Collabmem – a memory system for long-term collaboration with AI

Stores conversation history in a structured format with semantic embeddings, enabling the system to retrieve relevant past interactions based on meaning rather than keyword matching. Uses vector similarity search to surface contextually relevant memories across long conversation threads, allowing the AI to reference and build upon previous discussions without explicit recall prompts.

Maintains structured state across multiple conversation turns, tracking conversation metadata (participants, timestamps, topics), message relationships, and conversation branches. Implements a graph or tree structure to represent conversation flow, allowing the system to navigate between different discussion threads and maintain coherent context across non-linear conversation patterns.

Automatically generates summaries of conversation segments, extracts key decisions and action items, and synthesizes insights from multiple conversation threads. Uses LLM-based summarization to create hierarchical summaries (conversation-level, session-level, project-level) that compress long histories into actionable insights while preserving critical context.

Automatically retrieves relevant memories from the persistent store and injects them into LLM prompts as context, using a retrieval-augmented generation (RAG) pattern. Implements ranking and filtering logic to select the most relevant memories, manages token budgets to fit memories within context windows, and formats memories for optimal LLM comprehension.

Persists conversation memories to durable storage with versioning and change tracking, enabling recovery of past conversation states and audit trails of memory modifications. Implements append-only or snapshot-based storage patterns to ensure memory integrity and support rollback to previous collaboration states.

Manages memory visibility and access permissions across multiple participants in a collaboration session, implementing role-based or permission-based filtering of memories. Ensures that private or sensitive memories are not exposed to unauthorized participants while maintaining shared context for collaborative work.

Evaluates the quality and relevance of stored memories using heuristics or learned models, ranking memories by usefulness for current queries. Implements scoring mechanisms based on recency, frequency of reference, semantic similarity, and explicit user feedback to surface high-quality memories and deprioritize stale or irrelevant context.

Automatically identifies and extracts topics, themes, and entities from conversations, applying semantic tags or categories to memories for improved organization and retrieval. Uses NLP techniques (topic modeling, named entity recognition, or LLM-based extraction) to label memories with relevant topics, enabling topic-based filtering and navigation of conversation history.

Provides abstraction layer for integrating with multiple LLM providers (OpenAI, Anthropic, local models, etc.) and external APIs for embeddings, summarization, and other NLP tasks. Implements provider-agnostic interfaces and handles API-specific authentication, rate limiting, and error handling to enable flexible model selection and provider switching.

Exports collaborative memories in multiple formats (markdown, JSON, structured documents) and generates knowledge bases or documentation from conversation history. Enables creation of project wikis, decision logs, or reference materials from accumulated memories, with customizable export templates and filtering options.