Simplify the documentation structure in `README.md` by removing the
`todo/` directory reference and detailed design documents for planned
features. Consolidate future roadmap items into a high-level summary
under "Future Enhancements".
Update `node-control-tools.md` to include documentation for new topology
management tools (create node, create link, get template, rename node)
and reflect updated API imports for `Link` support.
- Upgrade langchain packages to latest versions (langchain 1.2.10, langgraph 1.0.9, etc.)
- Add langsmith SDK for enhanced observability and tracing
- Update Pillow to 12.1.1 for image processing improvements
- Maintain compatibility with existing AI and automation framework
Add comprehensive documentation proposing TOSCA (Topology and Orchestration Specification for Cloud Applications) as the standard format for GNS3 network topologies. The document outlines strategic benefits including standardized YAML descriptions, toolchain ecosystem integration, Git-based workflows, and template reuse capabilities. This initiative aims to modernize GNS3 topology management, improve user experience, and align with industry best practices for network automation and orchestration.
- Add GNS3StopNodeTool and GNS3SuspendNodeTool to lab automation assistant mode
- Update tools_v2 __init__.py to export new node control tools
- Document node control tools in README with key features and implementation status
- Update last modified date in documentation
The new tools provide complete node lifecycle control for automated lab workflows, including stopping nodes for shutdown and suspending nodes while preserving state.
- Remove redundant "(REQUIRED)" and detailed explanations from context_limit field descriptions
- Shorten copilot_mode descriptions by removing parenthetical details about mode capabilities
- Maintain field formatting consistency across LLMModelConfigData, Create, and Update schemas
- Add README.md with documentation overview and structure guide
- Move implemented designs to docs/gns3-copilot/implemented/:
- chat-api.md (from ai-chat-api-design.md)
- llm-model-configs.md (from llm-model-configs-api.md)
- command-security.md
- context-window-management.md
- Add Jinja2 configuration template system design documents:
- jinja2-config-templates-system.md
- config-templates-implementation-guide.md
- ai-prompting-for-config-templates.md
- Remove obsolete documents (acl-web-ui, Chinese RBAC doc)
This reorganization makes it clearer which features are implemented
vs planned, following the established documentation structure.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add comprehensive documentation for AI prompting to generate structured configuration data for Jinja2 templates. The guide includes:
- Core system prompt with critical rules for structured data generation
- Supported vendors and OS types (Cisco, Juniper, Huawei, Arista, Mikrotik)
- Detailed schemas for network features including OSPF, BGP, VLANs, ACLs, and more
- Example prompts and expected structured outputs
- Integration with the configuration renderer system
This documentation ensures AI-generated configurations follow the required structured format for proper template rendering, preventing direct configuration text generation and maintaining consistency across different network device vendors.
- Add exception for .claude/development.md to allow tracking development documentation
- Comment out PROJECT_CONTEXT.md exclusion to enable version control of project context file
- Create PROJECT_CONTEXT.md with comprehensive project overview for AI assistant support
- Document project structure, AI copilot data flow, SSE event types, and code standards
- Provide flake8 static analysis guidelines and common error fixes
Added new section in ai-chat-api-design.md explaining:
- LangGraph Agent architecture and main components
- tool_node function and tool output serialization mechanism
- Why serialization happens in tool_node (not agent_service)
- Tool output data flow diagram showing SSE and history paths
- Explanation of JSON format consistency across streaming and storage
This documentation clarifies the fix for single-quotes issue in
conversation history and helps future maintenance.
Co-Authored-By: YueGuobin <yueguobin@outlook.com>
Fixed tool output serialization in tool_node function to ensure
ToolMessage.content is always in JSON format, not Python str() representation.
This fixes the issue where conversation history showed tool outputs
with single quotes (Python format) instead of standard JSON.
Changes:
- Added json import to gns3_copilot.py
- Modified tool_node() to serialize observation to JSON before creating ToolMessage
- Ensures both SSE streaming and history storage use consistent JSON format
Root cause: ToolMessage was created with raw dict/list objects, which
LangChain converted to Python str() representation when saving to history.
Co-Authored-By: YueGuobin <yueguobin@outlook.com>
Add PROJECT_CONTEXT.md to the .gitignore file to prevent it from being tracked in version control. This file likely contains project-specific context or configuration that should not be committed to the repository.
Changed tool output serialization in AgentService._convert_event_to_chunk()
from str() to json.dumps() to ensure structured data (dict/list) is properly
formatted as standard JSON instead of Python string representation.
Changes:
- Added json import to agent_service.py
- Modified on_tool_end event handling to use json.dumps(output, ensure_ascii=False, indent=2)
- Updated ai-chat-api-design.md to document tool_output format
Benefits:
- Frontend can parse tool results with standard JSON.parse()
- Chinese and non-ASCII characters are preserved (not escaped)
- Formatted output (indent=2) improves readability
Co-Authored-By: YueGuobin <yueguobin@outlook.com>
Updated the author name and copyright statements across the
gns3_copilot module. The name has been standardized from
"Guobin Yue" to "Yue Guobin (岳国宾)" to reflect the correct
author attribution including Chinese characters.
- Improve POST /chat endpoint documentation with request/response examples
- Add session ID management flow explanation
- Enhance GET /sessions endpoint with query parameters and response example
- Update GET /sessions/{session_id}/history with detailed response structure
- Format parameters as tables for better readability
- Clarify session ID usage in streaming conversations
Add .claude/ directory to .gitignore to prevent accidental
commit of Claude Code settings which may contain sensitive
API keys. This ensures development environment configuration
files with potential credentials are excluded from version
control.
Add comprehensive documentation for handling datetime timezone discrepancies between backend and frontend. The issue arises when backend returns naive datetime strings without timezone suffixes, causing JavaScript to parse them as local time instead of UTC. The guide includes problem description, root cause analysis, three frontend solutions (dayjs UTC parsing, manual 'Z' suffix addition, and global Axios interceptor), backend context, and testing checklist.
Add comprehensive implementation guide for ACL management feature in GNS3 Web UI. The guide includes:
- Feature overview with core functionality and user flow
- Detailed API endpoint specifications for ACE CRUD operations
- Data structures and relationships
- Frontend implementation guide with component architecture
- UI/UX design recommendations and wireframes
- Common usage scenarios and error handling patterns
- Example code with React components and API service layer
This document serves as a reference for frontend developers implementing ACL management interface, covering everything from API integration to user interface design.
Add comprehensive documentation for the two-tier permission control system combining RBAC and ACL features. The guide covers system overview, core concepts, data model, permission check flow, usage examples, best practices, and common issues. This documentation is applicable for GNS3 Server v3.0+ and provides implementation details for administrators managing user permissions.
Add detailed section explaining that plaintext API keys for group configurations are never exposed through the application API, even to super admins. This clarifies the security design where group configs are intended for inheritance only, not manual viewing, while maintaining internal decryption for inheritance functionality.
Update the LLM model configs API documentation to provide clearer explanations of API key visibility controls and encryption behavior. The changes include:
- Enhanced visibility table with more specific scenarios and encryption states
- Added detailed rules explaining when API keys are visible as plaintext, hidden (null), or shown encrypted
- Clarified super admin capabilities and database-level access
- Updated JSON examples to reflect actual encrypted values and null placeholders
- Added important notes about Fernet encryption, on-the-fly decryption, and group config behavior
These updates provide better transparency about security measures and help users understand what to expect when viewing different types of configurations.
Add comprehensive user context to the `/me` endpoint by including group memberships, accessible resource pools, and access control entries (ACEs). This enables users to view inherited configurations, available resources, and their permissions directly from the API.
Key additions:
- Group membership details with inherited configs
- Resource pool access information
- ACE visibility for permission transparency
- Support for user-selectable group default configurations
The enhancement addresses user needs for better visibility into permissions and accessible resources within the system.
Changed the example value of `copilot_mode` from "lab_assistant" to "lab_automation_assistant" in the API documentation to reflect the correct mode name used in the system.
Rename base_prompt.py to teaching_assistant_prompt.py and lab_assistant_prompt.py to lab_automation_assistant_prompt.py to better reflect their purposes. Update all imports and references accordingly to maintain consistency across the codebase. This improves code readability and aligns naming with the actual functionality of each prompt module.
The `python scripts/show_model_context_limits.py` command was removed from the documentation as it is no longer necessary or relevant for users configuring model context limits. The remaining content still provides clear guidance on calculating context limits in K tokens.
Translate the GNS3 Copilot Agent Chat API design document from Chinese to English to improve accessibility for international contributors and align with project documentation standards. The translation covers all sections including overview, core features, architecture design, API endpoints, and response formats.
Add comprehensive design document outlining the race condition issue when multiple users simultaneously operate on the same network device via GNS3-Copilot Agent. The document details the problem statement, affected components, and proposes two solutions: device-level mutex lock (recommended) and connection pooling with session isolation. It includes implementation details, API changes, and UI considerations to ensure safe concurrent operations.
Add support for runtime control parameters `max_iterations` and `max_tool_calls` in the chat API to allow users to dynamically adjust agent behavior per request. This addresses current limitations where iteration limits and tool call constraints are hardcoded, providing flexibility for complex tasks and cost control.
- Import filter_forbidden_commands utility from command_filter module
- Add _filter_forbidden_commands_from_device_configs method to filter out restricted commands before execution
- Store blocked commands information and log filtered commands for audit purposes
- Update _process_task_results to include blocked commands info in response
- Prevent execution of potentially dangerous commands while maintaining transparency about filtered content
Add comprehensive documentation for troubleshooting issues caused by using `kill -9` on gns3server processes. The guide explains the root cause where SIGKILL prevents proper cleanup of child processes like dynamips and vpcs, leading to port conflicts and resource allocation errors when restarting. It provides multiple solutions including manual process cleanup, proper shutdown procedures, and preventive measures with example scripts. This documentation helps users resolve common issues with Dynamips VM creation failures, undefined project_id errors, and TCP port warnings.
