f1f2ea4ea5
First successful E2E test uncovered 7 blocking bugs. All fixed and deployed to production server. User confirmed full pipeline now runs through. ## Bug Fixes ### #1 LLMClient exponential backoff retry - recognize RateLimitError / 429 / 5xx / 1302 / timeout / connection errors - 5 retries with 1→2→4→8→16s backoff + random jitter - file: backend/app/utils/llm_client.py ### #2 GLM → Qwen 32B dual-LLM fallback (ontology generation) - primary LLM (智谱 GLM-4-Flash) retries exhausted → single attempt fallback to SiliconFlow Qwen 2.5-32B via GRAPHITI_LLM_* config - solves GLM low RPM quota intermittent throttling - fallback does NOT retry (avoid cascade) - file: backend/app/services/ontology_generator.py ### #3 Qwen 32K context overflow - MAX_TEXT_LENGTH_FOR_LLM 50000 → 28000 chars - ensures prompt+response stays under 32768 tokens - truncated docs get marker line "[文档已截断以适应 LLM 上下文窗口]" - file: backend/app/services/ontology_generator.py ### #4 Neo4j entity summary flatten via monkey-patch - Qwen occasionally returns {summary: {value, type, title, description}} which Neo4j rejects (properties must be primitives) - monkey-patch Neo4jEntityNodeOperations.save / save_bulk - recursive _flatten_entity_property extracts .value from nested dicts, json.dumps as string fallback - file: backend/app/services/graphiti_client.py ### #5 Frontend stuck at /process/new with no pending state - pendingUpload is in-memory reactive, lost on refresh / direct URL - MainView.handleNewProject early-returned on empty state without navigating, leaving UI permanently in "waiting for ontology" limbo - fix: detect empty state, log redirect msg, router.replace to Home after 800ms - file: frontend/src/views/MainView.vue ### #6 Semaphore 100 → 30 (OASIS simulation) - high concurrency triggered GLM rate limit cascade during profile/action LLM calls - drop to 30 eliminates 1302 retries, total runtime impact < 10% - file: backend/scripts/run_parallel_simulation.py ### #7 SimulationReplayView redesigned to Manus cinematic style - previous 3-column analyst panel felt like a dashboard, not a replay - new single-column immersive layout matching Manus's "observation window": - top breadcrumb: "Foresight is running Reddit simulation · Round 8/15" - main stage: browser chrome + native-style platform post card (Reddit subreddit header / Twitter tweet header) - action type badges, agent avatars with gradient palettes - stage meta bar: per-round action counts by type - bottom scrubber row: timestamp chip + Jump to live button + live indicator (pulsing green dot when sim is running) + step/play/step buttons + 5 speed levels - bottom task bar: current pipeline step icon/label/progress - dark theme (#0A0A0B base + #FF5722 accent) - analyst mode toggle (◫/▦) preserves original 3-column view - auto-polling every 10s while sim is running, auto-tracks live position - file: frontend/src/views/SimulationReplayView.vue ## Docs - PRD.md bumped to v0.3.1 with full hotfix changelog + decision log entries |
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README.md
简洁通用的群体智能引擎，预测万物
A Simple and Universal Swarm Intelligence Engine, Predicting Anything
⚡ Overview
Foresight is a next-generation AI prediction engine powered by multi-agent technology. By extracting seed information from the real world (such as breaking news, policy drafts, or financial signals), it automatically constructs a high-fidelity parallel digital world. Within this space, thousands of intelligent agents with independent personalities, long-term memory, and behavioral logic freely interact and undergo social evolution. You can inject variables dynamically from a "God's-eye view" to precisely deduce future trajectories — rehearse the future in a digital sandbox, and win decisions after countless simulations.
You only need to: Upload seed materials (data analysis reports or interesting novel stories) and describe your prediction requirements in natural language
Foresight will return: A detailed prediction report and a deeply interactive high-fidelity digital world
Our Vision
Foresight is dedicated to creating a swarm intelligence mirror that maps reality. By capturing the collective emergence triggered by individual interactions, we break through the limitations of traditional prediction:
- At the Macro Level: We are a rehearsal laboratory for decision-makers, allowing policies and public relations to be tested at zero risk
- At the Micro Level: We are a creative sandbox for individual users — whether deducing novel endings or exploring imaginative scenarios, everything can be fun, playful, and accessible
From serious predictions to playful simulations, we let every "what if" see its outcome, making it possible to predict anything.
🌐 Live Demo
Welcome to visit our online demo environment and experience a prediction simulation on trending public opinion events we've prepared for you: foresight-live-demo
📸 Screenshots
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🎬 Demo Videos
1. Wuhan University Public Opinion Simulation + Foresight Project Introduction
Click the image to watch the complete demo video for prediction using BettaFish-generated "Wuhan University Public Opinion Report"
2. Dream of the Red Chamber Lost Ending Simulation
Click the image to watch Foresight's deep prediction of the lost ending based on hundreds of thousands of words from the first 80 chapters of "Dream of the Red Chamber"
Financial Prediction, Political News Prediction and more examples coming soon...
🔄 Workflow
- Graph Building: Seed extraction & Individual/collective memory injection & GraphRAG construction (Graphiti + Neo4j)
- Environment Setup: Entity relationship extraction & Persona generation & Agent configuration injection (with "Skip & Continue" button)
- Simulation: Dual-platform parallel simulation (Twitter + Reddit) & Auto-parse prediction requirements & Dynamic temporal memory updates
- Report Generation: ReportAgent with rich toolset for deep interaction with post-simulation environment
- Deep Interaction: Chat with any agent in the simulated world & Interact with ReportAgent
- Process Replay (v0.3 New): Manus-style draggable timeline to replay the entire workflow and every round of agent actions — perfect for retros and demos
🚀 Quick Start
Option 1: Source Code Deployment (Recommended)
Prerequisites
| Tool | Version | Description | Check Installation |
|---|---|---|---|
| Node.js | 18+ | Frontend runtime, includes npm | node -v |
| Python | ≥3.11, ≤3.12 | Backend runtime | python --version |
| uv | Latest | Python package manager | uv --version |
1. Configure Environment Variables
# Copy the example configuration file
cp .env.example .env
# Edit the .env file and fill in the required API keys
Required Environment Variables:
# LLM API Configuration (supports any LLM API with OpenAI SDK format)
# Recommended: Alibaba Qwen-plus model via Bailian Platform: https://bailian.console.aliyun.com/
# High consumption, try simulations with fewer than 40 rounds first
LLM_API_KEY=your_api_key
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
LLM_MODEL_NAME=qwen-plus
# Zep Cloud Configuration
# Free monthly quota is sufficient for simple usage: https://app.getzep.com/
ZEP_API_KEY=your_zep_api_key
2. Install Dependencies
# One-click installation of all dependencies (root + frontend + backend)
npm run setup:all
Or install step by step:
# Install Node dependencies (root + frontend)
npm run setup
# Install Python dependencies (backend, auto-creates virtual environment)
npm run setup:backend
3. Start Services
# Start both frontend and backend (run from project root)
npm run dev
Service URLs:
- Frontend:
http://localhost:3000 - Backend API:
http://localhost:5001
Start Individually:
npm run backend # Start backend only
npm run frontend # Start frontend only
Option 2: Docker Deployment
# 1. Configure environment variables (same as source deployment)
cp .env.example .env
# 2. Pull image and start
docker compose up -d
Reads .env from root directory by default, maps ports 3000 (frontend) / 5001 (backend)
Mirror address for faster pulling is provided as comments in
docker-compose.yml, replace if needed.
📬 Join the Conversation
The Foresight team is recruiting full-time/internship positions. If you're interested in multi-agent simulation and LLM applications, feel free to send your resume to: foresight@shanda.com
📄 Acknowledgments
Foresight has received strategic support and incubation from Shanda Group!
Foresight's simulation engine is powered by OASIS (Open Agent Social Interaction Simulations), We sincerely thank the CAMEL-AI team for their open-source contributions!