{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-airweave-ai--airweave","slug":"airweave-ai--airweave","name":"airweave","type":"agent","url":"https://airweave.ai","page_url":"https://unfragile.ai/airweave-ai--airweave","categories":["rag-knowledge"],"tags":["agent-infrastructure","ai","ai-agents","ai-infrastructure","api","context-retrieval","data-connectors","developer-tools","enterprise-data","information-retrieval","integration","llm","open-source","rag","retrieval","retrieval-augmented-generation","sdk","search","search-api","semantic-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-airweave-ai--airweave__cap_0","uri":"capability://tool.use.integration.multi.source.data.connector.orchestration.with.incremental.sync","name":"multi-source data connector orchestration with incremental sync","description":"Airweave implements a source connector architecture that abstracts heterogeneous data sources (Google Docs, Linear, Intercom, Trello, etc.) through a unified interface. Each connector implements OAuth integration via an Auth Provider System, handles incremental sync using cursor-based tracking to avoid re-processing, and manages token refresh lifecycle. The Temporal Workflow System orchestrates sync jobs with configurable schedules (one-time, recurring, continuous), while the Entity Processing Pipeline streams entities through a queue with backpressure handling and concurrency controls to prevent source API throttling.","intents":["Connect multiple SaaS platforms to a single knowledge base without building custom integrations","Sync only new/changed data incrementally rather than full re-indexing on every run","Manage OAuth tokens and authentication state across multiple third-party services","Schedule and monitor data sync jobs with visibility into progress and error handling"],"best_for":["Enterprise teams building AI agents that need access to fragmented data across 10+ SaaS tools","Developers building RAG systems who want to avoid writing custom source connectors","Organizations with strict data freshness requirements needing scheduled incremental syncs"],"limitations":["Connector coverage limited to pre-built integrations (Google Docs, Linear, Intercom, Trello, ClickUp, OneNote, Word, Google Slides) — custom sources require extending the Source Connector Architecture","Incremental sync relies on source API cursor support — sources without cursor pagination fall back to full sync","Temporal Workflow System adds operational complexity; requires Temporal server deployment for production scheduling","OAuth token refresh requires secure storage in PostgreSQL; self-hosted deployments must manage credential encryption"],"requires":["Python 3.9+","PostgreSQL database for source connection state and credentials","Temporal server for workflow orchestration (can use Temporal Cloud or self-hosted)","OAuth credentials for each source platform being connected","Network access to source APIs (no local-only mode)"],"input_types":["OAuth credentials (client_id, client_secret, refresh_token)","Source configuration (workspace IDs, folder paths, filters)","Sync schedule definitions (cron expressions or one-time triggers)"],"output_types":["Normalized entity objects with breadcrumb metadata (source, document_id, parent_id)","Sync progress metrics (entities_processed, errors, last_sync_timestamp)","Error logs with retry state for failed entities"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_1","uri":"capability://search.retrieval.semantic.search.with.vespa.backed.vector.retrieval.and.agentic.ranking","name":"semantic search with vespa-backed vector retrieval and agentic ranking","description":"Airweave implements a Search System built on Vespa for distributed vector similarity search across indexed entities. The search pipeline accepts natural language queries, converts them to embeddings, and retrieves candidates using Vespa's ranking framework. The Agentic Search capability allows AI agents to refine queries iteratively — agents can inspect initial results, reformulate queries, and re-rank results based on relevance signals. The search operations pipeline supports hybrid search (combining vector similarity with BM25 keyword matching) and filters by collection, source, and metadata breadcrumbs to scope results to relevant document hierarchies.","intents":["Query a multi-source knowledge base with natural language and get semantically relevant results ranked by relevance","Build AI agents that can iteratively refine search queries based on intermediate results","Filter search results by source, collection, or document hierarchy (e.g., 'only from Linear tickets in Q1')","Combine semantic similarity with keyword matching for hybrid search accuracy"],"best_for":["Teams building AI agents that need to search across fragmented enterprise data","RAG systems requiring sub-100ms search latency across millions of documents","Applications where agents need to refine queries based on intermediate results (agentic search)"],"limitations":["Vespa integration requires separate Vespa cluster deployment and maintenance; no embedded vector DB option","Agentic search adds latency per iteration (typically 100-300ms per refinement cycle)","Embedding generation is external dependency — requires OpenAI, Anthropic, or other embedding model","Ranking relevance depends on embedding model quality; poor embeddings degrade search quality regardless of Vespa tuning"],"requires":["Vespa cluster (self-hosted or managed)","Embedding model API (OpenAI, Anthropic, or local model)","Indexed entities in Qdrant or Vespa vector store","Query text (natural language or structured filters)"],"input_types":["Natural language query string","Optional filters (collection_id, source_id, metadata breadcrumbs)","Optional ranking parameters (top_k, similarity_threshold)"],"output_types":["Ranked list of entity results with similarity scores","Metadata breadcrumbs (source, document_id, parent_id) for result context","Agentic search: intermediate results for query refinement"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_10","uri":"capability://automation.workflow.frontend.dashboard.for.collection.management.sync.monitoring.and.usage.analytics","name":"frontend dashboard for collection management, sync monitoring, and usage analytics","description":"Airweave provides a web-based Dashboard with React frontend (state management via Zustand) for managing collections, viewing sync status, and monitoring usage. The Collection Management UI enables creating/editing collections and managing source connections. The dashboard displays sync progress (entities processed, errors, duration) and allows triggering manual syncs. Real-Time Updates and SSE enable live progress updates without polling. The Usage Limits and Billing UI shows API usage, sync counts, and billing status. The Application Structure and Routing uses React Router for navigation between dashboard sections. OAuth Callback Flow is handled transparently in the UI for source connection setup.","intents":["Manage collections and source connections via a user-friendly web interface","Monitor sync progress and errors in real-time","View API usage and billing information","Trigger manual syncs and troubleshoot connection issues"],"best_for":["Non-technical users managing collections and syncs","Operators monitoring sync health and debugging failures","Teams needing visibility into API usage and billing"],"limitations":["Dashboard is web-only; no mobile or desktop app","Real-time updates via SSE require persistent connection; may not work behind some proxies","Zustand state management is local to browser; no cross-device state sync","Dashboard is read-only for some operations (e.g., cannot edit source configuration after creation)"],"requires":["Web browser with JavaScript enabled","Network access to Airweave backend","User account with appropriate permissions"],"input_types":["User interactions (form submissions, button clicks)","OAuth callbacks from source platforms"],"output_types":["Rendered UI with collection, sync, and usage information","Real-time progress updates via SSE","Navigation to different dashboard sections"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_11","uri":"capability://automation.workflow.self.hosted.deployment.with.docker.and.postgresql.qdrant.configuration.management","name":"self-hosted deployment with docker and postgresql/qdrant configuration management","description":"Airweave supports self-hosted deployment via Docker containers. The Docker and Deployment documentation provides Dockerfiles for backend, frontend, and worker services. Configuration Management via environment variables and YAML files (dev.integrations.yaml, prd.integrations.yaml, self-hosted.integrations.yaml) enables customization of OAuth providers, storage backends, and feature flags. The backend service uses PostgreSQL for relational data and Qdrant for vector storage; both can be self-hosted or cloud-managed. The start.sh script automates local setup with Docker Compose. Self-hosted deployments have full control over data residency and can customize integrations (e.g., add custom OAuth providers).","intents":["Deploy Airweave in private infrastructure with full data control","Customize OAuth providers and integrations for specific environments","Manage PostgreSQL and Qdrant infrastructure independently","Enable air-gapped deployments without external service dependencies"],"best_for":["Enterprise organizations with data residency requirements","Teams with existing PostgreSQL/Qdrant infrastructure","Deployments requiring custom OAuth providers or integrations"],"limitations":["Self-hosted deployments require managing PostgreSQL and Qdrant; no managed service option","Temporal Workflow System requires separate Temporal server deployment; adds operational complexity","Configuration management via environment variables and YAML is error-prone; no validation framework","Docker images are not published to public registries; must build from source","Scaling requires managing multiple worker containers and load balancing; no built-in orchestration"],"requires":["Docker and Docker Compose","PostgreSQL 12+ instance","Qdrant instance (self-hosted or managed)","Temporal server (self-hosted or Temporal Cloud)","OAuth credentials for each source platform","Environment variables for configuration"],"input_types":["Docker Compose configuration","Environment variables (.env file)","YAML configuration files (integrations.yaml)"],"output_types":["Running Docker containers (backend, frontend, worker)","PostgreSQL database with schema","Qdrant collections for vector storage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_12","uri":"capability://data.processing.analysis.incremental.sync.with.cursor.based.pagination.and.change.detection","name":"incremental sync with cursor-based pagination and change detection","description":"Airweave implements Incremental Sync and Cursors to avoid re-processing all entities on every sync. Source connectors track a cursor (e.g., last_modified_timestamp, page_token) that marks the point of the last successful sync. On subsequent syncs, the connector fetches only entities modified after the cursor, reducing API calls and processing time. The Sync System stores cursors in PostgreSQL and updates them after each successful sync. Change detection is source-specific: some sources provide modification timestamps, others use pagination tokens. The Entity Processing Pipeline processes only new/changed entities, making incremental syncs much faster than full syncs.","intents":["Sync only new/changed data from sources, avoiding redundant API calls and processing","Reduce sync time and cost for large data sources","Maintain up-to-date knowledge bases with frequent incremental syncs","Detect and process only entities that have changed since last sync"],"best_for":["Large data sources (millions of entities) where full syncs are prohibitively expensive","Frequent sync schedules (hourly, continuous) requiring minimal API usage","Cost-sensitive deployments where API call volume directly impacts expenses"],"limitations":["Incremental sync relies on source API cursor support; sources without cursors fall back to full sync","Cursor tracking is source-specific; some sources don't expose modification timestamps","Cursor corruption (e.g., due to source API changes) requires manual reset and full re-sync","Change detection may miss deletions if source API doesn't expose deleted entities"],"requires":["Source API with cursor support (timestamp, page token, or similar)","PostgreSQL for cursor storage","Source connector implementing cursor-based pagination"],"input_types":["Cursor value from previous sync (timestamp, token, or offset)","Source configuration and credentials"],"output_types":["New/changed entities since cursor","Updated cursor for next sync","Sync metadata (entities_processed, cursor_value, timestamp)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_2","uri":"capability://memory.knowledge.multi.tenant.vector.storage.with.qdrant.and.postgresql.dual.write","name":"multi-tenant vector storage with qdrant and postgresql dual-write","description":"Airweave uses a Qdrant Multi-Tenant Architecture where each organization's vectors are isolated in separate Qdrant collections, with metadata stored in PostgreSQL. The QdrantDestination API implements a write path that batches entity embeddings and writes them to Qdrant with error handling and retry logic. PostgreSQL stores the relational schema (collections, source connections, sync metadata) and serves as the source of truth for entity relationships and breadcrumbs. The dual-write pattern ensures consistency: vectors in Qdrant are indexed for search, while PostgreSQL maintains referential integrity and enables complex queries (e.g., 'find all entities from source X synced after timestamp Y').","intents":["Store embeddings for millions of entities across multiple organizations with isolation guarantees","Query entity metadata and relationships via SQL while searching vectors in Qdrant","Maintain consistency between vector store and relational metadata during sync operations","Scale vector storage without managing separate vector DB infrastructure per tenant"],"best_for":["Multi-tenant SaaS platforms building RAG features for customers","Enterprise deployments requiring strict data isolation between organizations","Teams needing both vector search and complex relational queries on entity metadata"],"limitations":["Dual-write pattern introduces consistency risk — Qdrant and PostgreSQL can diverge if writes fail partially; requires application-level reconciliation","Qdrant multi-tenancy via collection isolation doesn't provide hard security boundaries; relies on application-level access control","PostgreSQL becomes bottleneck for high-frequency metadata queries; requires careful indexing and query optimization","Vector deletion/update requires coordinated writes to both stores; no transactional guarantee across systems"],"requires":["PostgreSQL 12+ with appropriate indexes on collections, source_connections, entities tables","Qdrant cluster (self-hosted or managed) with sufficient disk for vector storage","Application-level transaction handling to manage dual-write consistency","Entity embeddings pre-computed before write (external embedding model)"],"input_types":["Entity objects with embeddings (vector + metadata)","Collection ID for tenant isolation","Batch size for write optimization"],"output_types":["Qdrant point IDs for vector references","PostgreSQL entity records with foreign keys to collections and sources","Write operation status (success, partial failure, retry state)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_3","uri":"capability://tool.use.integration.mcp.server.integration.for.agent.native.search.tool.exposure","name":"mcp server integration for agent-native search tool exposure","description":"Airweave exposes search capabilities as a Model Context Protocol (MCP) server, allowing Claude and other MCP-compatible agents to invoke search as a native tool. The MCP Server Architecture defines a search tool schema that agents can call with natural language queries and filters. The MCP Search Tool handles query parsing, invokes the underlying Search System (Vespa-backed), and returns results in a format agents can reason about. This enables agents to autonomously search the knowledge base without explicit function-calling code — the agent sees search as a first-class capability in its tool registry.","intents":["Enable Claude and other MCP agents to search enterprise knowledge bases autonomously","Expose search as a native tool in agent tool registries without custom function-calling wrappers","Allow agents to iteratively search and refine queries based on intermediate results","Integrate Airweave search into existing MCP-based agent workflows"],"best_for":["Teams building Claude agents that need access to enterprise knowledge bases","Developers using MCP-compatible LLMs (Claude, open-source models with MCP support)","Organizations standardizing on MCP for agent tool integration"],"limitations":["MCP server requires separate deployment and management; adds operational overhead","Tool schema must be pre-defined; agents cannot dynamically discover filter options (e.g., available sources)","MCP protocol adds network latency per tool call (typically 50-200ms) vs. in-process function calls","Limited to MCP-compatible models; OpenAI function calling requires separate integration"],"requires":["MCP-compatible LLM (Claude 3+, or open-source models with MCP support)","MCP server running and accessible to the LLM","Airweave API credentials configured in MCP server","Search System (Vespa) operational and indexed"],"input_types":["MCP tool call with query string and optional filters","Agent-generated natural language query"],"output_types":["Ranked search results formatted for agent reasoning","Metadata breadcrumbs for result context","Tool call response in MCP format"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_4","uri":"capability://tool.use.integration.embeddable.connect.widget.for.oauth.based.source.connection.ui","name":"embeddable connect widget for oauth-based source connection ui","description":"Airweave provides a Connect Widget — an embeddable React component that handles the full OAuth flow for connecting sources. The Connect Widget Architecture manages OAuth Callback Flow internally: it initiates OAuth with the source platform, handles the redirect callback, exchanges the authorization code for tokens, and stores credentials securely. The Connect Client SDKs (JavaScript/TypeScript) expose a simple API for embedding the widget in external applications. Connect Session Management tracks widget state (pending, authenticated, error) and enables parent applications to listen for connection events. This eliminates the need for applications to implement OAuth flows themselves.","intents":["Embed a pre-built source connection UI in external applications without building OAuth flows","Handle OAuth token exchange and secure credential storage transparently","Provide users with a familiar connection experience across multiple SaaS sources","Track connection status and errors from the parent application"],"best_for":["SaaS platforms building white-label RAG features for customers","Teams embedding Airweave into existing applications without OAuth expertise","Applications needing to support multiple source connections with minimal UI code"],"limitations":["Widget is React-only; no Vue, Angular, or vanilla JS support","OAuth callback requires network access to Airweave backend; no offline mode","Widget styling is limited to theme customization; deep UI customization requires forking","Session state is ephemeral; parent application must handle persistence of connection status"],"requires":["React 16.8+ (hooks support)","Airweave API credentials (client_id, client_secret)","Airweave backend accessible from browser","OAuth credentials configured for each source platform"],"input_types":["Widget configuration (source type, styling, callback handlers)","User context (organization_id, user_id for audit)"],"output_types":["Connection event callbacks (onSuccess, onError, onCancel)","Source connection object with credentials stored server-side","Session state for UI updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_5","uri":"capability://memory.knowledge.collection.based.knowledge.base.organization.with.hierarchical.entity.breadcrumbs","name":"collection-based knowledge base organization with hierarchical entity breadcrumbs","description":"Airweave organizes indexed entities into Collections, which are logical groupings of related data (e.g., 'Q1 2024 Research', 'Customer Support Docs'). Collections can contain entities from multiple sources, and each entity maintains breadcrumb metadata (source, document_id, parent_id) that preserves document hierarchy. The Collections API enables CRUD operations on collections and supports filtering search results by collection. Breadcrumbs enable hierarchical queries (e.g., 'find all entities under parent document X') and preserve context for agents (e.g., 'this result came from a Linear ticket in the Q1 Planning project'). This enables agents to reason about result provenance and scope searches to relevant document trees.","intents":["Organize multi-source data into logical knowledge bases (collections) for different use cases","Preserve document hierarchy (parent-child relationships) across different source formats","Filter search results by collection or document hierarchy","Provide agents with rich context about result provenance and relationships"],"best_for":["Organizations with multiple knowledge bases (e.g., per-team, per-project, per-customer)","RAG systems requiring hierarchical document organization","Agents that need to reason about document relationships and provenance"],"limitations":["Breadcrumb metadata is source-specific; mapping hierarchies across different source formats (Google Docs folder structure vs. Linear project hierarchy) requires custom logic","Collection membership is static; entities cannot belong to multiple collections (no cross-collection queries)","Breadcrumb depth is limited by source API capabilities; some sources don't expose full hierarchy"],"requires":["Collection created via Collections API before syncing sources","Source entities must include parent_id and document_id metadata","Search filters must reference collection_id to scope results"],"input_types":["Collection name and description","Source connections to include in collection","Optional metadata tags for organization"],"output_types":["Collection object with ID and metadata","Entities with breadcrumb metadata (source, document_id, parent_id)","Filtered search results scoped to collection"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_6","uri":"capability://automation.workflow.source.connection.lifecycle.management.with.oauth.token.refresh.and.error.resilience","name":"source connection lifecycle management with oauth token refresh and error resilience","description":"Airweave manages the full lifecycle of source connections: OAuth authentication, token storage in PostgreSQL, automatic token refresh before expiry, and error handling with retry logic. The Source Connection Lifecycle pattern tracks connection state (authenticated, expired, error) and implements Token Management and Refresh that automatically refreshes OAuth tokens before they expire, preventing sync failures. The Factory Pattern and Context Building construct source-specific clients with refreshed credentials at sync time. Error Handling and Resilience implements exponential backoff and dead-letter queues for failed syncs, enabling operators to retry failed connections without manual intervention.","intents":["Manage OAuth credentials for multiple sources without manual token refresh","Automatically refresh tokens before expiry to prevent sync interruptions","Handle authentication errors gracefully with retry logic and operator visibility","Track connection health and alert on credential expiry or authentication failures"],"best_for":["Multi-source systems requiring hands-off credential management","Production deployments where sync reliability is critical","Teams without dedicated DevOps resources to manually refresh tokens"],"limitations":["Token refresh requires secure storage in PostgreSQL; self-hosted deployments must manage encryption at rest","Refresh token rotation (some OAuth providers invalidate old tokens after refresh) requires careful handling; some sources may require re-authentication","Error resilience adds complexity; requires monitoring and alerting infrastructure to detect persistent failures","Exponential backoff can delay sync recovery; maximum retry delay may be too long for time-sensitive data"],"requires":["PostgreSQL with encrypted credential storage","OAuth refresh tokens for each source (not all sources support refresh tokens)","Temporal Workflow System for retry orchestration","Monitoring/alerting infrastructure to detect persistent failures"],"input_types":["OAuth credentials (access_token, refresh_token, expires_at)","Source connection configuration"],"output_types":["Refreshed OAuth tokens stored in PostgreSQL","Connection state (authenticated, expired, error)","Retry metadata (attempt_count, next_retry_time, error_message)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_7","uri":"capability://automation.workflow.temporal.workflow.based.sync.orchestration.with.schedule.management.and.progress.tracking","name":"temporal workflow-based sync orchestration with schedule management and progress tracking","description":"Airweave uses Temporal Workflows to orchestrate data syncs as reliable, resumable jobs. The Temporal Worker Architecture runs activities (source-specific sync logic) within workflow contexts that handle retries, timeouts, and state persistence. Workflows define sync schedules (one-time, recurring via cron, continuous polling) and manage the full sync lifecycle: entity fetching, processing, and writing to storage. The Sync Orchestration layer coordinates multiple sources syncing in parallel while respecting rate limits and backpressure. Progress Tracking and Metrics capture sync progress (entities_processed, errors, duration) and enable operators to monitor sync health via dashboards. Schedule Management allows dynamic schedule updates without restarting workers.","intents":["Run reliable, resumable data syncs that survive worker failures and network interruptions","Schedule syncs on flexible cadences (one-time, hourly, daily, custom cron)","Monitor sync progress and errors in real-time with visibility into entity processing","Parallelize syncs across multiple sources while respecting rate limits"],"best_for":["Production deployments requiring reliable, resumable sync jobs","Teams needing flexible sync scheduling (not just fixed intervals)","Large-scale syncs where progress tracking and error visibility are critical"],"limitations":["Temporal Workflow System adds operational complexity; requires Temporal server deployment and management","Workflow state is persisted in Temporal; debugging workflow issues requires Temporal UI and logs","Schedule updates require workflow version management; changing schedules may require workflow redeployment","Temporal pricing (if using Temporal Cloud) can be significant for high-frequency syncs"],"requires":["Temporal server (self-hosted or Temporal Cloud)","Temporal Python SDK","Worker processes running Temporal activities","Sync schedule definitions (cron expressions or one-time triggers)"],"input_types":["Sync schedule (cron expression, one-time timestamp, or continuous)","Source connection ID and configuration","Sync parameters (full vs. incremental, batch size)"],"output_types":["Workflow execution ID for tracking","Progress metrics (entities_processed, errors, duration)","Sync completion status (success, partial failure, timeout)","Error logs with retry state"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_8","uri":"capability://data.processing.analysis.entity.processing.pipeline.with.stream.based.queue.management.and.concurrency.control","name":"entity processing pipeline with stream-based queue management and concurrency control","description":"The Entity Processing Pipeline implements stream-based processing of entities from sources through a queue with backpressure handling. Entities are streamed from source connectors into an in-memory queue, processed in batches (normalization, embedding generation), and written to storage. The Source Stream and Queue Management layer implements backpressure: if the queue fills up, source fetching pauses until downstream processing catches up. Concurrency and Backpressure controls limit parallel processing to prevent overwhelming source APIs or downstream services (embedding models, vector stores). This enables high-throughput syncs without resource exhaustion or API throttling.","intents":["Process millions of entities from sources without overwhelming downstream services","Implement backpressure to prevent queue overflow and memory exhaustion","Batch entities for efficient embedding generation and vector store writes","Monitor processing throughput and identify bottlenecks in the pipeline"],"best_for":["Large-scale syncs (millions of entities) requiring efficient resource utilization","Systems with limited downstream capacity (rate-limited embedding APIs, small vector stores)","Teams needing visibility into processing throughput and bottlenecks"],"limitations":["Queue is in-memory; worker failure loses queued entities (requires re-sync from source)","Backpressure is local to worker; distributed workers don't coordinate queue depth","Batch size tuning is manual; no adaptive batching based on downstream latency","Concurrency limits are static; no dynamic adjustment based on source API rate limits"],"requires":["Source connector implementing streaming interface","Downstream services (embedding model, vector store) with known throughput capacity","Worker process with sufficient memory for queue"],"input_types":["Entity stream from source connector","Batch size and concurrency parameters","Backpressure threshold (queue depth limit)"],"output_types":["Batched entities ready for embedding/storage","Processing metrics (throughput, queue depth, latency)","Backpressure signals to source connector"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-airweave-ai--airweave__cap_9","uri":"capability://tool.use.integration.rest.api.with.openapi.schema.for.programmatic.collection.source.and.search.management","name":"rest api with openapi schema for programmatic collection, source, and search management","description":"Airweave exposes a comprehensive REST API (documented via OpenAPI/Fern) for programmatic management of collections, sources, and search. The Collections API enables CRUD operations on collections and membership. The Source Connections API manages OAuth connections and sync state. The Sources API lists available source types and their configuration schemas. The Search API accepts queries and returns ranked results. The API uses standard REST conventions (GET, POST, PUT, DELETE) and returns JSON responses. Authentication is via API keys stored in PostgreSQL. The API enables external applications to integrate Airweave without using the web UI or SDKs.","intents":["Programmatically create and manage collections and source connections","Trigger syncs and monitor sync progress via API","Execute searches and retrieve results from external applications","Integrate Airweave into existing workflows and automation tools"],"best_for":["Teams building custom integrations with Airweave","Automation tools and scripts that need to manage collections and syncs","External applications embedding Airweave search without using SDKs"],"limitations":["API is synchronous; long-running operations (large syncs) may timeout","Rate limiting is not explicitly documented; high-frequency API calls may be throttled","Pagination is not standardized across endpoints; some endpoints may not support offset/limit","Error responses vary by endpoint; no consistent error schema"],"requires":["API key (generated via dashboard or admin API)","HTTP client (curl, requests, axios, etc.)","Knowledge of OpenAPI schema for endpoint discovery"],"input_types":["JSON request bodies for POST/PUT operations","Query parameters for filtering and pagination","API key in Authorization header"],"output_types":["JSON responses with collection, source, or search result objects","HTTP status codes (200, 201, 400, 401, 404, 500)","Error messages with error codes"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PostgreSQL database for source connection state and credentials","Temporal server for workflow orchestration (can use Temporal Cloud or self-hosted)","OAuth credentials for each source platform being connected","Network access to source APIs (no local-only mode)","Vespa cluster (self-hosted or managed)","Embedding model API (OpenAI, Anthropic, or local model)","Indexed entities in Qdrant or Vespa vector store","Query text (natural language or structured filters)","Web browser with JavaScript enabled"],"failure_modes":["Connector coverage limited to pre-built integrations (Google Docs, Linear, Intercom, Trello, ClickUp, OneNote, Word, Google Slides) — custom sources require extending the Source Connector Architecture","Incremental sync relies on source API cursor support — sources without cursor pagination fall back to full sync","Temporal Workflow System adds operational complexity; requires Temporal server deployment for production scheduling","OAuth token refresh requires secure storage in PostgreSQL; self-hosted deployments must manage credential encryption","Vespa integration requires separate Vespa cluster deployment and maintenance; no embedded vector DB option","Agentic search adds latency per iteration (typically 100-300ms per refinement cycle)","Embedding generation is external dependency — requires OpenAI, Anthropic, or other embedding model","Ranking relevance depends on embedding model quality; poor embeddings degrade search quality regardless of Vespa tuning","Dashboard is web-only; no mobile or desktop app","Real-time updates via SSE require persistent connection; may not work behind some proxies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.619989906215541,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2026-05-01T10:40:01Z"},"community":{"stars":6277,"forks":784,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=airweave-ai--airweave","compare_url":"https://unfragile.ai/compare?artifact=airweave-ai--airweave"}},"signature":"/GTr/RjKyc3esNodnQ4LemVKWRLBIu1xiZ5UVn9rZfB7xHf0Z2HPCQToC+eHA7degILDZUxRkiAYLfjKfow8Cg==","signedAt":"2026-06-21T21:36:16.333Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/airweave-ai--airweave","artifact":"https://unfragile.ai/airweave-ai--airweave","verify":"https://unfragile.ai/api/v1/verify?slug=airweave-ai--airweave","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"}}