- Add explicit AAA/password configuration prohibition to safety reminders
- Implement multiline command expansion for banner and similar commands
- Add error handling for devices with missing device_type tags
- Improve logging for configuration errors and multiline expansions
practice for multi-vendor device support
Refactor Nornir configuration to use host-level connection_options
instead of dynamic groups, following Nornir's configuration priority
model (host > group > defaults).
**Problem:**
Previous implementation used first device's configuration for all
devices, causing Cisco devices to use Huawei driver and vice versa.
**Solution:**
- Each host now has device-specific connection_options at host level
- Single generic "network_devices" group for shared settings
- Host-level config automatically overrides group-level config
**Changes:**
- Remove: _get_nornir_groups_config() helper function
- Remove: _get_nornir_group() helper function
- Simplify: _initialize_nornir() to use single generic group
- Update: get_gns3_device_port.py() to return host-level config
- Reserve: platform field for future NAPALM/scrapli plugin support
**Benefits:**
- Cleaner code structure (no dynamic group creation)
- Follows Nornir best practice ("configuration proximity")
- Easy to extend with new device types
- Properly handles mixed-vendor topologies
Netmiko's `ssh_dispatcher` calculates platform lists at module import
time. When custom device types (like Huawei CE) are registered
dynamically, these cached lists become stale and do not include the
new platforms.
This change imports `netmiko.ssh_dispatcher` and recalculates the
`platforms`, `platforms_base`, and `telnet_platforms` attributes to
ensure Netmiko recognizes the custom device types.
- 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
Disable mypy type checking for the Huawei CE driver module due to
Netmiko library limitations. Netmiko lacks type stubs (py.typed) and
uses dynamic attributes, which causes unresolved import and attribute
errors in static analysis.
Added a comprehensive comment block at the top of the file explaining
the rationale for disabling mypy to prevent future confusion.
Additionally, performed code cleanup including:
- Reformatted module docstrings and comments for better readability
- Added missing imports (importlib, logging)
- Removed unused typing imports (Optional)
- Refactored variable assignments in send_config_set for clarity
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 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.
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>
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
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.
- 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
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
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
Set logging level for nornir.core and nornir loggers to WARNING in config_tools_nornir.py and display_tools_nornir.py. This prevents nornir from logging task execution at INFO level to the console, as the logging={"enabled": False} parameter in InitNornir only disables plugin internal logs.
- 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 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.
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
Refactor context preparation to embed topology information directly into the system prompt using a {{topology_info}} placeholder, replacing the previous approach of appending a separate SystemMessage. This consolidates context into a single system message and simplifies token accounting. The logging is updated to reflect the new token breakdown, showing combined system prompt tokens (base + topology) instead of separate components.
The GNS3TopologyTool has been removed from the list of available tools for the agent. This change simplifies the toolset by eliminating a tool that is no longer needed or supported in the current workflow.
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
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.
- Remove unused `llm_calls` variable and redundant logging in `should_continue`
- Simplify title generation routing by eliminating unnecessary condition checks
- Improve code clarity and maintainability by focusing on essential flow control
- 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.
Add initialization check for `_checkpointer_conn` in `AgentService` to ensure the checkpointer database connection exists before attempting to retrieve chat sessions. This prevents potential null reference errors when the checkpointer hasn't been initialized yet during agent operations.
Introduce a new tool for updating node names within the GNS3 copilot agent. This expands the agent's capabilities to include node name modifications alongside existing node and link management operations.
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.
- Remove `selected_project` tuple from MessagesState as it's no longer needed
- Replace tuple-based project selection with direct `project_id` from config
- Remove unused `mode` parameter from llm_call function
- Update topology retrieval to use project_id directly from configurable settings
- Streamline context messages by removing redundant project info formatting
- Move project_id from metadata to configurable section in agent_service config
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.
The `stream_chat` method was yielding a "done" message after streaming all chunks, but this is unnecessary as the streaming completion is already indicated by the end of the stream. Removing this redundant message simplifies the response handling and aligns with typical streaming patterns.
