{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-video-db--director","slug":"video-db--director","name":"Director","type":"agent","url":"https://chat.videodb.io","page_url":"https://unfragile.ai/video-db--director","categories":["video-generation"],"tags":["agent","agent-framework","ai-agents","framework","llm","openai","rag","search","text-to-video","video-editing","video-processing","videodb"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-video-db--director__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.for.video.workflows","name":"multi-agent orchestration for video workflows","description":"Coordinates 25+ specialized agents (VideoGenerationAgent, TextToVideoAgent, AudioAgent, SearchAgent, etc.) through a reasoning engine that interprets natural language commands and routes them to appropriate agents based on task decomposition. Each agent inherits from BaseAgent, defines JSON schemas for inputs, implements business logic via run() methods, and communicates status through OutputMessage objects and WebSocket emissions. The reasoning engine (backend/director/core/reasoning.py) handles agent selection, parameter binding, and execution sequencing.","intents":["I want to issue a single natural language command like 'upload this YouTube video and create highlights' and have the system automatically break it into sub-tasks and execute them in sequence","I need to build complex video workflows that combine generation, editing, search, and audio processing without manually orchestrating each step","I want to extend the framework with custom agents that integrate seamlessly with the existing orchestration system"],"best_for":["teams building video AI applications that require multi-step workflows","developers creating custom video processing agents that need to integrate with a larger ecosystem","builders prototyping complex video generation pipelines from natural language specifications"],"limitations":["Agent orchestration adds latency per reasoning step as the LLM must decompose and route tasks sequentially","No built-in transaction semantics — partial failures in multi-agent workflows require manual rollback logic","Agent communication is synchronous; no native support for parallel agent execution or async task queuing"],"requires":["Python 3.9+","Flask backend running (backend/director/entrypoint/api/server.py)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","VideoDB infrastructure for video storage and indexing"],"input_types":["natural language text commands","JSON agent parameter schemas","video file references (URLs, file paths, VideoDB IDs)"],"output_types":["structured agent execution logs","video artifacts (generated, edited, or processed)","JSON status messages via WebSocket","OutputMessage objects with progress metadata"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_1","uri":"capability://image.visual.natural.language.to.video.generation.with.multi.provider.support","name":"natural language to video generation with multi-provider support","description":"Translates natural language prompts into video generation requests by routing to 18+ integrated AI services (OpenAI, Anthropic, StabilityAI, ElevenLabs, etc.) through a unified tool interface. The VideoGenerationAgent and TextToVideoAgent classes implement provider-specific logic while abstracting differences via a common parameter schema. Requests flow through backend/director/tools/ai_service_tools.py which handles API calls, response parsing, and error handling. Generated videos are automatically stored in VideoDB infrastructure for indexing and retrieval.","intents":["I want to generate videos from text descriptions without learning multiple provider APIs or managing credentials separately","I need to compare outputs across different video generation models (OpenAI, StabilityAI, etc.) by issuing the same prompt to multiple providers","I want generated videos to be automatically indexed and searchable within my video collection"],"best_for":["content creators building video generation workflows without deep ML expertise","teams evaluating different video generation models for quality and cost","applications requiring multi-model fallback (use StabilityAI if OpenAI quota exhausted)"],"limitations":["Provider-specific limitations cascade to the framework (e.g., OpenAI's video generation has length/resolution caps)","No built-in prompt optimization or model-specific tuning — prompts are passed through with minimal transformation","Video generation is asynchronous but framework lacks native polling/webhook support for long-running jobs","Cost tracking and rate limiting are provider-specific; no unified quota management across providers"],"requires":["Python 3.9+","API keys for at least one video generation provider (OpenAI, StabilityAI, etc.)","VideoDB account and API credentials for storage","Backend Flask server running with configured provider credentials in environment"],"input_types":["natural language text prompts","structured generation parameters (duration, resolution, style, etc.)","reference images or video clips for style guidance"],"output_types":["video files (MP4, WebM, etc.)","VideoDB video IDs for indexed retrieval","generation metadata (model used, duration, resolution, cost)"],"categories":["image-visual","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_10","uri":"capability://data.processing.analysis.video.collection.management.and.organization","name":"video collection management and organization","description":"Provides organizational primitives for managing video collections through VideoDB's collection system. Users can create collections, organize videos by tags/metadata, and perform bulk operations (search, edit, delete) across collections. Collections are persisted in VideoDB and accessible via the API. Supports hierarchical organization (nested collections) and sharing/permission controls.","intents":["I want to organize my videos into logical groups (by project, date, topic) for easier management","I need to perform bulk operations on collections (search all videos in a project, apply effects to all clips)","I want to share specific collections with team members while keeping others private"],"best_for":["media teams managing large video libraries with multiple projects","content creators organizing videos by series, season, or topic","collaborative environments where different team members work on different collections"],"limitations":["Collection operations are not atomic; partial failures in bulk operations may leave collections in inconsistent state","No built-in versioning; overwriting collection metadata is permanent","Permission controls are basic (owner/viewer); no fine-grained role-based access control","Collection queries are limited to VideoDB's native filtering; no custom query language"],"requires":["VideoDB infrastructure with collection support","User authentication for permission checks"],"input_types":["collection names and metadata","video IDs to add/remove from collections","permission specifications (owner, viewer, editor)"],"output_types":["collection objects with metadata","list of videos in collection","permission status"],"categories":["data-processing-analysis","memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_11","uri":"capability://safety.moderation.error.handling.and.graceful.degradation.across.agent.failures","name":"error handling and graceful degradation across agent failures","description":"Implements error handling at multiple levels: agent-level try-catch blocks, provider fallback logic, and user-facing error messages. When an agent fails, the system attempts fallback strategies (e.g., use alternative provider, retry with different parameters) before surfacing errors to the user. Error context (stack traces, provider responses, input parameters) is logged for debugging. Partial failures in multi-agent workflows are handled gracefully, allowing subsequent agents to proceed with available data.","intents":["I want the system to gracefully handle provider failures (API down, quota exceeded) without crashing the entire workflow","I need detailed error messages that help me understand what went wrong and how to fix it","I want the system to retry failed operations automatically with exponential backoff"],"best_for":["production systems where reliability is critical","applications using multiple external providers that may fail independently","teams needing detailed error logs for debugging and monitoring"],"limitations":["Fallback strategies are provider-specific; not all failures have automatic recovery paths","Retry logic may mask transient issues; excessive retries can waste API quota","Error messages are only as good as provider error responses; some providers give vague errors","Partial failures in multi-agent workflows may produce incorrect results if downstream agents don't validate input"],"requires":["Fallback provider credentials (if using multi-provider fallback)","Logging infrastructure for error tracking","Monitoring/alerting system to detect systematic failures"],"input_types":["agent execution results (success or failure)","provider error responses","user input and context"],"output_types":["user-facing error messages","detailed error logs with stack traces","retry status and fallback provider used"],"categories":["safety-moderation","automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_12","uri":"capability://tool.use.integration.extensible.agent.framework.for.custom.video.processing.tasks","name":"extensible agent framework for custom video processing tasks","description":"Provides a plugin architecture for developers to create custom agents by extending BaseAgent (backend/director/agents/base.py). Custom agents define JSON parameter schemas, implement run() methods, and integrate with the existing tool ecosystem. The framework handles parameter validation, execution lifecycle, status communication, and WebSocket streaming. Documentation and examples guide developers through agent creation, testing, and deployment.","intents":["I want to build custom agents for domain-specific video processing tasks (e.g., sports highlight detection, product placement analysis)","I need to integrate proprietary video processing algorithms into the Director framework","I want to extend the framework with new capabilities without forking the codebase"],"best_for":["developers building specialized video processing applications","teams with proprietary video analysis algorithms wanting to integrate with Director","researchers prototyping new video AI capabilities"],"limitations":["Custom agents must follow BaseAgent interface; significant architectural changes require framework modifications","No built-in testing framework; developers must write their own unit tests","Documentation for agent development is minimal; learning curve is steep","Custom agents don't automatically benefit from framework optimizations (caching, parallelization)"],"requires":["Python 3.9+","Understanding of BaseAgent interface and execution lifecycle","Knowledge of JSON schema for parameter definition","Access to Director codebase for testing and integration"],"input_types":["agent parameter schemas (JSON)","video references (VideoDB IDs or file paths)","configuration and credentials for external services"],"output_types":["agent execution results (video, metadata, analysis)","status messages and progress updates","error logs and debugging information"],"categories":["tool-use-integration","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_13","uri":"capability://automation.workflow.batch.processing.and.asynchronous.job.execution","name":"batch processing and asynchronous job execution","description":"Supports asynchronous execution of long-running tasks (video generation, transcription, editing) through a job queue system. Jobs are submitted with parameters, assigned unique IDs, and processed asynchronously by backend workers. Users can poll job status or subscribe to WebSocket updates. Completed jobs are stored with results and metadata. Supports job cancellation, retry on failure, and priority queuing.","intents":["I want to submit a batch of videos for processing (e.g., generate highlights for 100 videos) without blocking on each one","I need to check the status of long-running jobs and retrieve results when ready","I want to cancel or reprioritize jobs in the queue"],"best_for":["applications processing large volumes of videos","workflows where users don't need immediate results","systems with limited concurrent processing capacity"],"limitations":["Job queue adds latency; immediate results are not possible for long-running tasks","No built-in job persistence across server restarts; jobs in progress may be lost","Scaling to many concurrent jobs requires distributed job queue (Celery, RQ); built-in implementation may be single-threaded","Job status polling adds API overhead; WebSocket subscriptions are more efficient but require persistent connections"],"requires":["Job queue backend (in-memory, Redis, Celery, etc.)","Worker processes to execute jobs asynchronously","Job storage (database or cache) for status and results"],"input_types":["job parameters (video IDs, processing options)","priority level (optional)","job ID for status queries"],"output_types":["job ID for tracking","job status (queued, processing, completed, failed)","job results and metadata","WebSocket updates on status changes"],"categories":["automation-workflow","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_2","uri":"capability://search.retrieval.semantic.video.search.and.retrieval.with.natural.language.queries","name":"semantic video search and retrieval with natural language queries","description":"Enables searching video collections using natural language by leveraging VideoDB's native indexing and semantic understanding. The SearchAgent (backend/director/agents/) accepts natural language queries, translates them into VideoDB search parameters, and returns ranked results with relevance scores. Internally uses embeddings-based retrieval (memory-knowledge layer) combined with metadata filtering. Results are streamed back to the frontend via WebSocket with progressive refinement as more results are indexed.","intents":["I want to find specific moments in a video collection by describing what I'm looking for in plain English ('find the scene where the CEO talks about growth strategy')","I need to search across multiple videos simultaneously and get ranked results ordered by relevance","I want to combine semantic search with metadata filters (e.g., 'find clips from Q4 2024 that mention product launches')"],"best_for":["media teams managing large video libraries who need fast semantic search","content creators finding reference material or inspiration across collections","researchers analyzing video corpora for specific topics or patterns"],"limitations":["Search quality depends on VideoDB's indexing completeness — newly uploaded videos may not be immediately searchable","No full-text search on transcripts; semantic search only (requires transcription agent to run first)","Relevance ranking is VideoDB-native; no custom ranking functions or ML model swapping","Large result sets (1000+ videos) may have pagination/performance implications"],"requires":["VideoDB infrastructure with indexed video collection","Video transcripts or metadata for semantic indexing (generated by TranscriptionAgent if not present)","Backend session management (backend/director/core/session.py) to maintain search context"],"input_types":["natural language search queries","optional metadata filters (date range, tags, duration)","video collection IDs or scope parameters"],"output_types":["ranked list of video clips with timestamps","relevance scores (0-1 range)","metadata for each result (duration, source, transcript excerpt)","WebSocket stream of progressive results"],"categories":["search-retrieval","memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_3","uri":"capability://data.processing.analysis.automatic.speech.to.text.and.transcription.with.speaker.diarization","name":"automatic speech-to-text and transcription with speaker diarization","description":"Processes video audio to generate timestamped transcripts with speaker identification using the TranscriptionAgent (backend/director/agents/transcription.py). Internally routes to external speech-to-text providers (OpenAI Whisper, AssemblyAI, etc.) via the AI service tools layer. Transcripts are stored as metadata in VideoDB, enabling downstream search, dubbing, and content analysis. Supports multiple languages and automatic language detection.","intents":["I want to automatically generate searchable transcripts from video uploads without manual transcription","I need to identify who is speaking at each moment in a video for content analysis or editing","I want transcripts to be immediately available for semantic search and downstream processing"],"best_for":["content creators and media companies processing large volumes of video","accessibility teams generating captions and transcripts for compliance","researchers analyzing spoken content across video collections"],"limitations":["Transcription quality varies by provider and audio quality; background noise significantly degrades accuracy","Speaker diarization may fail with many speakers (5+) or overlapping speech","No real-time transcription — processing is asynchronous and can take minutes for long videos","Language support depends on provider; some providers don't support all languages equally"],"requires":["API credentials for transcription provider (OpenAI Whisper, AssemblyAI, etc.)","Video with audio track (mono or stereo)","Backend processing queue for asynchronous transcription jobs"],"input_types":["video files with audio","language preference (optional; auto-detected if not specified)","speaker diarization enabled/disabled flag"],"output_types":["timestamped transcript (JSON or SRT format)","speaker labels and turn boundaries","confidence scores per segment","metadata stored in VideoDB for indexing"],"categories":["data-processing-analysis","text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_4","uri":"capability://image.visual.multi.language.audio.dubbing.and.voice.synthesis","name":"multi-language audio dubbing and voice synthesis","description":"Generates dubbed audio in target languages by combining transcription, translation, and text-to-speech synthesis. The AudioAgent and DubbingAgent classes orchestrate this pipeline: extract transcript → translate to target language → synthesize speech via ElevenLabs or similar providers → replace original audio track. Maintains speaker voice characteristics and emotional tone through provider-specific voice cloning parameters. Dubbed videos are stored back in VideoDB with language metadata.","intents":["I want to create versions of my video in multiple languages without hiring voice actors","I need to preserve the original speaker's voice characteristics while dubbing into a new language","I want to generate dubbed videos that maintain lip-sync or at least audio-visual coherence"],"best_for":["content creators distributing videos globally across multiple language markets","educational platforms localizing course content","media companies expanding reach without proportional localization costs"],"limitations":["Lip-sync is not guaranteed — audio duration may differ from original, requiring video re-editing","Voice cloning quality depends on provider and original audio quality; synthetic voices may sound unnatural","Translation quality affects final output; idioms and cultural references may not translate well","No support for preserving background music/effects separately — entire audio track is replaced","Processing time scales with video length (minutes to hours for long-form content)"],"requires":["Transcription of original video (TranscriptionAgent must run first)","Translation API credentials (Google Translate, DeepL, etc.)","Text-to-speech provider credentials (ElevenLabs, Google Cloud TTS, etc.)","Original video file for audio replacement"],"input_types":["video with audio track","target language code (e.g., 'es', 'fr', 'ja')","voice preference (gender, accent, tone)","optional speaker voice sample for cloning"],"output_types":["dubbed video file with new audio track","VideoDB video ID with language metadata","translation and synthesis logs for quality review"],"categories":["image-visual","text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_5","uri":"capability://image.visual.video.editing.and.frame.level.manipulation.with.agent.control","name":"video editing and frame-level manipulation with agent control","description":"Enables programmatic video editing through the FrameAgent (backend/director/agents/frame.py) which accepts natural language editing commands and translates them into frame-level operations. Supports trimming, concatenation, overlay insertion, effect application, and frame extraction. Internally uses FFmpeg or similar video processing libraries for codec-agnostic manipulation. Edited videos are re-encoded and stored in VideoDB with edit history metadata.","intents":["I want to trim, concatenate, or extract clips from videos using natural language commands like 'remove the first 30 seconds and the last 10 seconds'","I need to programmatically insert overlays, text, or graphics at specific timestamps","I want to apply effects or filters to video segments without learning video editing software"],"best_for":["developers building automated video processing pipelines","content creators who want to script repetitive editing tasks","teams generating variations of videos (different cuts, lengths, overlays)"],"limitations":["Complex effects (color grading, advanced compositing) are limited compared to professional editing software","Re-encoding adds latency and quality loss; multiple edits compound degradation","No real-time preview — edits must be fully processed before review","Frame-accurate editing requires understanding video codec details; some formats may not support precise frame boundaries","Overlay positioning and sizing are specified in absolute coordinates, not responsive to video resolution changes"],"requires":["FFmpeg installed and accessible to backend process","Video file in supported codec (H.264, VP9, etc.)","Precise timestamp specifications or frame numbers for edits"],"input_types":["natural language editing commands","video file or VideoDB video ID","timestamps or frame ranges for edits","overlay files (images, videos, text) if applicable"],"output_types":["edited video file","VideoDB video ID with edit metadata","edit operation log with timestamps and parameters"],"categories":["image-visual","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_6","uri":"capability://memory.knowledge.session.based.context.management.and.multi.turn.conversations","name":"session-based context management and multi-turn conversations","description":"Maintains conversation state across multiple user interactions through the Session Management system (backend/director/core/session.py). Each session stores user context, previous agent outputs, video references, and conversation history. Sessions are persisted in a database (likely SQLite or PostgreSQL based on backend/requirements.txt) and retrieved on subsequent requests. WebSocket connections maintain real-time session updates, enabling progressive result streaming and live agent status updates.","intents":["I want to have multi-turn conversations where the system remembers previous commands and context (e.g., 'edit that video' refers to the video from the previous message)","I need to maintain state across multiple agent executions so that one agent's output becomes the next agent's input","I want to see real-time progress updates as agents execute long-running tasks"],"best_for":["chat-based interfaces where users expect conversational context","workflows requiring multi-step execution where each step depends on previous results","applications needing audit trails of user interactions and agent decisions"],"limitations":["Session storage adds database latency; large conversation histories may slow retrieval","No automatic session cleanup — old sessions accumulate unless explicitly pruned","Context window is limited by LLM token limits; very long conversations may lose early context","WebSocket connections are stateful; disconnections require reconnection and session re-sync"],"requires":["Database backend (SQLite, PostgreSQL, etc.) for session persistence","WebSocket server (Flask-SocketIO or similar) for real-time updates","Session ID generation and validation mechanism"],"input_types":["user messages (text)","session ID (from previous interaction or new session creation)","agent execution results and status updates"],"output_types":["session state object with conversation history","WebSocket messages with real-time updates","agent execution logs and results"],"categories":["memory-knowledge","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_7","uri":"capability://data.processing.analysis.video.upload.and.ingestion.with.automatic.metadata.extraction","name":"video upload and ingestion with automatic metadata extraction","description":"Handles video file uploads from multiple sources (local files, URLs, YouTube links) and automatically ingests them into VideoDB infrastructure. The upload pipeline extracts metadata (duration, resolution, codec, frame rate), generates thumbnails, and initiates transcription and indexing. Supports resumable uploads for large files and progress tracking via WebSocket. Uploaded videos are immediately available for search, editing, and downstream processing.","intents":["I want to upload videos from various sources (my computer, YouTube, cloud storage) without manual format conversion","I need automatic metadata extraction so videos are immediately searchable and analyzable","I want to track upload progress for large files and resume interrupted uploads"],"best_for":["content management systems ingesting videos from multiple sources","teams with large video libraries requiring bulk import","applications where video availability is time-critical"],"limitations":["Upload speed is limited by network bandwidth and VideoDB processing capacity","Large files (1GB+) may timeout or require chunked upload implementation","Metadata extraction is asynchronous; videos are not immediately fully indexed","YouTube/URL uploads require downloading to local storage first, adding latency and storage overhead","No deduplication — identical videos uploaded multiple times create separate entries"],"requires":["VideoDB API credentials and storage quota","Backend file handling (Flask file upload support)","Sufficient disk space for temporary file storage during upload","Video codec support (H.264, VP9, etc.)"],"input_types":["video files (MP4, WebM, MOV, etc.)","URLs to video files or YouTube links","optional metadata (title, description, tags)"],"output_types":["VideoDB video ID","extracted metadata (duration, resolution, codec, frame rate)","thumbnail image","upload progress events via WebSocket"],"categories":["data-processing-analysis","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_8","uri":"capability://tool.use.integration.llm.provider.abstraction.and.multi.model.support","name":"llm provider abstraction and multi-model support","description":"Abstracts LLM interactions across multiple providers (OpenAI, Anthropic, Ollama, etc.) through a unified interface in the LLM integration layer. Agents call LLM methods without knowing which provider is active, enabling easy switching or fallback. Configuration is centralized in environment variables or config files. Supports streaming responses for real-time output, token counting for cost estimation, and provider-specific features (function calling, vision, etc.).","intents":["I want to switch between LLM providers (OpenAI to Anthropic) without changing agent code","I need fallback behavior if one provider is unavailable or quota-exhausted","I want to use provider-specific features (OpenAI function calling, Claude vision) while maintaining agent portability"],"best_for":["teams evaluating different LLM providers for cost and quality","applications requiring high availability with multi-provider redundancy","developers building LLM-agnostic agent frameworks"],"limitations":["Provider differences (token limits, response formats, latency) are not fully abstracted; agents may need provider-specific tuning","Streaming responses are provider-specific; not all providers support identical streaming formats","Function calling schemas differ between providers; complex schemas may not work across all providers","Cost tracking requires provider-specific rate card integration; no unified cost model"],"requires":["API keys for at least one LLM provider","Configuration mechanism (environment variables or config file)","Backend LLM integration module (backend/director/core/llm.py or similar)"],"input_types":["natural language prompts","system messages and context","optional function/tool schemas for function calling"],"output_types":["LLM responses (text, structured JSON, function calls)","token usage metadata (input tokens, output tokens, cost)"],"categories":["tool-use-integration","text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-video-db--director__cap_9","uri":"capability://automation.workflow.websocket.based.real.time.agent.status.and.progress.streaming","name":"websocket-based real-time agent status and progress streaming","description":"Provides real-time visibility into agent execution through WebSocket connections that stream progress updates, intermediate results, and status changes. The Handler Architecture (backend/director/handler.py) manages WebSocket lifecycle and message routing. Agents emit OutputMessage objects containing status, progress percentage, and partial results. Frontend receives updates in real-time, enabling live progress bars, streaming results, and early cancellation of long-running tasks.","intents":["I want to see real-time progress as agents execute long-running tasks (video generation, transcription) instead of waiting for completion","I need to cancel or interrupt agent execution if I realize the task is incorrect or unnecessary","I want to stream partial results to the frontend as they become available, not wait for full completion"],"best_for":["interactive chat interfaces where users expect real-time feedback","long-running video processing tasks where progress visibility is critical","applications requiring cancellation or interruption of in-flight operations"],"limitations":["WebSocket connections are stateful and require persistent server resources; scaling to many concurrent users is challenging","Message ordering is not guaranteed in high-concurrency scenarios; frontend must handle out-of-order updates","No built-in reconnection logic; network interruptions require manual reconnection","Progress updates add overhead; very frequent updates (per-frame) may saturate bandwidth"],"requires":["WebSocket server (Flask-SocketIO or similar)","Frontend WebSocket client library","Session management to route messages to correct client"],"input_types":["agent execution events (start, progress, complete, error)","intermediate results from agents","user cancellation requests"],"output_types":["WebSocket messages with status and progress","partial results (e.g., first 10 search results before full result set)","error messages and stack traces"],"categories":["automation-workflow","planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Flask backend running (backend/director/entrypoint/api/server.py)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","VideoDB infrastructure for video storage and indexing","API keys for at least one video generation provider (OpenAI, StabilityAI, etc.)","VideoDB account and API credentials for storage","Backend Flask server running with configured provider credentials in environment","VideoDB infrastructure with collection support","User authentication for permission checks","Fallback provider credentials (if using multi-provider fallback)"],"failure_modes":["Agent orchestration adds latency per reasoning step as the LLM must decompose and route tasks sequentially","No built-in transaction semantics — partial failures in multi-agent workflows require manual rollback logic","Agent communication is synchronous; no native support for parallel agent execution or async task queuing","Provider-specific limitations cascade to the framework (e.g., OpenAI's video generation has length/resolution caps)","No built-in prompt optimization or model-specific tuning — prompts are passed through with minimal transformation","Video generation is asynchronous but framework lacks native polling/webhook support for long-running jobs","Cost tracking and rate limiting are provider-specific; no unified quota management across providers","Collection operations are not atomic; partial failures in bulk operations may leave collections in inconsistent state","No built-in versioning; overwriting collection metadata is permanent","Permission controls are basic (owner/viewer); no fine-grained role-based access control","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.47179744531426,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:59:47.981Z","last_commit":"2026-01-23T15:34:16Z"},"community":{"stars":1371,"forks":225,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=video-db--director","compare_url":"https://unfragile.ai/compare?artifact=video-db--director"}},"signature":"1/cb9h4vNqIjs3qTHkD7bz+T3WxzlyG5t7ZheXXkQBfDrpE0hntJpgtqvMGpSyHLJrOHOAWoDxmK5XdzXFZ1Cg==","signedAt":"2026-06-20T21:31:20.953Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/video-db--director","artifact":"https://unfragile.ai/video-db--director","verify":"https://unfragile.ai/api/v1/verify?slug=video-db--director","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"}}