* feat: enhance knowledge event handling in Agent class
- Updated the Agent class to include task context in knowledge retrieval events.
- Emitted new events for knowledge retrieval and query processes, capturing task and agent details.
- Refactored knowledge event classes to inherit from a base class for better structure and maintainability.
- Added tracing for knowledge events in the TraceCollectionListener to improve observability.
This change improves the tracking and management of knowledge queries and retrievals, facilitating better debugging and performance monitoring.
* refactor: remove task_id from knowledge event emissions in Agent class
- Removed the task_id parameter from various knowledge event emissions in the Agent class to streamline event handling.
- This change simplifies the event structure and focuses on the essential context of knowledge retrieval and query processes.
This refactor enhances the clarity of knowledge events and aligns with the recent improvements in event handling.
* surface association for guardrail events
* fix: improve LLM selection logic in converter
- Updated the logic for selecting the LLM in the convert_with_instructions function to handle cases where the agent may not have a function_calling_llm attribute.
- This change ensures that the converter can still function correctly by falling back to the standard LLM if necessary, enhancing robustness and preventing potential errors.
This fix improves the reliability of the conversion process when working with different agent configurations.
* fix test
* fix: enforce valid LLM instance requirement in converter
- Updated the convert_with_instructions function to ensure that a valid LLM instance is provided by the agent.
- If neither function_calling_llm nor the standard llm is available, a ValueError is raised, enhancing error handling and robustness.
- Improved error messaging for conversion failures to provide clearer feedback on issues encountered during the conversion process.
This change strengthens the reliability of the conversion process by ensuring that agents are properly configured with a valid LLM.
* rebuilding executor
* removing langchain
* Making all tests good
* fixing types and adding ability for nor using system prompts
* improving types
* pleasing the types gods
* pleasing the types gods
* fixing parser, tools and executor
* making sure all tests pass
* final pass
* fixing type
* Updating Docs
* preparing to cut new version
* Refactoring task cache to be a tool
The previous implementation of the task caching system was early exiting
the agent executor due to the fact it was returning an AgentFinish object.
This now refactors it to use a cache specific tool that is dynamically
added and forced into the agent in case of a task execution that was
already executed with the same input.
* Adding tool caching a loop execution prevention.
This adds some guardrails, to both prevent the same tool to be used
consecutively and also caching tool's results across the entire crew
so it cuts down execution time and eventual LLM calls.
This plays a huge role for smaller opensource models that usually fall
into those behaviors patterns.
It also includes some smaller improvements around the tool prompt and
agent tools, all with the same intention of guiding models into
better conform with agent instructions.
