Optimize GNS3StartNodeTool with device-type-aware wait time calculation
to significantly reduce startup time for fast devices (VPCS, IOU).
Changes:
- Add NODE_STARTUP_TIME configuration
* VPCS: 15s base + 2s per additional node
* IOU: 25s base + 3s per additional node
* Other devices: 120s base + 10s per additional node (conservative)
- Add calculate_startup_time() function
* Detects device types via node.node_type
* Uses fast startup time if all nodes are VPCS/IOU
* Uses conservative time if any slow device present
* Logs selected strategy and detected types
- Optimize GNS3StartNodeTool._run() method
* Retrieve node info (including node_type) before starting
* Calculate wait time based on detected device types
* Send start commands after info retrieval
* Use calculated wait time for progress bar
* Reuse collected node objects for status retrieval
Performance improvements:
- 1 VPCS node: 140s → 15s (89% faster)
- 5 VPCS nodes: 180s → 23s (87% faster)
- 1 IOU node: 140s → 25s (82% faster)
- 5 IOU nodes: 180s → 37s (79% faster)
- Mixed VPCS/IOU: 180s → 33s (82% faster)
Documentation:
- Update node-control-tools.md with dynamic wait time strategy
- Add device type comparison table
- Document performance improvements
- Update changelog
Code quality:
- All comments in English
- flake8 check passed
- mypy check passed
Optimize GNS3StartNodeTool with device-type-aware wait time calculation
to significantly reduce startup time for fast devices (VPCS, IOU).
Changes:
- Add NODE_STARTUP_TIME configuration
* VPCS: 10s base + 2s per additional node
* IOU: 20s base + 3s per additional node
* Other devices: 120s base + 10s per additional node (conservative)
- Add calculate_startup_time() function
* Detects device types via node.node_type
* Uses fast startup time if all nodes are VPCS/IOU
* Uses conservative time if any slow device present
* Logs selected strategy and detected types
- Optimize GNS3StartNodeTool._run() method
* Retrieve node info (including node_type) before starting
* Calculate wait time based on detected device types
* Send start commands after info retrieval
* Use calculated wait time for progress bar
* Reuse collected node objects for status retrieval
Performance improvements:
- 1 VPCS node: 140s → 10s (93% faster)
- 5 VPCS nodes: 180s → 18s (90% faster)
- 1 IOU node: 140s → 20s (86% faster)
- 5 IOU nodes: 180s → 32s (82% faster)
- Mixed VPCS/IOU: 180s → 28s (84% faster)
Documentation:
- Update node-control-tools.md with dynamic wait time strategy
- Add device type comparison table
- Document performance improvements
- Update changelog
Code quality:
- All comments in English
- flake8 check passed
- mypy check passed
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.
- 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 copyright notice and author attribution to multiple Python files in the gns3-copilot module. This ensures proper licensing attribution and clarifies authorship for the project files.
Removed repetitive "This module is part of the GNS3-Copilot project" and GitHub URL comments from multiple module docstrings. These comments were redundant since the project information is already established in the main package documentation. This cleanup improves code readability and reduces maintenance overhead by eliminating duplicate attribution statements across the codebase.
- 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.
Updated import statements across multiple agent files to use absolute paths starting with `gns3server.agent.gns3_copilot` instead of relative `gns3_copilot` imports. This ensures proper module resolution within the gns3-server package structure and prevents import errors when the agent is executed from different contexts.
Integrate the gns3-copilot AI assistant module to provide intelligent
automation and interaction capabilities for GNS3 network emulation.
Key components:
- AI agent framework with LLM integration (supports Qwen vision model)
- GNS3 client library for project topology management
- Extensive prompt templates for various network operation scenarios
- Tool library for node creation, linking, configuration, and management
- Support for English level assessment (A1-C2) and specialized personas
- Network drawing and topology visualization tools
- Linux device automation via Nornir/Telnetlib
- Window controller for UI interaction
Features:
- Multi-modal AI agent with vision capabilities
- Automated network topology deployment and configuration
- Interactive node and drawing management
- File-based project operations (read, write, list)
- Specialized prompts for different scenarios and skill levels
- Comprehensive tool set for network device management
