Agent Workflow Memory is a Python library designed to augment AI agents with persistent memory across complex workflows. It leverages vector stores to encode and retrieve relevant context, enabling agents to recall past interactions, maintain state, and make informed decisions. The library integrates seamlessly with frameworks like LangChain’s WorkflowAgent, providing customizable memory callbacks, data eviction policies, and support for various storage backends. By housing conversation histories and task metadata in vector databases, it allows semantic similarity searches to surface the most relevant memories. Developers can fine-tune retrieval scopes, compress historical data, and implement custom persistence strategies. Ideal for long-running sessions, multi-agent coordination, and context-rich dialogues, Agent Workflow Memory ensures AI agents operate with continuity, enabling more natural, context-aware interactions while reducing redundancy and improving efficiency.

Sherloq is a next-generation collaborative platform focused on SQL data management. It enables teams to easily create, save, and share queries across various projects, significantly reducing time spent on data retrieval and analysis. By automating the documentation process, it helps users quickly find and understand their data. The platform integrates seamlessly with popular IDEs and requires no restructuring of existing workflows, making it an ideal tool for data-driven teams looking to enhance productivity and collaboration.