or "").lower() == "gemini" and not base_url:
+            raw_url = "https://generativelanguage.googleapis.com/v1beta/openai/"
+
+        raw_url, default_query = parse_azure_url(raw_url)
 
         self.base_url = raw_url
         self.client = OpenAI(

backend/pyproject.toml

@@ -37,6 +37,10 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
+graphiti = [
+    "graphiti-core>=0.3.0",
+    "neo4j>=5.23.0",
+]
 dev = [
     "pytest>=8.0.0",
     "pytest-asyncio>=0.23.0",

backend/tests/conftest.py

@@ -0,0 +1,12 @@
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def reset_graph_factory_singleton():
+    """Reset the graph backend singleton before each test to avoid cross-test contamination."""
+    yield
+    try:
+        import backend.app.graph.factory as fmod
+        fmod._backend_instance = None
+    except ImportError:
+        pass

backend/tests/test_graph_factory.py

@@ -0,0 +1,124 @@
+import pytest
+from unittest.mock import MagicMock, patch
+
+
+def test_graph_backend_has_required_methods():
+    from backend.app.graph.base import GraphBackend
+    required = [
+        "create_graph", "set_ontology", "add_batch", "get_episode",
+        "get_all_nodes", "get_all_edges", "get_node", "get_node_edges",
+        "search", "add_text", "delete_graph",
+    ]
+    for method in required:
+        assert hasattr(GraphBackend, method), f"GraphBackend missing: {method}"
+
+
+def test_config_graph_backend_default():
+    import os
+    os.environ.pop("GRAPH_BACKEND", None)
+    import importlib
+    import backend.app.config as cfg_mod
+    importlib.reload(cfg_mod)
+    assert cfg_mod.Config.GRAPH_BACKEND == "zep"
+
+
+def test_config_zep_errors_when_key_missing():
+    import backend.app.config as cfg_mod
+    orig_backend = cfg_mod.Config.GRAPH_BACKEND
+    orig_key = cfg_mod.Config.ZEP_API_KEY
+    try:
+        cfg_mod.Config.GRAPH_BACKEND = "zep"
+        cfg_mod.Config.ZEP_API_KEY = None
+        errors = cfg_mod.Config.get_graph_config_errors()
+        assert any("ZEP_API_KEY" in e for e in errors)
+    finally:
+        cfg_mod.Config.GRAPH_BACKEND = orig_backend
+        cfg_mod.Config.ZEP_API_KEY = orig_key
+
+
+def test_zep_backend_implements_interface():
+    from backend.app.graph.base import GraphBackend
+    from backend.app.graph.zep_backend import ZepBackend
+    assert issubclass(ZepBackend, GraphBackend)
+
+
+def test_zep_backend_raises_without_key():
+    import backend.app.config as cfg_mod
+    orig = cfg_mod.Config.ZEP_API_KEY
+    try:
+        cfg_mod.Config.ZEP_API_KEY = None
+        from backend.app.graph.zep_backend import ZepBackend
+        with pytest.raises(ValueError, match="ZEP_API_KEY"):
+            ZepBackend()
+    finally:
+        cfg_mod.Config.ZEP_API_KEY = orig
+
+
+def test_factory_returns_zep_by_default():
+    import backend.app.graph.factory as fmod
+    import backend.app.config as cfg
+    orig_backend = cfg.Config.GRAPH_BACKEND
+    orig_key = cfg.Config.ZEP_API_KEY
+    try:
+        cfg.Config.GRAPH_BACKEND = "zep"
+        cfg.Config.ZEP_API_KEY = "test-key"
+        fmod._backend_instance = None
+        backend_instance = fmod.get_graph_backend()
+        from backend.app.graph.zep_backend import ZepBackend
+        assert isinstance(backend_instance, ZepBackend)
+    finally:
+        cfg.Config.GRAPH_BACKEND = orig_backend
+        cfg.Config.ZEP_API_KEY = orig_key
+        fmod._backend_instance = None
+
+
+def test_factory_raises_on_unknown_backend():
+    import backend.app.graph.factory as fmod
+    import backend.app.config as cfg
+    orig = cfg.Config.GRAPH_BACKEND
+    try:
+        cfg.Config.GRAPH_BACKEND = "unknown"
+        fmod._backend_instance = None
+        with pytest.raises(ValueError, match="Unknown GRAPH_BACKEND"):
+            fmod.get_graph_backend()
+    finally:
+        cfg.Config.GRAPH_BACKEND = orig
+        fmod._backend_instance = None
+
+
+def test_graphiti_backend_importable():
+    try:
+        from backend.app.graph.graphiti_backend import GraphitiBackend
+        from backend.app.graph.base import GraphBackend
+        assert issubclass(GraphitiBackend, GraphBackend)
+    except ImportError as e:
+        pytest.skip(f"graphiti-core not installed: {e}")
+
+
+def test_graphiti_backend_raises_without_password():
+    try:
+        from backend.app.graph.graphiti_backend import GraphitiBackend
+    except ImportError:
+        pytest.skip("graphiti-core not installed")
+    import backend.app.config as cfg_mod
+    orig = cfg_mod.Config.NEO4J_PASSWORD
+    try:
+        cfg_mod.Config.NEO4J_PASSWORD = None
+        with pytest.raises(ValueError, match="NEO4J_PASSWORD"):
+            GraphitiBackend()
+    finally:
+        cfg_mod.Config.NEO4J_PASSWORD = orig
+
+
+def test_config_graphiti_errors_when_missing():
+    import backend.app.config as cfg_mod
+    orig_backend = cfg_mod.Config.GRAPH_BACKEND
+    orig_pw = cfg_mod.Config.NEO4J_PASSWORD
+    try:
+        cfg_mod.Config.GRAPH_BACKEND = "graphiti"
+        cfg_mod.Config.NEO4J_PASSWORD = None
+        errors = cfg_mod.Config.get_graph_config_errors()
+        assert len(errors) >= 1
+    finally:
+        cfg_mod.Config.GRAPH_BACKEND = orig_backend
+        cfg_mod.Config.NEO4J_PASSWORD = orig_pw

backend/tests/test_llm_client.py

@@ -0,0 +1,72 @@
+import pytest
+from unittest.mock import patch, MagicMock
+
+GEMINI_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
+
+
+def test_gemini_provider_sets_base_url_automatically():
+    import backend.app.config as cfg
+    orig_provider = cfg.Config.LLM_PROVIDER
+    orig_key = cfg.Config.LLM_API_KEY
+    orig_url = cfg.Config.LLM_BASE_URL
+    try:
+        cfg.Config.LLM_PROVIDER = "gemini"
+        cfg.Config.LLM_API_KEY = "AIzatest"
+        cfg.Config.LLM_BASE_URL = "https://api.openai.com/v1"
+
+        with patch("backend.app.utils.llm_client.OpenAI") as mock_openai:
+            mock_openai.return_value = MagicMock()
+            import importlib
+            import backend.app.utils.llm_client as lm
+            importlib.reload(lm)
+            client = lm.LLMClient()
+            assert GEMINI_URL in client.base_url
+    finally:
+        cfg.Config.LLM_PROVIDER = orig_provider
+        cfg.Config.LLM_API_KEY = orig_key
+        cfg.Config.LLM_BASE_URL = orig_url
+
+
+def test_non_gemini_provider_uses_configured_url():
+    import backend.app.config as cfg
+    orig_provider = cfg.Config.LLM_PROVIDER
+    orig_key = cfg.Config.LLM_API_KEY
+    orig_url = cfg.Config.LLM_BASE_URL
+    try:
+        cfg.Config.LLM_PROVIDER = ""
+        cfg.Config.LLM_API_KEY = "sk-test"
+        cfg.Config.LLM_BASE_URL = "https://api.openai.com/v1"
+
+        with patch("backend.app.utils.llm_client.OpenAI") as mock_openai:
+            mock_openai.return_value = MagicMock()
+            import importlib
+            import backend.app.utils.llm_client as lm
+            importlib.reload(lm)
+            client = lm.LLMClient()
+            assert "openai.com" in client.base_url
+    finally:
+        cfg.Config.LLM_PROVIDER = orig_provider
+        cfg.Config.LLM_API_KEY = orig_key
+        cfg.Config.LLM_BASE_URL = orig_url
+
+
+def test_explicit_base_url_overrides_gemini_auto():
+    """If base_url is passed explicitly, it should NOT be replaced even if LLM_PROVIDER=gemini."""
+    import backend.app.config as cfg
+    orig_provider = cfg.Config.LLM_PROVIDER
+    orig_key = cfg.Config.LLM_API_KEY
+    try:
+        cfg.Config.LLM_PROVIDER = "gemini"
+        cfg.Config.LLM_API_KEY = "AIzatest"
+
+        with patch("backend.app.utils.llm_client.OpenAI") as mock_openai:
+            mock_openai.return_value = MagicMock()
+            import importlib
+            import backend.app.utils.llm_client as lm
+            importlib.reload(lm)
+            client = lm.LLMClient(base_url="https://custom.endpoint/v1")
+            assert "custom.endpoint" in client.base_url
+            assert GEMINI_URL not in client.base_url
+    finally:
+        cfg.Config.LLM_PROVIDER = orig_provider
+        cfg.Config.LLM_API_KEY = orig_key

backend/uv.lock

@@ -475,6 +475,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "diskcache"
+version = "5.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/21/1c1ffc1a039ddcc459db43cc108658f32c57d271d7289a2794e401d0fdb6/diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc", size = 67916, upload-time = "2023-08-31T06:12:00.316Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/27/4570e78fc0bf5ea0ca45eb1de3818a23787af9b390c0b0a0033a1b8236f9/diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19", size = 45550, upload-time = "2023-08-31T06:11:58.822Z" },
+]
+
 [[package]]
 name = "distlib"
 version = "0.4.0"
@@ -592,6 +601,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/51/c7/b64cae5dba3a1b138d7123ec36bb5ccd39d39939f18454407e5468f4763f/fsspec-2025.12.0-py3-none-any.whl", hash = "sha256:8bf1fe301b7d8acfa6e8571e3b1c3d158f909666642431cc78a1b7b4dbc5ec5b", size = 201422, upload-time = "2025-12-03T15:23:41.434Z" },
 ]
 
+[[package]]
+name = "graphiti-core"
+version = "0.11.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "diskcache" },
+    { name = "neo4j" },
+    { name = "numpy" },
+    { name = "openai" },
+    { name = "pydantic" },
+    { name = "python-dotenv" },
+    { name = "tenacity" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/30/94/3f84400e5f02ea8e9dc79784202de4173cbc16f4b3ad1bd4302da888e4d8/graphiti_core-0.11.6.tar.gz", hash = "sha256:31d26621834d7d4b8865059ab749feb18af15937b59c69598a640a5dfabea331", size = 71928, upload-time = "2025-05-15T17:58:02.304Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ac/2e/c8f22f01585bf173d1c82f6d4615511aebc75aeda764c69aa394446fa93c/graphiti_core-0.11.6-py3-none-any.whl", hash = "sha256:6ec4807a884f5ea88b942d0c8b7bcd2e107c7358ab4f98ef2a2092c229929707", size = 111001, upload-time = "2025-05-15T17:58:00.542Z" },
+]
+
 [[package]]
 name = "gunicorn"
 version = "25.3.0"
@@ -1275,6 +1302,10 @@ dev = [
     { name = "pytest" },
     { name = "pytest-asyncio" },
 ]
+graphiti = [
+    { name = "graphiti-core" },
+    { name = "neo4j" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -1290,7 +1321,9 @@ requires-dist = [
     { name = "charset-normalizer", specifier = ">=3.0.0" },
     { name = "flask", specifier = ">=3.0.0" },
     { name = "flask-cors", specifier = ">=6.0.0" },
+    { name = "graphiti-core", marker = "extra == 'graphiti'", specifier = ">=0.3.0" },
     { name = "gunicorn", specifier = ">=22.0.0" },
+    { name = "neo4j", marker = "extra == 'graphiti'", specifier = ">=5.23.0" },
     { name = "openai", specifier = ">=1.0.0" },
     { name = "pipreqs", marker = "extra == 'dev'", specifier = ">=0.5.0" },
     { name = "pydantic", specifier = ">=2.0.0" },
@@ -1301,7 +1334,7 @@ requires-dist = [
     { name = "python-dotenv", specifier = ">=1.0.0" },
     { name = "zep-cloud", specifier = "==3.13.0" },
 ]
-provides-extras = ["dev"]
+provides-extras = ["graphiti", "dev"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -3003,6 +3036,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252, upload-time = "2022-10-06T17:21:44.262Z" },
 ]
 
+[[package]]
+name = "tenacity"
+version = "9.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/c6/ee486fd809e357697ee8a44d3d69222b344920433d3b6666ccd9b374630c/tenacity-9.1.4.tar.gz", hash = "sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a", size = 49413, upload-time = "2026-02-07T10:45:33.841Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d7/c1/eb8f9debc45d3b7918a32ab756658a0904732f75e555402972246b0b8e71/tenacity-9.1.4-py3-none-any.whl", hash = "sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55", size = 28926, upload-time = "2026-02-07T10:45:32.24Z" },
+]
+
 [[package]]
 name = "texttable"
 version = "1.7.0"

conftest.py

@@ -0,0 +1,5 @@
+import sys
+import os
+
+# Allow `import backend.app.*` from the project root
+sys.path.insert(0, os.path.dirname(__file__))

frontend/src/components/GraphPanel.vue

@@ -77,10 +77,12 @@
             <div class="detail-section" v-if="selectedItem.data.attributes && Object.keys(selectedItem.data.attributes).length > 0">
               <div class="section-title">Properties:</div>
               <div class="properties-list">
-                <div v-for="(value, key) in selectedItem.data.attributes" :key="key" class="property-item">
-                  <span class="property-key">{{ key }}:</span>
-                  <span class="property-value">{{ value || 'None' }}</span>
-                </div>
+                <template v-for="(value, key) in selectedItem.data.attributes" :key="key">
+                  <div class="property-item" v-if="!String(key).endsWith('_embedding') && !Array.isArray(value) && key !== 'summary'">
+                    <span class="property-key">{{ key }}:</span>
+                    <span class="property-value">{{ value || 'None' }}</span>
+                  </div>
+                </template>
               </div>
             </div>
             
@@ -288,8 +290,10 @@ const entityTypes = computed(() => {
   // 美观的颜色调色板
   const colors = ['#FF6B35', '#004E89', '#7B2D8E', '#1A936F', '#C5283D', '#E9724C', '#3498db', '#9b59b6', '#27ae60', '#f39c12']
   
-  props.graphData.nodes.forEach(node => {
-    const type = node.labels?.find(l => l !== 'Entity') || 'Entity'
+  const HIDDEN = new Set(['Episodic', 'Community', 'EpisodicEdge'])
+  const SKIP = new Set(['Entity', 'Episodic', 'Community'])
+  props.graphData.nodes.filter(n => !n.labels?.some(l => HIDDEN.has(l))).forEach(node => {
+    const type = node.labels?.find(l => !SKIP.has(l)) || 'Entity'
     if (!typeMap[type]) {
       typeMap[type] = { name: type, count: 0, color: colors[Object.keys(typeMap).length % colors.length] }
     }
@@ -344,19 +348,23 @@ const renderGraph = () => {
     
   svg.selectAll('*').remove()
   
-  const nodesData = props.graphData.nodes || []
+  const HIDDEN_LABELS = new Set(['Episodic', 'Community', 'EpisodicEdge'])
+  const nodesData = (props.graphData.nodes || []).filter(
+    n => !n.labels?.some(l => HIDDEN_LABELS.has(l))
+  )
   const edgesData = props.graphData.edges || []
-  
+
   if (nodesData.length === 0) return
 
   // Prep data
   const nodeMap = {}
   nodesData.forEach(n => nodeMap[n.uuid] = n)
-  
+
+  const SKIP_TYPE_LABELS = new Set(['Entity', 'Episodic', 'Community'])
   const nodes = nodesData.map(n => ({
     id: n.uuid,
     name: n.name || 'Unnamed',
-    type: n.labels?.find(l => l !== 'Entity') || 'Entity',
+    type: n.labels?.find(l => !SKIP_TYPE_LABELS.has(l)) || 'Entity',
     rawData: n
   }))
   

locales/ca.json

@@ -89,6 +89,12 @@
     "ontologyGenerating": "Generant",
     "ontologyPending": "Pendent",
     "ontologyDesc": "El LLM analitza el contingut del document i els requisits de simulació, extreu llavors de realitat i auto-genera una estructura d'ontologia adequada",
+    "ontologyFallbackPersonName": "Persona",
+    "ontologyFallbackPersonDesc": "Qualsevol persona individual que no encaixa en altres tipus de persona més específics.",
+    "ontologyFallbackPersonExamples": ["ciutadà ordinari", "internauta anònim"],
+    "ontologyFallbackOrgName": "Organització",
+    "ontologyFallbackOrgDesc": "Qualsevol organització que no encaixa en altres tipus d'organització més específics.",
+    "ontologyFallbackOrgExamples": ["petita empresa", "grup comunitari"],
     "analyzingDocs": "Analitzant documents...",
     "graphRagBuild": "Construcció de GraphRAG",
     "graphRagDesc": "Basant-se en l'ontologia generada, els documents es divideixen automàticament en fragments i s'envien a Zep per construir un graf de coneixement, extraient entitats i relacions, formant memòria temporal i resums de comunitat",

locales/en.json

@@ -89,6 +89,12 @@
     "ontologyGenerating": "Generating",
     "ontologyPending": "Pending",
     "ontologyDesc": "LLM analyzes document content and simulation requirements, extracts reality seeds, and auto-generates a suitable ontology structure",
+    "ontologyFallbackPersonName": "Person",
+    "ontologyFallbackPersonDesc": "Any individual person not fitting other specific person types.",
+    "ontologyFallbackPersonExamples": ["ordinary citizen", "anonymous netizen"],
+    "ontologyFallbackOrgName": "Organization",
+    "ontologyFallbackOrgDesc": "Any organization not fitting other specific organization types.",
+    "ontologyFallbackOrgExamples": ["small business", "community group"],
     "analyzingDocs": "Analyzing documents...",
     "graphRagBuild": "GraphRAG Build",
     "graphRagDesc": "Based on the generated ontology, documents are auto-chunked and sent to Zep to build a knowledge graph, extracting entities and relations, forming temporal memory and community summaries",

locales/es.json

@@ -89,6 +89,12 @@
     "ontologyGenerating": "Generando",
     "ontologyPending": "Pendiente",
     "ontologyDesc": "El LLM analiza el contenido del documento y los requisitos de simulación, extrae semillas de la realidad y genera automáticamente la estructura ontológica adecuada",
+    "ontologyFallbackPersonName": "Person",
+    "ontologyFallbackPersonDesc": "Cualquier persona individual que no encaja en otros tipos de persona más específicos.",
+    "ontologyFallbackPersonExamples": ["ciudadano ordinario", "internauta anónimo"],
+    "ontologyFallbackOrgName": "Organization",
+    "ontologyFallbackOrgDesc": "Cualquier organización que no encaja en otros tipos de organización más específicos.",
+    "ontologyFallbackOrgExamples": ["pequeña empresa", "grupo comunitario"],
     "analyzingDocs": "Analizando documentos...",
     "graphRagBuild": "Construcción de GraphRAG",
     "graphRagDesc": "A partir de la ontología generada, los documentos se fragmentan automáticamente y se envían a Zep para construir un grafo de conocimiento, extrayendo entidades y relaciones, formando memoria temporal y resúmenes de comunidad",

locales/zh.json

@@ -89,6 +89,12 @@
     "ontologyGenerating": "生成中",
     "ontologyPending": "等待",
     "ontologyDesc": "LLM分析文档内容与模拟需求，提取出现实种子，自动生成合适的本体结构",
+    "ontologyFallbackPersonName": "Person",
+    "ontologyFallbackPersonDesc": "任何不适合其他具体人物类型的个人。",
+    "ontologyFallbackPersonExamples": ["普通市民", "匿名网友"],
+    "ontologyFallbackOrgName": "Organization",
+    "ontologyFallbackOrgDesc": "任何不适合其他具体组织类型的组织。",
+    "ontologyFallbackOrgExamples": ["小型企业", "社区团体"],
     "analyzingDocs": "正在分析文档...",
     "graphRagBuild": "GraphRAG构建",
     "graphRagDesc": "基于生成的本体，将文档自动分块后调用 Zep 构建知识图谱，提取实体和关系，并形成时序记忆与社区摘要",