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)
- Extract context variable management functions from connector_factory.py to new context_helpers.py module
- Update imports in gns3_copilot.py, agent_service.py, and __init__.py to use new module
- Remove inline imports and ensure consistent access to context helpers
- Improves code organization and maintainability by separating concerns
- 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
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
Improve logging in GNS3 topology reader to provide more structured and informative output. The previous single-line JSON log has been replaced with separate info and debug logs:
- Info log now includes project ID, project name, node count, and link count
- Debug log retains the full topology details for deeper inspection
This makes it easier to monitor topology retrieval in production while keeping detailed data available for debugging.
Add GNS3ProjectInfoTool to the __all__ list in gns3_client/__init__.py to make it available for import. This ensures the tool is properly exposed as part of the public API for use in copilot agent modules.
- Add info and debug logging to llm_call node for tracking LLM invocations and configuration
- Add error handling and logging to tool_node for tool execution failures
- Add startup logging to stream_chat method with session details
- Improve observability of agent workflow and debugging capabilities
The `get_gns3_connector` function now accepts an optional `jwt_token` parameter. If not provided, the token will be retrieved from context, improving flexibility for scenarios where authentication is handled externally or deferred.
- Refactor `llm_call` and `generate_title` nodes to retrieve `llm_config` from request-scoped context variable instead of LangGraph config
- Remove `jwt_token` and `llm_config` from LangGraph configurable parameters in `AgentService.stream`
- Add `set_current_llm_config` and `get_current_llm_config` functions to `connector_factory` and export them in `__init__.py`
- Update `tool_node` to no longer extract `jwt_token` from config as it is now handled via context variable
- Improves thread safety and decouples configuration from LangGraph's state management
- Move JWT token from state to configurable context for better security and request isolation
- Add user_id parameter to agent service for enhanced metadata tracking
- Update checkpoint directory name from .gns3-copilot to gns3-copilot
- Implement context-aware JWT token management using ContextVar
- Improve tool node to extract JWT token from config instead of state
- Replace separate user_id and jwt_token parameters with unified llm_config dict
- Simplify model factory to accept llm_config directly instead of fetching from API
- Update llm_call and generate_title nodes to extract llm_config from LangGraph config
- Remove deprecated API fetching logic from model factory
- Maintain backward compatibility for existing tool usage patterns
This change centralizes LLM configuration management, reducing API calls and improving performance by passing configuration directly from the API layer rather than fetching it repeatedly.
Refactor get_llm_config to handle various async contexts more robustly. The function now properly checks for running event loops using asyncio.get_running_loop() and handles edge cases when called from thread pools or existing loops. This prevents RuntimeErrors and ensures reliable execution in both sync and async environments.
Remove support for environment variable fallback in LLM model configuration. The configuration now strictly follows:
1. Provided llm_config dictionary (highest priority)
2. Fetch from llm_model_configs system via connector_factory (requires user_id and jwt_token)
This change ensures consistent configuration management and eliminates the outdated environment variable approach. When no configuration is found, a clear ValueError is raised with appropriate error messages.
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.
Removed GNS3UpdateDrawingTool import and from __all__ list in gns3_client __init__.py to clean up the public interface and eliminate unused or deprecated components.
Update all references from FlowNet-Lab to GNS3-Copilot in package names, documentation, and logging. This includes:
- Module and package __init__.py files
- License headers and file descriptions
- Log messages and internal comments
- Remove deprecated tools: GNS3CreateAreaDrawingTool and LinuxTelnetBatchTool
The renaming aligns with the project's new branding while maintaining all existing functionality.
Refactor model factory to support three configuration sources in priority order:
1. Direct llm_config dictionary parameter
2. Fetch from llm_model_configs system via connector_factory (requires user_id and jwt_token)
3. Environment variables as fallback for backward compatibility
This improves flexibility by allowing runtime configuration while maintaining compatibility with existing environment-based setups.
- Add `get_gns3_server_host()` function to `connector_factory.py` for extracting GNS3 server hostname from controller, config, or default URL
- Export new function in `__init__.py` for public API access
- Replace `os.getenv("GNS3_SERVER_HOST", "127.0.0.1")` calls with `get_gns3_server_host()` in Nornir configuration tools (`config_tools_nornir.py`, `display_tools_nornir.py`)
- Ensures consistent host detection across tools using the same priority logic as `get_gns3_connector`
Introduce `get_gns3_connector_with_llm_config` as a convenience function that combines the creation of a GNS3 API connector and retrieval of the user's default LLM configuration. This simplifies initialization for operations requiring both GNS3 connectivity and AI model settings, reducing boilerplate code in callers. The function returns a dictionary containing the connector and LLM config, or None on failure.
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
