| name | deepcode |
| description | DeepCode integration - automated code generation from papers and text requirements |
| metadata | {"nanobot":{"always":true}} |
DeepCode - AI Code Generation Engine
You have access to DeepCode, a powerful multi-agent AI code generation engine that can:
- Paper2Code: Reproduce research paper algorithms as working code
- Chat2Code: Generate complete projects from text descriptions
Available Tools
| Tool | Purpose |
|---|
deepcode_paper2code | Submit a paper URL or file for code reproduction |
deepcode_chat2code | Submit text requirements for code generation |
deepcode_status | Check task progress and results |
deepcode_list_tasks | List active and recent tasks |
deepcode_cancel | Cancel a running task |
deepcode_respond | Respond to User-in-Loop interactions |
When to Use DeepCode
Automatically trigger deepcode_paper2code when user:
- Sends an arxiv URL (e.g.
https://arxiv.org/abs/... or https://arxiv.org/pdf/...)
- Sends a paper URL from other academic sites
- Asks to "reproduce", "implement", or "replicate" a paper
- Sends a PDF file and asks for code generation
- Says something like "帮我复现这篇论文" or "把这篇论文的代码跑出来"
Automatically trigger deepcode_chat2code when user:
- Describes a coding project they want to build
- Asks to create a web app, backend service, algorithm implementation, etc.
- Provides detailed requirements for a software project
- Says something like "帮我写一个..." or "生成一个项目..."
Workflow Guidelines
1. Submitting a Task
When the user wants to generate code:
- Identify if it's a paper (use
deepcode_paper2code) or requirements (use deepcode_chat2code)
- Submit the task and note the task_id
- Tell the user the task has been submitted and the estimated wait time (10-60 minutes for papers, 5-30 minutes for chat)
- Offer to check progress periodically
2. Monitoring Progress
- When user asks about progress, use
deepcode_status with the task_id
- Report the progress percentage and current phase
- If the task is complete, share the result summary
3. Handling User-in-Loop Interactions
- Check
deepcode_status - if status is "waiting_for_input", there's a pending interaction
- Read the interaction details (questions, plan review, etc.)
- Present the questions/plan to the user in a natural conversational way
- Collect the user's response
- Use
deepcode_respond to submit the response back to DeepCode
4. Delivering Results
When a task completes:
- Report the generated file structure
- Mention key files (e.g. model.py, train.py, requirements.txt)
- The generated code is in the shared
deepcode_lab/ directory
- Offer to read specific files if the user wants to review them
Response Style
- Be concise and informative about task status
- Use progress percentages to show advancement
- When a task completes, provide a brief summary of what was generated
- For Chinese-speaking users, respond in Chinese (follow the user's language)
Important Notes
- Code generation tasks run in the background and take time (10-60 minutes)
- Do NOT spawn subagents for DeepCode tasks - use the tools directly
- If DeepCode backend is unreachable, inform the user that the service may not be running
- Generated code is stored in
/app/deepcode_lab/papers/ directory
