Retain: A Unified Knowledge Base for AI Coding Conversations

Bayram Annakov has unveiled Retain, a native macOS application designed to solve a common problem for developers using artificial intelligence tools. The software acts as a unified knowledge base, aggregating conversations from multiple AI coding platforms into a single, searchable interface.

As developers increasingly rely on AI assistants like Claude and ChatGPT for daily coding tasks, valuable insights, decisions, and patterns often become lost across disparate chat histories. Retain aims to preserve this institutional knowledge by providing a centralized, local-first repository for all AI-driven coding interactions.

Modern developers frequently switch between multiple AI coding assistants, creating a fragmented landscape of valuable conversations. Annakov identified this issue firsthand, noting that "every conversation contains decisions, corrections, and patterns I forget existed weeks later." This fragmentation leads to repetitive work, as developers often find themselves re-explaining the same preferences and context to different AI systems.

The challenge extends beyond simple forgetfulness; it represents a significant efficiency drain. When critical coding decisions are scattered across claude.ai, ChatGPT, and Claude Code sessions, retrieving specific information becomes a time-consuming manual process. This problem inspired the creation of claude-reflect, an earlier CLI tool that extracted learnings from Claude Code sessions.

Retain represents the evolution of this concept, moving from a command-line interface to a full-featured native application. The transition reflects a broader need for accessible, visual tools that can handle the complexity of modern AI-assisted development workflows.

Retain functions as a comprehensive aggregator, pulling conversations from four primary sources: Claude Code, claude.ai, ChatGPT, and Codex CLI. The application creates a unified view of all interactions, eliminating the need to manually search through individual platform histories.

The core technical architecture relies on SQLite with FTS5 (Full-Text Search) extension, enabling instant search capabilities across thousands of conversations. This local-first approach ensures that all data remains on the user's machine, providing both speed and privacy benefits.

Key technical features include:

• Native macOS application for optimal performance
• Instant full-text search across all conversation history
• Local SQLite database storage with no external servers
• Web synchronization using browser cookies for direct conversation fetching
• Zero telemetry or data collection

Retain adopts a local-first philosophy, a critical design choice for developers handling sensitive code and proprietary information. All conversation data is stored exclusively in a local SQLite database on the user's machine, with no servers involved in the storage process.

This architecture provides several distinct advantages:

• Complete data ownership and control
• Enhanced security for proprietary code discussions
• Offline access to all conversation history
• No dependency on external cloud services for data storage

The application's web synchronization mechanism operates by using the user's browser cookies to fetch conversations directly from the respective platforms. This approach maintains the local-first principle while still enabling the aggregation of data from web-based AI interfaces.

Retain builds upon the foundation of claude-reflect, a command-line interface tool that Annakov initially developed to extract learnings from Claude Code sessions. The transition from CLI to native macOS application represents a significant step forward in usability and accessibility.

While the CLI tool served its purpose for technical users comfortable with terminal commands, the native app expands the potential user base to include developers who prefer graphical interfaces. This evolution mirrors the broader trend of developer tools becoming more user-friendly and visually oriented.

The development journey from claude-reflect to Retain demonstrates an iterative approach to solving real-world problems. By first addressing a specific need with a minimal tool, then expanding its capabilities and accessibility, the project has matured into a more comprehensive solution for AI conversation management.

Retain addresses a growing pain in the AI-assisted development landscape: the fragmentation of knowledge across multiple platforms. By providing a unified, searchable repository, the application enables developers to maintain continuity in their AI interactions and preserve valuable insights that would otherwise be lost.

The local-first architecture ensures that this knowledge remains secure and accessible, while the instant search capability transforms hours of manual searching into seconds of automated retrieval. As AI coding assistants continue to evolve and proliferate, tools like Retain will become increasingly essential for maintaining productivity and knowledge continuity.

For developers seeking to optimize their AI-assisted workflow, Retain offers a practical solution to a common problem, demonstrating how targeted tools can significantly improve the efficiency of modern software development practices.