{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47726056","slug":"collabmem-a-memory-system-for-long-term-collaborat","name":"Collabmem – a memory system for long-term collaboration with AI","type":"repo","url":"https://github.com/visionscaper/collabmem","page_url":"https://unfragile.ai/collabmem-a-memory-system-for-long-term-collaborat","categories":["ai-agents"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47726056__cap_0","uri":"capability://memory.knowledge.persistent.conversation.memory.with.semantic.indexing","name":"persistent conversation memory with semantic indexing","description":"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.","intents":["I want the AI to remember what we discussed last week without me having to repeat context","I need the AI to connect insights from multiple past conversations to inform current decisions","I want to avoid re-explaining my project goals and constraints in every interaction"],"best_for":["Teams running iterative AI-assisted workflows over weeks or months","Solo developers building long-term projects with AI pair programming","Researchers conducting multi-session experiments with AI assistance"],"limitations":["Semantic search quality depends on embedding model quality; may miss nuanced context without explicit tagging","Memory retrieval adds latency (~100-500ms per query depending on index size)","No built-in deduplication of similar memories, leading to potential redundancy in large conversation histories","Requires external vector database or embedding service; no lightweight in-process option documented"],"requires":["Embedding model (OpenAI, Anthropic, or local alternative)","Vector database or storage backend (Pinecone, Weaviate, or file-based)","Python 3.8+ or Node.js 16+"],"input_types":["conversation text","user queries","AI responses"],"output_types":["retrieved memory snippets","relevance scores","augmented context for LLM prompts"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_1","uri":"capability://memory.knowledge.multi.turn.conversation.state.management","name":"multi-turn conversation state management","description":"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.","intents":["I want to branch off into a tangent discussion and then return to the main topic without losing context","I need to track who said what and when across a long collaboration session","I want to see how a conversation evolved and revisit earlier decision points"],"best_for":["Multi-participant AI collaboration sessions","Iterative design or development workflows with frequent context switches","Research or analysis projects requiring conversation audit trails"],"limitations":["Conversation graph complexity grows quadratically with branching depth; may impact retrieval performance","No built-in conflict resolution for concurrent edits to shared conversation state","Requires explicit state serialization/deserialization; no automatic sync across distributed sessions"],"requires":["Persistent storage backend (file system, database, or cloud storage)","JSON or structured serialization format support"],"input_types":["user messages","AI responses","metadata (timestamps, participant IDs)"],"output_types":["conversation tree/graph structure","conversation summaries","context windows for specific turns"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_2","uri":"capability://text.generation.language.collaborative.memory.synthesis.and.summarization","name":"collaborative memory synthesis and summarization","description":"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.","intents":["I want a quick summary of what we decided in last month's discussions without reading through everything","I need to extract action items and decisions from a long collaboration session","I want to see the evolution of our thinking on a particular topic across multiple conversations"],"best_for":["Long-running projects with frequent AI collaboration sessions","Teams needing to onboard new members into ongoing AI-assisted workflows","Projects requiring decision audit trails and rationale documentation"],"limitations":["Summarization quality depends on LLM capability; may lose nuanced context or misinterpret implicit decisions","Hierarchical summarization requires multiple LLM calls, adding latency and cost","No built-in mechanism to flag contradictions or evolving decisions across summaries","Requires manual tuning of summary granularity and compression ratios"],"requires":["LLM API access (OpenAI, Anthropic, or compatible)","Conversation history in structured format","Sufficient token budget for summarization passes"],"input_types":["conversation text","conversation metadata","time ranges or conversation segments"],"output_types":["text summaries","extracted action items","decision logs","insight synthesis"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_3","uri":"capability://memory.knowledge.context.aware.prompt.augmentation.with.retrieved.memories","name":"context-aware prompt augmentation with retrieved memories","description":"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.","intents":["I want the AI to automatically reference relevant past discussions without me having to manually copy-paste context","I need the AI to understand my project constraints and preferences from previous conversations","I want the AI to avoid repeating suggestions it already made in past sessions"],"best_for":["Developers using AI for iterative coding tasks across multiple sessions","Product teams collaborating with AI on design and specification refinement","Researchers running multi-session experiments with consistent AI assistance"],"limitations":["Token budget constraints may force truncation of retrieved memories, losing context","Retrieval ranking may surface irrelevant memories if semantic similarity is high but context is different","No explicit mechanism to handle contradictory memories or outdated context","Augmentation adds latency to every prompt (~200-800ms depending on retrieval and ranking complexity)"],"requires":["Semantic search capability (vector database or embedding API)","LLM with sufficient context window to accommodate augmented prompts","Memory store with indexed conversation history"],"input_types":["user query","conversation context","memory index"],"output_types":["augmented prompt with retrieved memories","relevance scores for retrieved memories","token usage estimates"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_4","uri":"capability://memory.knowledge.collaborative.memory.persistence.and.versioning","name":"collaborative memory persistence and versioning","description":"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.","intents":["I want to restore a conversation to an earlier state if we went down the wrong path","I need an audit trail of how our decisions and understanding evolved over time","I want to ensure memories are not lost if the system crashes mid-session"],"best_for":["Teams requiring compliance or audit trails for AI-assisted decisions","Long-running projects where reverting to earlier collaboration states is valuable","Distributed teams where memory consistency across instances is critical"],"limitations":["Versioning overhead increases storage requirements linearly with conversation length","Rollback operations may require re-indexing of semantic embeddings, adding latency","No built-in conflict resolution for concurrent modifications to shared memories","Append-only storage can lead to unbounded growth without explicit compaction/cleanup"],"requires":["Persistent storage backend (file system, database, or cloud storage)","Versioning/change-tracking implementation (git-like, event-sourcing, or snapshot-based)","Sufficient disk space for conversation history and versions"],"input_types":["conversation messages","memory updates","metadata changes"],"output_types":["versioned memory snapshots","change logs","rollback operations"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_5","uri":"capability://memory.knowledge.multi.participant.memory.isolation.and.access.control","name":"multi-participant memory isolation and access control","description":"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.","intents":["I want to keep some conversation context private while sharing other parts with collaborators","I need to ensure sensitive information from past sessions is not leaked to new team members","I want different participants to have different levels of access to the shared memory store"],"best_for":["Multi-team or multi-organization collaborations using shared AI assistants","Enterprises requiring data isolation and access control for AI-assisted workflows","Projects with mixed public and confidential discussion threads"],"limitations":["Access control adds complexity to memory retrieval; may require filtering at query time, impacting performance","No built-in encryption of sensitive memories; requires external encryption layer for data at rest","Permission model must be explicitly defined; no sensible defaults for complex organizational structures","Audit logging of access attempts adds storage and computational overhead"],"requires":["Identity/authentication system for participants","Permission/access control model definition","Secure storage backend with encryption support"],"input_types":["participant identity","permission rules","memory content with access metadata"],"output_types":["filtered memory results","access denied responses","audit logs"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_6","uri":"capability://memory.knowledge.memory.quality.assessment.and.relevance.ranking","name":"memory quality assessment and relevance ranking","description":"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.","intents":["I want the AI to prioritize recent decisions over old ones when retrieving context","I need the system to learn which memories are actually useful and surface those first","I want to downrank memories that turned out to be incorrect or outdated"],"best_for":["Long-running collaborations where memory quality degrades over time","Teams wanting to improve memory retrieval accuracy through feedback","Projects where recent context is more valuable than historical context"],"limitations":["Quality assessment heuristics may not capture domain-specific relevance; requires tuning per use case","Learning from user feedback requires explicit feedback signals; implicit feedback is unreliable","Ranking computation adds latency to memory retrieval (~50-200ms depending on scoring complexity)","No built-in mechanism to detect and remove contradictory or superseded memories"],"requires":["Scoring/ranking model (heuristic-based or learned)","User feedback mechanism (explicit ratings or implicit signals)","Metadata tracking (recency, reference frequency, user ratings)"],"input_types":["memory content","query context","user feedback","metadata (timestamps, reference counts)"],"output_types":["relevance scores","ranked memory lists","quality metrics"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_7","uri":"capability://data.processing.analysis.conversation.topic.extraction.and.tagging","name":"conversation topic extraction and tagging","description":"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.","intents":["I want to find all conversations related to a specific feature or project component","I need to see what topics we've discussed and how they relate to each other","I want to filter memories by topic to avoid irrelevant context in retrieval"],"best_for":["Large projects with diverse discussion topics across many conversations","Teams wanting to organize and navigate long conversation histories","Projects requiring topic-based analysis of collaboration patterns"],"limitations":["Topic extraction quality depends on NLP model; may misidentify topics in domain-specific conversations","Requires additional LLM calls or model inference, adding latency and cost","No built-in mechanism to merge or consolidate related topics; manual curation may be needed","Topic taxonomy must be defined or learned; no universal topic model"],"requires":["NLP model for topic extraction (spaCy, NLTK, or LLM-based)","Conversation text in structured format","Storage for topic metadata"],"input_types":["conversation text","conversation metadata"],"output_types":["topic tags","entity lists","topic hierarchies","topic-based memory indices"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_8","uri":"capability://tool.use.integration.integration.with.external.llm.providers.and.apis","name":"integration with external llm providers and apis","description":"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.","intents":["I want to use different LLM providers for different tasks without rewriting code","I need to switch from one LLM provider to another without disrupting my workflow","I want to use local models for privacy-sensitive conversations while using cloud APIs for others"],"best_for":["Teams wanting flexibility in LLM provider selection","Projects with privacy or cost constraints requiring model switching","Developers building multi-provider AI applications"],"limitations":["Abstraction layer adds complexity and potential performance overhead","Provider-specific features (function calling, vision, etc.) may not be uniformly supported","Rate limiting and quota management must be handled per provider","API costs vary significantly across providers; no built-in cost optimization"],"requires":["API keys for one or more LLM providers","Network connectivity for cloud-based providers","Python 3.8+ or Node.js 16+"],"input_types":["prompts","conversation context","text for embedding/summarization"],"output_types":["LLM responses","embeddings","summaries","structured extractions"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47726056__cap_9","uri":"capability://text.generation.language.memory.export.and.knowledge.base.generation","name":"memory export and knowledge base generation","description":"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.","intents":["I want to generate project documentation from our collaboration history","I need to export memories for sharing with stakeholders or archival","I want to create a knowledge base or FAQ from our conversations"],"best_for":["Teams wanting to document decisions and rationale from AI-assisted projects","Projects requiring knowledge base generation from conversation history","Organizations needing to preserve institutional knowledge from collaborations"],"limitations":["Export quality depends on memory organization and metadata; poorly tagged memories produce poor exports","Knowledge base generation requires manual curation and editing; fully automated generation is unreliable","Large exports may be unwieldy; requires filtering or segmentation for readability","Format conversion may lose context or structure; no universal export format"],"requires":["Structured memory store with metadata","Export template definitions","Document generation library (Markdown, PDF, etc.)"],"input_types":["memory store","export filters/queries","template definitions"],"output_types":["markdown documents","JSON exports","HTML/PDF knowledge bases","structured documentation"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Embedding model (OpenAI, Anthropic, or local alternative)","Vector database or storage backend (Pinecone, Weaviate, or file-based)","Python 3.8+ or Node.js 16+","Persistent storage backend (file system, database, or cloud storage)","JSON or structured serialization format support","LLM API access (OpenAI, Anthropic, or compatible)","Conversation history in structured format","Sufficient token budget for summarization passes","Semantic search capability (vector database or embedding API)","LLM with sufficient context window to accommodate augmented prompts"],"failure_modes":["Semantic search quality depends on embedding model quality; may miss nuanced context without explicit tagging","Memory retrieval adds latency (~100-500ms per query depending on index size)","No built-in deduplication of similar memories, leading to potential redundancy in large conversation histories","Requires external vector database or embedding service; no lightweight in-process option documented","Conversation graph complexity grows quadratically with branching depth; may impact retrieval performance","No built-in conflict resolution for concurrent edits to shared conversation state","Requires explicit state serialization/deserialization; no automatic sync across distributed sessions","Summarization quality depends on LLM capability; may lose nuanced context or misinterpret implicit decisions","Hierarchical summarization requires multiple LLM calls, adding latency and cost","No built-in mechanism to flag contradictions or evolving decisions across summaries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.3,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:10:14.221Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=collabmem-a-memory-system-for-long-term-collaborat","compare_url":"https://unfragile.ai/compare?artifact=collabmem-a-memory-system-for-long-term-collaborat"}},"signature":"J5NUSTfGp6hCP0I2VpjCHLBuF+q5CBgXt21Y8kkTzmOqrMs1NHAE8ntbod5JmBF7GMTX5j9k6dr2/M1osvvxAA==","signedAt":"2026-06-21T12:02:54.726Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/collabmem-a-memory-system-for-long-term-collaborat","artifact":"https://unfragile.ai/collabmem-a-memory-system-for-long-term-collaborat","verify":"https://unfragile.ai/api/v1/verify?slug=collabmem-a-memory-system-for-long-term-collaborat","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}