- 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
Update agent service to properly access content from AIMessageChunk objects during chat model streaming. Instead of using dictionary get method on the chunk, now use getattr to directly access the content attribute, ensuring compatibility with the AIMessageChunk object structure.
- 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This change ensures consistent configuration management and eliminates the outdated environment variable approach.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.
- Change config parameter type from dict to RunnableConfig | None in llm_call and generate_title functions
- This improves type safety and aligns with LangChain's RunnableConfig usage
- No functional changes, only type annotations updated for better integration
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.
- Modified `llm_call` and `generate_title` functions to accept `config` parameter, extracting `user_id` and `jwt_token` for per-user LLM configuration and API authentication
- Updated `create_base_model_with_tools` and `create_title_model` calls to pass user authentication details
- Added `jwt_token` to state for tool usage in GNS3 API calls
- Integrated chat router into controller API routes under `/chat` endpoint
- Implemented `_cleanup_copilot_agent` method in `Project` class to remove AgentService resources upon project closure, preventing resource leaks
- Enhanced error handling in agent cleanup to avoid interrupting project close operations
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.
Removed checkpoint-related imports and exports from the agent package's __init__.py to clean up the public API. This change reduces unnecessary exposure of internal checkpoint utility functions, focusing the public interface on the core agent_builder functionality.
- 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`
Removed GNS3_SERVER_USERNAME and GNS3_SERVER_PASSWORD environment variables from Nornir configuration tools. Credentials are now set to empty strings by default, simplifying configuration and removing dependency on environment variables for authentication.
- Rename module description from FlowNet-Lab to GNS3 Copilot
- Replace linux_specialist_prompt and experiment_deploy_prompt imports with base_prompt
- Update __all__ export list to include SYSTEM_PROMPT instead of removed prompts
- Improve module docstring with detailed component descriptions and available prompts
- Maintain dynamic version management and module metadata
- Remove direct logging of LLM config from gns3_copilot.py
- Update model_factory to accept configuration from llm_model_configs dictionary
- Add fallback to environment variables for backward compatibility
- Centralize configuration loading in _load_llm_config function
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
