- Add script to generate Markdown documentation of all Netmiko supported
devices (SSH and Telnet), grouped by platform with source attribution
- Highlight custom GNS3-Copilot devices with "Custom ✨" marker
- Auto-generate device list to docs/gns3-copilot/netmiko_devices.md
- Simplify HuaweiTelnetCE driver: remove redundant aliases
(huawei_ce, huawei_telnet_ce_telnet), keep only huawei_telnet_ce
- Update tests to match simplified device registration
Add comprehensive multi-vendor support for GNS3 network automation,
including a custom Netmiko driver for Huawei CloudEngine devices.
Features:
- Custom HuaweiTelnetCE driver for GNS3 emulation (no authentication)
- Auto-commit before exit to prevent [Y/N/C] prompts
- Dynamic device type detection from GNS3 node tags
- Support for both Cisco IOS and Huawei devices
- Proper VRP command handling (system-view, return confirmation)
Implementation:
- New package: utils/custom_netmiko/
- huawei_ce.py: Huawei CloudEngine driver
- tests/test_huawei_ce.py: Unit tests (9/9 passing)
- README.md: Driver development guide
- Updated tools for multi-vendor support:
- display_tools_nornir.py: Dynamic group generation
- config_tools_nornir.py: Multi-vendor config commands
- get_gns3_device_port.py: Device port extraction
- Documentation: multi-vendor-device-support.md
Limitations:
- huawei_telnet_ce driver requires devices without authentication
- For devices with username/password, use standard huawei_telnet driver
Co-Authored-By: Yue Guobin <yueguobin@outlook.com>"
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.
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.
- 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.
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>
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 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.
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.
Updated references to the title generation node in both documentation and code:
- Changed node name from `generate_title` to `title_generator_node` in API design documentation
- Updated filtering logic in agent service to exclude `title_generator_node` from LLM call statistics, token counting, and frontend streaming events
- Maintains same functionality while using more descriptive node name for clarity
- Move command-security.md and hitl-implementation-plan.md to todo/ directory
- Add new documentation for orphan tool calls recovery with detailed problem analysis and implementation plan
- Document scenarios causing orphan tool calls and propose fix strategy using LangGraph API
Filter out internal LangGraph 'generate_title' node from LLM call counting and token usage tracking to avoid inflating statistics with internal operations. This ensures metrics only reflect user-facing AI interactions.
- Update API documentation to reflect new streaming tool call mechanism
- Add `message_id` optional field to content and tool_call events
- Change tool_call structure from array to single object with incremental updates
- Add `tool_call_id` to tool_start events for better event correlation
- Implement ToolCallStreamAccumulator class to handle parameter accumulation
- Provide frontend example code for handling streaming tool calls
- Maintain backward compatibility with existing session_id tracking
- Update AI chat API documentation with revised SSE event schema
- Add support for multiple tool calls in `tool_call` events
- Include `session_id` in all event types for better session tracking
- Implement `on_chat_model_end` handler to process LLM tool call decisions
- Update example JSON payloads to reflect new schema structure
- Rename "teaching" mode to "teaching_assistant" for better clarity
- Rename "lab_assistant" mode to "lab_automation_assistant" to reflect expanded capabilities
- Implement mode-specific tool sets: teaching_assistant gets read-only diagnostic tools only, while lab_automation_assistant gets full diagnostic and configuration tools
- Update API documentation examples to reflect new mode names
- Maintain backward compatibility with default tool set initialization
- Added `temperature` parameter to Chat API documentation with implementation notes
- Improved code formatting in context_manager.py with consistent string quotes and line breaks
- Added section on future runtime LLM parameter override capabilities
- Updated API schemas to include temperature parameter (currently unused but reserved for future implementation)
- Add `copilot_mode` field to request examples in API documentation
- Update prompt loader to read `copilot_mode` from flattened config structure
- Support both "teaching" and "lab_assistant" modes for different assistant behaviors
- Add `copilot_mode` field to LLM model configs API with "teaching" (diagnostics only) and "lab_assistant" (full configuration access) modes
- Introduce new `ExecuteMultipleDeviceConfigCommands` tool for executing configuration commands on multiple devices
- Include `tags` field in node data structure for enhanced project management
- Update API documentation examples to reflect new `copilot_mode` field and context limit additions
Removed extensive implementation details and configuration examples from the context window management documentation. The document now focuses on core permissions and prohibitions for tool usage, providing a clearer and more concise reference for allowed and forbidden actions. This streamlines the documentation to essential guidelines only.
- Refactor system message structure to combine system prompt and topology info using template variables
- Update token calculation process with merged system message approach
- Clarify priority order for message retention during context window management
- Add detailed token counting implementation using tiktoken library
- Include boundary case handling for system message exceeding budget
- Add SPDX license headers to tool files for proper licensing documentation
Enhanced the context window management system to properly account for tool definition tokens when trimming messages. The key changes include:
- Updated `trim_messages_for_context` function to accept `tool_tokens` parameter
- Modified token budget allocation logic to subtract tool tokens before message trimming
- Added detailed documentation explaining the token budget distribution between messages and tool definitions
- Implemented prioritized trimming strategy that preserves system messages and recent conversation history
- Added boundary case handling for scenarios where system messages or tools exceed available budget
The improvements ensure more accurate context window management by accounting for the ~1000-2000 tokens typically consumed by tool definitions that LangChain automatically includes in LLM requests.
- Add tiktoken as a required dependency for accurate token counting
- Update documentation with installation instructions and token counting strategy
- Improve logging to include tool definition token estimates
- Enhance error handling to fail fast when tiktoken is not available
- Update context manager to use tiktoken's cl100k_base encoding for GPT-4 compatibility
- Add `context_limit` as required field for LLM model configurations
- Add `context_strategy` as optional field with three trimming strategies
- Update API documentation with detailed examples for GPT-4o and Claude 3.5 Sonnet
- Clarify that context limit is specified in K tokens (thousands of tokens)
- Update example payloads to reflect current model versions and new fields
- Move AI chat API design document to gns3-copilot directory
- Add comprehensive HITL implementation plan with architecture and workflow
- Introduce human-in-the-loop confirmation for dangerous configuration commands
- Include state management, API endpoints, and frontend integration details
- Provide testing plan and deployment steps for the new feature
Add `normalize_tool_response` function to standardize tool output formats for consistent frontend display. The function converts various response types (dict, list, string) into a unified structure with success metrics, detailed data arrays, and metadata. This ensures backward compatibility while providing predictable response formats for UI components.
- Add `pinned` column to chat_sessions table with default FALSE
- Implement database migration for existing installations using PRAGMA table_info
- Create composite index for pinned + updated_at sorting
- Add pin/unpin API endpoints (PUT/DELETE /sessions/{id}/pin)
- Update session listing to sort by pinned status then updated_at
- Extend ChatSessionsRepository with pin_session method
- Update API documentation to reflect new pinning functionality
The feature allows users to pin important chat sessions to the top of the list. Sessions are sorted with pinned sessions first (by updated_at), followed by regular sessions (by updated_at). Database migration ensures backward compatibility with existing installations.
- Add message ID generation for initial HumanMessage creation
- Implement message converters for LangChain/OpenAI format interoperability
- Update documentation with detailed message format specifications
- Refactor AgentService to use centralized message conversion utilities
- Ensure tool_calls format compliance with OpenAI API standards
Update the AI chat API design documentation to provide comprehensive details about statistics collection during streaming conversations. The documentation now includes:
1. **Enhanced statistics collection logic**:
- Clarified message_count increments for user messages, AI responses, and tool results
- Added LLM call tracking via on_chat_model_start events
- Detailed token counting methodology using LangGraph's usage_metadata
2. **Improved implementation details**:
- Added specific event handlers for statistics collection
- Explained LangGraph's cumulative token counting behavior
- Provided real-world examples of token accumulation
3. **Updated data models**:
- Enhanced ChatSession model documentation with field descriptions
- Separated fields into categories (basic, statistics, timestamps, reserved)
4. **Refined architecture documentation**:
- Added detailed flow for stream_chat method
- Documented statistics collection mechanism during SSE streaming
- Explained batch update strategy to reduce database writes
The changes ensure developers understand how conversation statistics are collected, processed, and stored without impacting streaming performance.
This commit introduces a comprehensive design document outlining the implementation of AI Chat API for GNS3 Copilot Agent within GNS3 Server. The document provides:
- Overview and background on existing components including GNS3 Copilot Agent, LLM configuration management, and API framework
- Reference implementation details from FlowNet-Lab project
- Architecture design with clear component interactions between frontend clients and backend services
- RESTful API specifications for chat streaming, session management, and history retrieval
- Implementation details covering project-based agent management, SQLite checkpoint storage, and LangGraph integration
- Security considerations and deployment guidelines
The design enables clients to interact with GNS3 Copilot Agent through standardized APIs, supporting real-time chat streaming and persistent conversation sessions per project.
Updated documentation and implementation to clearly define the priority order for selecting default LLM model configurations. The logic now explicitly states:
1. User's config marked with `is_default: true` (highest priority)
2. Group's config marked with `is_default: true`
3. First config in the list (user configs come before group configs)
This ensures consistent behavior between the API documentation and the actual implementation in the repository code.
Update API documentation to reflect new nested `config` object structure in LLM model configurations endpoints. The response format now encapsulates provider-specific fields (provider, base_url, model, temperature, api_key, max_tokens) within a `config` object, while moving ownership metadata (user_id, group_id, is_default, version, created_at, updated_at) to the top level. This aligns the user-facing endpoints with the group configuration structure and improves API consistency.
