{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-deepadata-langchain-adapter","slug":"deepadata-langchain-adapter","name":"deepadata-langchain-adapter","type":"framework","url":"https://github.com/deepadata/deepadata-adapters/tree/main/packages/langchain#readme","page_url":"https://unfragile.ai/deepadata-langchain-adapter","categories":["frameworks-sdks"],"tags":["langchain","edm","emotional-data-model","deepadata","memory","ai-memory","enrichment","llm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-deepadata-langchain-adapter__cap_0","uri":"capability://memory.knowledge.emotional.schema.enrichment.for.memory.contexts","name":"emotional-schema-enrichment-for-memory-contexts","description":"Wraps LangChain memory implementations with an Emotional Data Model (EDM) layer that annotates conversation contexts with governed emotional metadata (valence, arousal, sentiment dimensions). The adapter intercepts memory read/write operations, applies schema-based emotional classification to messages, and stores enriched tuples of (content, emotional_vector, timestamp) without modifying underlying LangChain memory backends, enabling downstream reasoning over emotional state trajectories.","intents":["I want my chatbot to track emotional context across conversations so it can respond with appropriate empathy or tone shifts","I need to analyze conversation patterns by emotional state to identify user frustration or satisfaction trends","I want to condition agent behavior on detected emotional context without rewriting my LangChain memory setup"],"best_for":["LangChain developers building empathetic conversational agents","teams building customer support bots that need emotional awareness","researchers studying emotional dynamics in human-AI interaction"],"limitations":["EDM schema is opinionated — limited customization of emotional dimensions without forking the adapter","Emotional classification adds latency per message (inference cost depends on underlying model used for EDM)","No built-in persistence layer — emotional metadata lives only in active memory, requires external storage for historical analysis","Requires explicit LangChain memory type compatibility — not all custom memory implementations supported"],"requires":["LangChain 0.0.100+ (or compatible version with memory interface)","Node.js 14+ or Python 3.8+ depending on adapter variant","Access to an LLM or classifier for emotional schema inference (local or API-based)"],"input_types":["text (conversation messages)","structured memory objects (LangChain BaseMemory instances)"],"output_types":["enriched memory tuples with emotional metadata","emotional state vectors (numeric arrays)","annotated conversation history with emotional tags"],"categories":["memory-knowledge","emotional-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_1","uri":"capability://memory.knowledge.memory.type.agnostic.emotional.annotation","name":"memory-type-agnostic-emotional-annotation","description":"Provides a unified interface that works across multiple LangChain memory types (BufferMemory, ConversationSummaryMemory, EntityMemory, etc.) by implementing a generic wrapper that intercepts the memory interface contract. The adapter detects the underlying memory type, applies EDM enrichment at the abstraction layer, and delegates actual storage to the original memory implementation, enabling emotional tracking regardless of memory strategy chosen.","intents":["I want to add emotional awareness to my existing LangChain memory without rewriting it","I need to switch between memory types (buffer to summary) while preserving emotional context","I want to compare how different memory strategies handle emotional state tracking"],"best_for":["LangChain users with existing memory implementations who want to add emotional awareness incrementally","teams evaluating different memory strategies and need emotional metrics across all of them","developers building memory abstraction layers that need to be emotion-aware"],"limitations":["Adapter overhead adds ~50-150ms per memory operation depending on EDM inference complexity","Some LangChain memory types with custom serialization may require adapter-specific handling","Emotional metadata is not persisted by default — requires explicit integration with LangChain's load/save mechanisms","No automatic migration of emotional metadata when switching between memory types"],"requires":["LangChain 0.0.100+","Compatible memory type (BufferMemory, ConversationSummaryMemory, EntityMemory, or custom implementing BaseMemory)","Node.js 14+ or Python 3.8+"],"input_types":["LangChain memory instances","conversation messages (string or structured format)","memory configuration objects"],"output_types":["wrapped memory instances with emotional annotation capability","enriched memory output with emotional metadata attached"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_2","uri":"capability://data.processing.analysis.emotional.state.vector.generation","name":"emotional-state-vector-generation","description":"Converts conversation messages and context into multi-dimensional emotional state vectors using the EDM schema, producing numeric representations of emotional dimensions (e.g., valence, arousal, sentiment polarity, emotional intensity). The adapter applies a configurable classifier (LLM-based, rule-based, or hybrid) to extract emotional features from text, normalizes them to a standard vector space, and stores the vectors alongside memory entries for downstream reasoning or similarity comparisons.","intents":["I want to represent emotional state as vectors so I can use vector similarity to find emotionally similar past conversations","I need to track emotional trajectory over time as numeric data for analysis or visualization","I want to condition agent decisions on emotional state vectors (e.g., increase empathy if arousal is high)"],"best_for":["developers building emotional analytics dashboards for conversational AI","teams using vector databases and wanting to add emotional similarity search","researchers analyzing emotional patterns in conversation datasets"],"limitations":["Vector dimensionality is fixed by EDM schema — no dynamic dimension selection","Emotional classification accuracy depends on underlying model quality; may not capture nuanced or cultural emotional expressions","Vector generation adds inference latency (typically 100-500ms per message depending on model)","No built-in vector persistence — requires external vector store integration (e.g., Pinecone, Weaviate)"],"requires":["LangChain 0.0.100+","Emotional classifier (LLM endpoint, local model, or rule-based system)","Vector storage backend if persisting vectors (optional but recommended for scale)"],"input_types":["text (conversation messages)","structured memory objects with text content"],"output_types":["numeric vectors (float arrays, typically 5-20 dimensions)","emotional metadata objects with vector + scalar scores"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_3","uri":"capability://memory.knowledge.emotional.context.aware.memory.retrieval","name":"emotional-context-aware-memory-retrieval","description":"Enhances LangChain memory retrieval by filtering or ranking memory entries based on emotional context similarity. When retrieving relevant past messages, the adapter compares the current emotional state vector against stored emotional vectors, optionally re-ranking results by emotional relevance or filtering to emotionally similar contexts. This enables the agent to retrieve not just semantically relevant memories but emotionally resonant ones, improving response coherence in emotionally-charged conversations.","intents":["I want my agent to retrieve past conversations with similar emotional tone to respond more naturally","I need to filter memory retrieval to only emotionally relevant contexts (e.g., don't surface frustrated user interactions when user is calm)","I want to weight memory retrieval by emotional similarity in addition to semantic similarity"],"best_for":["conversational AI teams building empathetic response systems","customer support bots that need to match emotional tone with user state","dialogue systems where emotional continuity improves user experience"],"limitations":["Emotional filtering may exclude semantically relevant but emotionally distant memories, potentially missing important context","Re-ranking adds latency to memory retrieval (vector comparison cost scales with memory size)","Requires pre-computed emotional vectors for all stored memories — retrofitting to existing memory stores requires batch processing","No tuning parameters exposed for emotional similarity threshold — uses fixed defaults"],"requires":["LangChain memory with emotional enrichment (via this adapter)","Pre-computed emotional vectors for stored memories","Vector similarity function (cosine, euclidean, etc.)"],"input_types":["current emotional state vector","memory query (text or structured)","retrieval parameters (k, threshold)"],"output_types":["ranked list of memory entries with emotional similarity scores","filtered memory subset matching emotional criteria"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_4","uri":"capability://memory.knowledge.edm.schema.configuration.and.customization","name":"edm-schema-configuration-and-customization","description":"Provides configuration interfaces to customize the Emotional Data Model schema used for enrichment, allowing developers to define which emotional dimensions are tracked, how they are classified, and how they are normalized. The adapter exposes schema definition objects that map emotional concepts to classifier prompts or rules, enabling teams to adapt the EDM to domain-specific emotional vocabularies (e.g., customer support vs. mental health vs. gaming contexts) without forking the adapter code.","intents":["I want to customize emotional dimensions for my specific domain (e.g., track 'frustration' instead of generic 'arousal')","I need to use a different emotional classification model or rule set than the default","I want to add domain-specific emotional categories (e.g., 'trust', 'urgency') to the standard EDM"],"best_for":["teams building domain-specific conversational AI with custom emotional vocabularies","researchers experimenting with different emotional schema designs","organizations needing to adapt emotional tracking to cultural or contextual norms"],"limitations":["Schema customization requires understanding the EDM architecture — documentation may be limited","Custom emotional dimensions require corresponding classifier logic — no automatic inference","Changing schema after deployment requires re-processing historical emotional metadata","No validation of custom schema consistency — malformed schemas may cause silent failures"],"requires":["LangChain 0.0.100+","Understanding of EDM schema structure and configuration format","Classifier implementation for custom dimensions (LLM prompt, rule set, or model)"],"input_types":["schema configuration objects (JSON or TypeScript)","classifier definitions (prompts, rules, or model references)"],"output_types":["customized EDM instance","emotional vectors with custom dimensions"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_5","uri":"capability://memory.knowledge.emotional.metadata.persistence.integration","name":"emotional-metadata-persistence-integration","description":"Provides hooks and utilities to persist emotional metadata alongside memory entries when using LangChain's save/load mechanisms. The adapter intercepts serialization events, includes emotional vectors and EDM annotations in the persisted data, and reconstructs emotional state when memory is loaded from storage. This enables emotional context to survive across sessions and deployments, supporting long-term emotional trajectory analysis and continuity in multi-session conversations.","intents":["I want to save and restore emotional context when my agent restarts or loads a previous conversation","I need to persist emotional metadata to a database for long-term analysis and auditing","I want to export conversation history with emotional annotations for offline analysis"],"best_for":["teams building persistent conversational agents with session management","applications requiring audit trails of emotional state over time","researchers collecting datasets of conversations with emotional annotations"],"limitations":["Persistence format is adapter-specific — emotional metadata may not be portable to other systems","Serialization adds overhead to save operations; large memory stores with emotional metadata may have significant I/O cost","No built-in compression or optimization for emotional vector storage — can increase storage footprint","Requires explicit integration with LangChain's save/load API — not automatic for all memory types"],"requires":["LangChain 0.0.100+ with save/load support","Storage backend (file system, database, etc.)","Serialization format support (JSON, pickle, etc.)"],"input_types":["memory instances with emotional enrichment","serialization configuration"],"output_types":["serialized memory with emotional metadata (JSON, binary, etc.)","reconstructed memory instances with emotional state restored"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_6","uri":"capability://planning.reasoning.emotional.state.change.detection","name":"emotional-state-change-detection","description":"Monitors emotional state vectors across consecutive messages to detect significant emotional transitions or anomalies (e.g., sudden shift from calm to frustrated, sustained high arousal). The adapter compares emotional vectors using configurable distance metrics and thresholds, emits events when state changes exceed thresholds, and optionally triggers callbacks or logging. This enables agents to react to emotional shifts in real-time, such as escalating to a human agent when user frustration spikes.","intents":["I want to detect when a user becomes frustrated or angry so I can adjust my response strategy","I need to trigger alerts or escalations when emotional state changes significantly","I want to log emotional state transitions for analysis and debugging"],"best_for":["customer support bots that need to detect and respond to user frustration","mental health or wellness chatbots monitoring for emotional distress","conversational agents with adaptive response strategies based on emotional state"],"limitations":["Change detection is threshold-based — requires tuning thresholds for each domain (no automatic calibration)","Detects changes in emotional vectors, not underlying causes — may miss context-dependent emotional shifts","Requires consecutive messages with emotional vectors — gaps in conversation history may miss transitions","No built-in smoothing or noise filtering — may trigger false positives on noisy emotional classifications"],"requires":["Emotional vectors for consecutive messages","Distance metric function (cosine, euclidean, etc.)","Configurable threshold values"],"input_types":["sequence of emotional state vectors","threshold configuration"],"output_types":["boolean flags indicating state changes","change magnitude scores (numeric)","event objects with before/after emotional state"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-deepadata-langchain-adapter__cap_7","uri":"capability://data.processing.analysis.multi.turn.emotional.trajectory.analysis","name":"multi-turn-emotional-trajectory-analysis","description":"Analyzes emotional state across multiple conversation turns to compute trajectory metrics such as emotional stability, trend direction (improving/deteriorating), and cumulative emotional load. The adapter aggregates emotional vectors over conversation windows, applies statistical measures (variance, slope, moving averages), and produces summary metrics describing the overall emotional arc of a conversation. This enables understanding of long-term emotional patterns rather than isolated snapshots.","intents":["I want to understand if a conversation is trending toward resolution or escalation emotionally","I need to measure emotional stability or volatility in a conversation for quality assessment","I want to compute metrics like 'total emotional load' or 'emotional recovery time' for analysis"],"best_for":["customer support analytics teams measuring conversation quality by emotional arc","conversational AI researchers studying emotional dynamics in dialogue","teams building emotional health monitoring systems"],"limitations":["Trajectory analysis requires sufficient conversation history — short conversations may not produce meaningful metrics","Metrics are aggregate and may mask important local emotional events (e.g., brief spike in frustration)","No built-in visualization — metrics are numeric and require external tools for interpretation","Trajectory computation adds latency proportional to conversation length"],"requires":["Sequence of emotional vectors with timestamps","Minimum conversation length (typically 3+ turns for meaningful trajectory)"],"input_types":["ordered sequence of emotional state vectors","timestamps or turn indices"],"output_types":["trajectory metrics (stability, trend, cumulative load)","statistical summaries (mean, variance, slope)","trajectory classification (improving, stable, deteriorating)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["LangChain 0.0.100+ (or compatible version with memory interface)","Node.js 14+ or Python 3.8+ depending on adapter variant","Access to an LLM or classifier for emotional schema inference (local or API-based)","LangChain 0.0.100+","Compatible memory type (BufferMemory, ConversationSummaryMemory, EntityMemory, or custom implementing BaseMemory)","Node.js 14+ or Python 3.8+","Emotional classifier (LLM endpoint, local model, or rule-based system)","Vector storage backend if persisting vectors (optional but recommended for scale)","LangChain memory with emotional enrichment (via this adapter)","Pre-computed emotional vectors for stored memories"],"failure_modes":["EDM schema is opinionated — limited customization of emotional dimensions without forking the adapter","Emotional classification adds latency per message (inference cost depends on underlying model used for EDM)","No built-in persistence layer — emotional metadata lives only in active memory, requires external storage for historical analysis","Requires explicit LangChain memory type compatibility — not all custom memory implementations supported","Adapter overhead adds ~50-150ms per memory operation depending on EDM inference complexity","Some LangChain memory types with custom serialization may require adapter-specific handling","Emotional metadata is not persisted by default — requires explicit integration with LangChain's load/save mechanisms","No automatic migration of emotional metadata when switching between memory types","Vector dimensionality is fixed by EDM schema — no dynamic dimension selection","Emotional classification accuracy depends on underlying model quality; may not capture nuanced or cultural emotional expressions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.07649229846498884,"quality":0.26,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":291,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepadata-langchain-adapter","compare_url":"https://unfragile.ai/compare?artifact=deepadata-langchain-adapter"}},"signature":"QzCtZZfFP4sCj6rr5NKsoBqecPfmtKAMTXry2upqPPNkjBFrvKhKK+2kHeKiRFjIiIUrnM842i0tnqkSsnbBBg==","signedAt":"2026-06-20T14:12:54.209Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepadata-langchain-adapter","artifact":"https://unfragile.ai/deepadata-langchain-adapter","verify":"https://unfragile.ai/api/v1/verify?slug=deepadata-langchain-adapter","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"}}