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DeepCode: Open Agentic Coding

Advancing Code Generation with Multi-Agent Systems

🖥️ Interface Showcase

🖥️ CLI Interface Terminal-Based Development 🚀 Advanced Terminal Experience ⚡ Fast command-line workflow 🔧 Developer-friendly interface 📊 Real-time progress tracking Professional terminal interface for advanced users and CI/CD integration

🌐 Web Interface Visual Interactive Experience 🎨 Modern Web Dashboard 🖱️ Intuitive drag-and-drop 📱 Responsive design 🎯 Visual progress tracking Beautiful web interface with streamlined workflow for all skill levels

---

🎬 Introduction Video

🎯 Watch our complete introduction - See how DeepCode transforms research papers and natural language into production-ready code

---

"Where AI Agents Transform Ideas into Production-Ready Code"

📑 Table of Contents

• 📰 News
• 🚀 Key Features
• 🏗️ Architecture
• 📊 Experimental Results
• 🚀 Quick Start
• 🤖 nanobot Integration (Feishu Chatbot)
• 💡 Examples
  • 🎬 Live Demonstrations
• ⭐ Star History
• 📄 License

📰 News

🧭 [2026-05-01] OpenRouter model selector, session cleanup & workflow UX hardening

• 🧠 OpenRouter model catalog in Settings. The new UI can now fetch OpenRouter model metadata from https://openrouter.ai/api/v1/models, cache it locally, and expose searchable model selectors for the Default, Planning, and Implementation phases. Use exact OpenRouter model ids such as z-ai/glm-5.1 without editing JSON by hand.
• 🔄 Runtime model switching. Saving model choices from Settings updates deepcode_config.json and reloads the in-process LLM runtime so newly started workflows pick up the selected provider/model combination immediately.
• 🗑️ Session deletion now performs safe cascade cleanup. Deleting a session from the UI removes its persistent session store and associated deepcode_lab/tasks/<task_id>/ workspaces, while preserving shared uploads/ source files. Sessions with pending, running, or waiting_for_input tasks are blocked with a clear 409 Conflict.
• 📊 More accurate Paper2Code progress. The frontend now shows backend stage messages and avoids marking intermediate phases as fully "Done" while long LLM work is still running.
• 🛡️ Workflow robustness fixes. Uploads now reject Git LFS pointer files, cancelled tasks stop backend work promptly, stale browser session ids recover cleanly, planner retries fall back to a minimal valid plan when a model defers/tool-calls incorrectly, and document segmentation skips an extra validation LLM call that could stall progress.

🗂️ [2026-04-28] Persistent sessions & dual-layer logging

• 🆕 Sessions are now persistent. Every CLI / UI run is automatically attached to a session under ~/.deepcode/sessions/<id>/ (override with DEEPCODE_SESSIONS_DIR). Sessions are JSONL — tail -f session.jsonl works out of the box. List / inspect / branch them with python cli/main_cli.py session list|show <id>|new|resume <id>|delete <id>, or via GET /api/v1/sessions from the backend.
• 🔄 Resume a previous run by passing --session <id> to the CLI or session_id to POST /api/v1/workflows/paper-to-code (or chat-planning). Backend restarts no longer drop task history; running tasks left over from a crash are surfaced as interrupted.
• 💻 CLI session UX. The interactive CLI now supports Cursor-style slash commands: /resume opens a numbered session picker, /new [title] creates and switches sessions, /session shows the active session, and /help lists commands. You can also paste inline inputs directly at the menu prompt with @/path/to/paper.pdf, @"C:\path with spaces\paper.pdf", or @https://....
• 📜 Two-layer structured logging. A global rotating JSONL lives at logs/server-YYYYMMDD.jsonl; per-task logs at deepcode_lab/tasks/<task_id>/logs/{system,llm,mcp}.jsonl. Every loguru.logger call automatically picks up the active task_id via a contextvar — business code did not have to change. Configure via the new logger.{globalFile,taskFile,llm} block in deepcode_config.json.
• 📡 WebSocket log streaming. Tail one task with /ws/tasks/{task_id}/logs?channel=llm, or merge every task in a session via /ws/sessions/{session_id}/logs. The legacy /ws/logs/{session_id} endpoint that silently ignored its parameter has been removed.
• 🧹 Dead code removed. utils/simple_llm_logger.py, utils/dialogue_logger.py, and the in-memory services/session_service.py implementation are gone (the latter is now a thin re-export of core.sessions.SessionStore).

🛠️ [2026-04-17] Stability, Windows compatibility & secrets hygiene update

• 🐛 Code Implementation no longer crashes with name 'LoopDetector' is not defined — added the missing LoopDetector/ProgressTracker imports in both workflows/code_implementation_workflow.py and workflows/code_implementation_workflow_index.py.
• 🪟 Windows: mkdir -p / touch / rm -rf / cp -r / mv now work natively. tools/command_executor.py translates these common Unix file-tree commands via pathlib/shutil on every platform, eliminating the bug where cmd.exe would create a literal -p directory and stall the workflow.
• 🚀 Removed Brave Search end-to-end. All Python code, MCP server config, Dockerfile pre-installs, nanobot integration and docs are scrubbed of brave/BRAVE_API_KEY/WebSearchTool. Web fetching now relies entirely on the built-in fetch MCP server.
• 🔌 OpenAI-compatible providers documented. New Quick Start → Configuration snippet shows how to point the openai/openrouter blocks at Poe (https://api.poe.com/v1), OpenRouter, or Alibaba DashScope, plus how to set agents.defaults.model / agents.planning.model / agents.implementation.model (e.g. openai/gpt-5.4).
• 🔐 Secrets hygiene. All YAML config has been collapsed into a single deepcode_config.json (nanobot-style), and .gitignore now ignores it alongside secrets.json, *credentials*.json, .env, .env.* (with *.env.example whitelisted).
• 📝 Launch table fixed. deepcode (no flags) actually starts Docker mode — the README now shows deepcode --local for the no-Docker path and adds explicit Troubleshooting rows for "Docker is installed but not running", Windows GBK encoding, and the issues fixed above.
• 🧹 Misc: auto-create logs/ directory so JSONL logging never fails on a fresh checkout, replace bare except: with except Exception: in agent_orchestration_engine.py (Ruff E722), command_executor MCP tool descriptions now embed the host OS so the LLM picks compatible commands.

🎉 [2026-02] nanobot ✖️ DeepCode. Just chat naturally with openclaw/nanobot to handle your coding tasks:

✦

• nanobot nanobot now powers your agentic coding & engineering! 🤖💻
• Step away from your laptop — make vibe coding even more vibe! Code directly from your phone! 📱✨
• One-command deploy: ./nanobot/run_nanobot.sh → Setup Guide →

_{Feishu Bot in Action — Natural language → Full code generation with setup instructions}

🎉 [2026-02] New Web UI Experience Upgrade!

• 🔄 User-in-Loop Interaction: Support real-time user interaction during workflows - AI asks clarifying questions directly in the chat
• 💬 Inline Interaction Design: Interaction prompts appear naturally within the chat flow for a seamless experience
• 🚀 One-Click Launch: Simply run deepcode to start the new UI (cross-platform: Windows/macOS/Linux)
• 🔧 Improved Process Management: Enhanced service start/stop mechanism with automatic port cleanup
• 📡 WebSocket Real-time Communication: Fixed message loss issues, ensuring proper interaction state synchronization

_{DeepCode New Web UI - Modern React-based Interface}

🎉 [2025-10-28] DeepCode Achieves SOTA on PaperBench!

DeepCode sets new benchmarks on OpenAI's PaperBench Code-Dev across all categories:

• 🏆 Surpasses Human Experts: 75.9% (DeepCode) vs Top Machine Learning PhDs 72.4% (+3.5%).
• 🥇 Outperforms SOTA Commercial Code Agents: 84.8% (DeepCode) vs Leading Commercial Code Agents (+26.1%) (Cursor, Claude Code, and Codex).
• 🔬 Advances Scientific Coding: 73.5% (DeepCode) vs PaperCoder 51.1% (+22.4%).
• 🚀 Beats LLM Agents: 73.5% (DeepCode) vs best LLM frameworks 43.3% (+30.2%).

🚀 Key Features

🚀 Paper2Code Automated Implementation of Complex Algorithms Effortlessly converts complex algorithms from research papers into high-quality, production-ready code, accelerating algorithm reproduction.

🎨 Text2Web Automated Front-End Web Development Translates plain textual descriptions into fully functional, visually appealing front-end web code for rapid interface creation.

⚙️ Text2Backend Automated Back-End Development Generates efficient, scalable, and feature-rich back-end code from simple text inputs, streamlining server-side development.

📊 Experimental Results

We evaluate DeepCode on the PaperBench benchmark (released by OpenAI), a rigorous testbed requiring AI agents to independently reproduce 20 ICML 2024 papers from scratch. The benchmark comprises 8,316 gradable components assessed using SimpleJudge with hierarchical weighting.

Our experiments compare DeepCode against four baseline categories: (1) Human Experts, (2) State-of-the-Art Commercial Code Agents, (3) Scientific Code Agents, and (4) LLM-Based Agents.

① 🧠 Human Expert Performance (Top Machine Learning PhD)

DeepCode: 75.9% vs. Top Machine Learning PhD: 72.4% (+3.5%)

DeepCode achieves 75.9% on the 3-paper human evaluation subset, surpassing the best-of-3 human expert baseline (72.4%) by +3.5 percentage points. This demonstrates that our framework not only matches but exceeds expert-level code reproduction capabilities, representing a significant milestone in autonomous scientific software engineering.

② 💼 State-of-the-Art Commercial Code Agents

DeepCode: 84.8% vs. Best Commercial Agent: 58.7% (+26.1%)

On the 5-paper subset, DeepCode substantially outperforms leading commercial coding tools:

• Cursor: 58.4%
• Claude Code: 58.7%
• Codex: 40.0%
• DeepCode: 84.8%

This represents a +26.1% improvement over the leading commercial code agent. All commercial agents utilize Claude Sonnet 4.5 or GPT-5 Codex-high, highlighting that DeepCode's superior architecture—rather than base model capability—drives this performance gap.

③ 🔬 Scientific Code Agents

DeepCode: 73.5% vs. PaperCoder: 51.1% (+22.4%)

Compared to PaperCoder (51.1%), the state-of-the-art scientific code reproduction framework, DeepCode achieves 73.5%, demonstrating a +22.4% relative improvement. This substantial margin validates our multi-module architecture combining planning, hierarchical task decomposition, code generation, and iterative debugging over simpler pipeline-based approaches.

④ 🤖 LLM-Based Agents

DeepCode: 73.5% vs. Best LLM Agent: 43.3% (+30.2%)

DeepCode significantly outperforms all tested LLM agents:

• Claude 3.5 Sonnet + IterativeAgent: 27.5%
• o1 + IterativeAgent (36 hours): 42.4%
• o1 BasicAgent: 43.3%
• DeepCode: 73.5%

The +30.2% improvement over the best-performing LLM agent demonstrates that sophisticated agent scaffolding, rather than extended inference time or larger models, is critical for complex code reproduction tasks.

🎯 Autonomous Self-Orchestrating Multi-Agent Architecture

The Challenges:

• 📄 Implementation Complexity: Converting academic papers and complex algorithms into working code requires significant technical effort and domain expertise

• 🔬 Research Bottleneck: Researchers spend valuable time implementing algorithms instead of focusing on their core research and discovery work

• ⏱️ Development Delays: Product teams experience long wait times between concept and testable prototypes, slowing down innovation cycles

• 🔄 Repetitive Coding: Developers repeatedly implement similar patterns and functionality instead of building on existing solutions

DeepCode addresses these workflow inefficiencies by providing reliable automation for common development tasks, streamlining your development workflow from concept to code.

flowchart LR
    A["📄 Research Papers<br/>💬 Text Prompts<br/>🌐 URLs & Document<br/>📎 Files: PDF, DOC, PPTX, TXT, HTML"] --> B["🧠 DeepCode<br/>Multi-Agent Engine"]
    B --> C["🚀 Algorithm Implementation <br/>🎨 Frontend Development <br/>⚙️ Backend Development"]

    style A fill:#ff6b6b,stroke:#c0392b,stroke-width:2px,color:#000
    style B fill:#00d4ff,stroke:#0984e3,stroke-width:3px,color:#000
    style C fill:#00b894,stroke:#00a085,stroke-width:2px,color:#000

🏗️ Architecture

📊 System Overview

DeepCode is an AI-powered development platform that automates code generation and implementation tasks. Our multi-agent system handles the complexity of translating requirements into functional, well-structured code, allowing you to focus on innovation rather than implementation details.

🎯 Technical Capabilities:

🧬 Research-to-Production Pipeline
Multi-modal document analysis engine that extracts algorithmic logic and mathematical models from academic papers. Generates optimized implementations with proper data structures while preserving computational complexity characteristics.

🪄 Natural Language Code Synthesis
Context-aware code generation using fine-tuned language models trained on curated code repositories. Maintains architectural consistency across modules while supporting multiple programming languages and frameworks.

⚡ Automated Prototyping Engine
Intelligent scaffolding system generating complete application structures including database schemas, API endpoints, and frontend components. Uses dependency analysis to ensure scalable architecture from initial generation.

💎 Quality Assurance Automation
Integrated static analysis with automated unit test generation and documentation synthesis. Employs AST analysis for code correctness and property-based testing for comprehensive coverage.

🔮 CodeRAG Integration System
Advanced retrieval-augmented generation combining semantic vector embeddings with graph-based dependency analysis. Automatically discovers optimal libraries and implementation patterns from large-scale code corpus.

🔧 Core Techniques

• 🧠 Intelligent Orchestration Agent: Central decision-making system that coordinates workflow phases and analyzes requirements. Employs dynamic planning algorithms to adapt execution strategies in real-time based on evolving project complexity. Dynamically selects optimal processing strategies for each implementation step.
  

• 💾 Efficient Memory Mechanism: Advanced context engineering system that manages large-scale code contexts efficiently. Implements hierarchical memory structures with intelligent compression for handling complex codebases. This component enables instant retrieval of implementation patterns and maintains semantic coherence across extended development sessions.
  

• 🔍 Advanced CodeRAG System: Global code comprehension engine that analyzes complex inter-dependencies across repositories. Performs cross-codebase relationship mapping to understand architectural patterns from a holistic perspective. This module leverages dependency graphs and semantic analysis to provide globally-aware code recommendations during implementation.

🤖 Multi-Agent Architecture of DeepCode:

• 🎯 Central Orchestrating Agent: Orchestrates entire workflow execution and makes strategic decisions. Coordinates specialized agents based on input complexity analysis. Implements dynamic task planning and resource allocation algorithms.
  

• 📝 Intent Understanding Agent: Performs deep semantic analysis of user requirements to decode complex intentions. Extracts functional specifications and technical constraints through advanced NLP processing. Transforms ambiguous human descriptions into precise, actionable development specifications with structured task decomposition.
  

• 📄 Document Parsing Agent: Processes complex technical documents and research papers with advanced parsing capabilities. Extracts algorithms and methodologies using document understanding models. Converts academic concepts into practical implementation specifications through intelligent content analysis.
  

• 🏗️ Code Planning Agent: Performs architectural design and technology stack optimization. Dynamic planning for adaptive development roadmaps. Enforces coding standards and generates modular structures through automated design pattern selection.
  

• 🔍 Code Reference Mining Agent: Discovers relevant repositories and frameworks through intelligent search algorithms. Analyzes codebases for compatibility and integration potential. Provides recommendations based on similarity metrics and automated dependency analysis.
  

• 📚 Code Indexing Agent: Builds comprehensive knowledge graphs of discovered codebases. Maintains semantic relationships between code components. Enables intelligent retrieval and cross-reference capabilities.
  

• 🧬 Code Generation Agent: Synthesizes gathered information into executable code implementations. Creates functional interfaces and integrates discovered components. Generates comprehensive test suites and documentation for reproducibility.

🛠️ Implementation Tools Matrix

🔧 Powered by MCP (Model Context Protocol)

DeepCode leverages the Model Context Protocol (MCP) standard to seamlessly integrate with various tools and services. This standardized approach ensures reliable communication between AI agents and external systems, enabling powerful automation capabilities.

📡 MCP Servers & Tools

🔧 Legacy Tool Functions (for reference)

🎛️ Multi-Interface Framework
RESTful API with CLI and web frontends featuring real-time code streaming, interactive debugging, and extensible plugin architecture for CI/CD integration.

🚀 Multi-Agent Intelligent Pipeline:

🚀 Quick Start

📋 Prerequisites

Before installing DeepCode, ensure you have the following:

# Check your versions
python --version   # Should be 3.9+
node --version     # Should be 18+
npm --version      # Should be 8+

# macOS (using Homebrew)
brew install node

# Ubuntu/Debian
curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
sudo apt-get install -y nodejs

# Windows
# Download from https://nodejs.org/

📦 Step 1: Installation

Choose one of the following installation methods:

⚡ Direct Installation (Recommended)

# 🚀 Install DeepCode package directly
pip install deepcode-hku

# 🔑 Download the unified configuration template
curl -O https://raw.githubusercontent.com/HKUDS/DeepCode/main/deepcode_config.json.example
cp deepcode_config.json.example deepcode_config.json

🔧 Development Installation (From Source)

git clone https://github.com/HKUDS/DeepCode.git
cd DeepCode/

curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv --python=3.13
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt

# Install frontend dependencies
npm install --prefix new_ui/frontend

git clone https://github.com/HKUDS/DeepCode.git
cd DeepCode/

pip install -r requirements.txt

# Install frontend dependencies
npm install --prefix new_ui/frontend

pip install -e .

🔧 Step 2: Configuration

The following configuration applies to all installation methods (pip, UV, source, and Docker). Everything lives in one file: deepcode_config.json (single source of truth, nanobot-style).

🔑 API Keys (required)

Edit deepcode_config.json and fill in at least one provider key. Inline strings work, and ${ENV_VAR} references are resolved at load time.

{
  "providers": {
    "openai":    { "apiKey": "your_openai_api_key" },
    "anthropic": { "apiKey": "${ANTHROPIC_API_KEY}" },
    "gemini":    { "apiKey": "" }
  }
}

{
  "agents": {
    "defaults": {
      "provider": "openrouter",
      "model": "z-ai/glm-5.1"
    },
    "planning":       { "provider": "openrouter", "model": "z-ai/glm-5.1" },
    "implementation": { "provider": "openrouter", "model": "z-ai/glm-5.1" }
  },
  "providers": {
    "openai":     { "apiKey": "your_openai_api_key" },
    "openrouter": { "apiKey": "your_openrouter_key", "apiBase": "https://openrouter.ai/api/v1" }
  }
}

🤖 LLM Provider (optional)

The provider is inferred from the model slug (openai/..., anthropic/..., gemini/..., etc.). To force a specific backend, set agents.defaults.provider:

{
  "agents": {
    "defaults": { "provider": "openai" }
  }
}

📄 Document Segmentation (optional)

{
  "documentSegmentation": {
    "enabled": true,
    "sizeThresholdChars": 50000
  }
}

# 1. Install MCP servers globally
npm i -g @modelcontextprotocol/server-filesystem

# 2. Find your global node_modules path
npm -g root

{
  "tools": {
    "mcpServers": {
      "filesystem": {
        "type": "stdio",
        "command": "node",
        "args": ["C:/Program Files/nodejs/node_modules/@modelcontextprotocol/server-filesystem/dist/index.js", "."]
      }
    }
  }
}

{
  "tools": { "defaultSearchServer": "filesystem" }
}

⚡ Step 3: Launch Application

Choose your preferred launch method:

git clone https://github.com/HKUDS/DeepCode.git
cd DeepCode/
cp deepcode_config.json.example \
   deepcode_config.json
# Edit deepcode_config.json with your API keys

./deepcode_docker/run_docker.sh
# Access → http://localhost:8000

deepcode --local
# Frontend → http://localhost:5173
# Backend  → http://localhost:8000
# Ctrl+C to stop

# macOS / Linux
./run.sh
# or: python deepcode.py --local

# Windows
run.bat
# or: python deepcode.py --local

# Classic Streamlit UI
deepcode --classic

# CLI mode
deepcode --cli
# or: python cli/main_cli.py

💻 CLI sessions & inline inputs

The CLI is session-aware by default. A run without --session creates a new persistent session under ~/.deepcode/sessions/<id>/; pass --session <id> to attach a new task to an existing session.

# Session management from the shell
python cli/main_cli.py session list
python cli/main_cli.py session show <session_id>
python cli/main_cli.py session resume <session_id>   # show history, then enter interactive mode
python cli/main_cli.py --session <session_id> --file paper.pdf

Inside python cli/main_cli.py, type these at the main menu prompt:

/resume                 # pick a previous session from a numbered list
/new My experiment      # create and switch to a fresh session
/session                # show the currently active session
@/absolute/path.pdf     # process a file without opening the file picker
@"C:\path with spaces\paper.pdf"
@https://arxiv.org/pdf/....

Every task created from these flows inherits the active session_id; per-task logs are written to deepcode_lab/tasks/<task>/logs/.

In the web UI, use the Sessions menu in the header to resume or delete a session. Deleting a session removes its JSONL session record and associated task workspace under deepcode_lab/tasks/, but keeps original files in uploads/. If the session still has pending, running, or waiting_for_input tasks, the backend rejects the deletion until the task is cancelled or completed.

./deepcode_docker/run_docker.sh stop      # Stop
./deepcode_docker/run_docker.sh restart   # Restart (no rebuild needed for config changes)
./deepcode_docker/run_docker.sh --build   # Force rebuild
./deepcode_docker/run_docker.sh logs      # Real-time logs
./deepcode_docker/run_docker.sh status    # Health check
./deepcode_docker/run_docker.sh clean     # Remove containers & images

docker compose -f deepcode_docker/docker-compose.yml up --build   # Build & start
docker compose -f deepcode_docker/docker-compose.yml down         # Stop
docker compose -f deepcode_docker/docker-compose.yml logs -f      # Logs

🎯 Step 4: Generate Code

1. 📄 Input — Upload a research paper, type requirements, or paste a URL
2. 🤖 Processing — The multi-agent system analyzes, plans, and generates
3. ⚡ Output — Receive production-ready code with tests and documentation

🔧 Troubleshooting

🤖 nanobot Integration (Feishu Chatbot)

Chat with DeepCode from Feishu — powered by nanobot.

flowchart LR
    subgraph Clients["💬 Chat Platforms"]
        direction TB
        F["<b>Feishu</b><br/>WebSocket"]
        T["<b>Telegram</b><br/>Polling"]
        D["<b>Discord</b><br/>Gateway"]
    end

    subgraph Gateway["🐈 nanobot Gateway"]
        direction TB
        A["Agent Loop<br/><i>LLM + Tool Calls</i>"]
    end

    subgraph Engine["🧠 DeepCode Engine"]
        direction TB
        P2C["Paper → Code"]
        C2C["Chat → Code"]
        TRK["Task Tracking"]
    end

    F & T & D <-->|"messages"| A
    A -->|"HTTP API"| P2C & C2C & TRK
    A -.->|"LLM API"| LLM["☁️ OpenRouter"]

    style Clients fill:#1a1a2e,stroke:#00d9ff,color:#fff
    style Gateway fill:#1a1a2e,stroke:#4ecdc4,color:#fff
    style Engine fill:#1a1a2e,stroke:#ff6b6b,color:#fff
    style LLM fill:#1a1a2e,stroke:#9b59b6,color:#fff

✦

Both services run inside the same Docker Compose network. Prerequisites: Docker Desktop + OpenRouter API Key (get one) + Feishu App.

Step 1 · Create a Feishu Bot

Step 2 · Configure

cp nanobot_config.json.example nanobot_config.json

Edit nanobot_config.json — fill in the 3 required fields:

{
  "channels": {
    "feishu": {
      "enabled": true,
      "appId": "cli_xxx",              // ← Feishu App ID
      "appSecret": "xxx",              // ← Feishu App Secret
      "allowFrom": []                  // [] = allow all users
    }
  },
  "providers": {
    "openrouter": {
      "apiKey": "sk-or-v1-xxx"         // ← OpenRouter API Key
    }
  },
  "agents": {
    "defaults": {
      "model": "anthropic/claude-sonnet-4-20250514"
    }
  }
}

Model choice: Any model on openrouter.ai/models. Use anthropic/claude-sonnet-4-20250514 for English, minimax/minimax-m2.1 for Chinese.

Step 3 · Launch

Make sure deepcode_config.json has your DeepCode API keys (see Configuration), then:

./nanobot/run_nanobot.sh -d          # Start both DeepCode + nanobot in background

The script checks Docker, validates configs, builds images (first run only), and starts both containers.

✓ DeepCode API:  http://localhost:8000
✓ Nanobot:       http://localhost:18790

Now open Feishu → find your bot → send a message!

./nanobot/run_nanobot.sh              # Start (foreground)
./nanobot/run_nanobot.sh -d           # Start (background)
./nanobot/run_nanobot.sh stop         # Stop all services
./nanobot/run_nanobot.sh restart      # Restart (config changes take effect immediately)
./nanobot/run_nanobot.sh --build      # Force rebuild Docker images
./nanobot/run_nanobot.sh logs         # View real-time logs
./nanobot/run_nanobot.sh status       # Health check
./nanobot/run_nanobot.sh clean        # Remove containers & images

💡 Examples

🎬 Live Demonstrations

📄 Paper2Code Demo Research to Implementation ▶️ Watch Demo Transform academic papers into production-ready code automatically

🖼️ Image Processing Demo AI-Powered Image Tools ▶️ Watch Demo Intelligent image processing with background removal and enhancement

🌐 Frontend Implementation Complete Web Application ▶️ Watch Demo Full-stack web development from concept to deployment

🆕 Recent Updates

📄 Smart Document Segmentation (v1.2.0)

• Intelligent Processing: Automatically handles large research papers and technical documents that exceed LLM token limits
• Configurable Control: Toggle segmentation via configuration with size-based thresholds
• Semantic Analysis: Advanced content understanding with algorithm, concept, and formula preservation
• Backward Compatibility: Seamlessly falls back to traditional processing for smaller documents

🚀 Coming Soon

We're continuously enhancing DeepCode with exciting new features:

🔧 Enhanced Code Reliability & Validation

• Automated Testing: Comprehensive functionality testing with execution verification and error detection.
• Code Quality Assurance: Multi-level validation through static analysis, dynamic testing, and performance benchmarking.
• Smart Debugging: AI-powered error detection with automatic correction suggestions

📊 PaperBench Performance Showcase

• Benchmark Dashboard: Comprehensive performance metrics on the PaperBench evaluation suite.
• Accuracy Metrics: Detailed comparison with state-of-the-art paper reproduction systems.
• Success Analytics: Statistical analysis across paper categories and complexity levels.

⚡ System-wide Optimizations

• Performance Boost: Multi-threaded processing and optimized agent coordination for faster generation.
• Enhanced Reasoning: Advanced reasoning capabilities with improved context understanding.
• Expanded Support: Extended compatibility with additional programming languages and frameworks.

⭐ Star History

Community Growth Trajectory

🚀 Ready to Transform Development?

---

📖 Citation

If you find DeepCode useful in your research or applications, please kindly cite:

hljs language-ini

Copy

@misc{li2025deepcodeopenagenticcoding,
      title={DeepCode: Open Agentic Coding},
      author={Zongwei Li and Zhonghang Li and Zirui Guo and Xubin Ren and Chao Huang},
      year={2025},
      eprint={2512.07921},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2512.07921},
}

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📄 License

MIT License - Copyright (c) 2025 Data Intelligence Lab, The University of Hong Kong

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