{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openai-assistants","slug":"openai-assistants","name":"OpenAI Assistants","type":"api","url":"https://platform.openai.com/docs/assistants","page_url":"https://unfragile.ai/openai-assistants","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"openai-assistants__cap_0","uri":"capability://memory.knowledge.persistent.multi.turn.conversation.threading.with.server.side.state","name":"persistent multi-turn conversation threading with server-side state","description":"Manages conversation history as immutable thread objects stored server-side, where each message appends to a thread rather than requiring clients to maintain conversation state. Threads persist across API calls and sessions, enabling stateless client implementations. The architecture decouples conversation management from model invocation, allowing assistants to be reused across multiple independent threads without state collision.","intents":["build a chatbot that remembers conversation history without managing state locally","create multiple independent conversations with the same assistant without context leakage","enable conversation resumption after client disconnection or session timeout","audit and replay conversation history for compliance or debugging"],"best_for":["teams building multi-user chat applications with audit requirements","developers avoiding local session management complexity","applications requiring conversation persistence across deployments"],"limitations":["thread state is immutable — message editing/deletion not supported, only append-only operations","no built-in conversation branching or forking — each thread is linear","thread storage counts toward API usage costs; long-running threads accumulate expense","no client-side caching of thread state — every message retrieval requires API call"],"requires":["OpenAI API key with Assistants API access","thread_id parameter for resuming conversations","network connectivity for all state queries"],"input_types":["text messages","file attachments (PDFs, images, code files)"],"output_types":["text responses","message metadata (role, timestamp, file references)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_1","uri":"capability://code.generation.editing.code.execution.sandbox.with.python.interpreter","name":"code execution sandbox with python interpreter","description":"Provides a managed Python 3.11 execution environment accessible via the Code Interpreter tool, where assistants can write and execute arbitrary Python code with access to common libraries (pandas, numpy, matplotlib, scikit-learn). Code runs in isolated sandboxes with file I/O, plotting, and data visualization capabilities. Execution results (stdout, stderr, generated files) are returned to the assistant for further processing.","intents":["enable assistants to perform data analysis and transformation without external compute","generate charts and visualizations from data within the conversation","execute mathematical computations and verify results in real-time","process and transform files (CSV, JSON, images) as part of agent workflows"],"best_for":["data science and analytics use cases where assistants need computational capability","educational applications teaching programming or data analysis","agents that need to validate or test code before returning it to users"],"limitations":["execution timeout of ~30 seconds per code block; long-running computations fail","no persistent state between code blocks — each execution is isolated; variables don't carry over","limited library ecosystem — only pre-installed packages available; pip install not supported","no GPU access or heavy ML training; suitable only for inference and lightweight analysis","file storage is ephemeral; generated files must be downloaded within the session"],"requires":["OpenAI API key with Code Interpreter tool enabled","assistant configured with tools=[{\"type\": \"code_interpreter\"}]","Python 3.11 compatible code"],"input_types":["Python code (as text or generated by assistant)","file uploads (CSV, JSON, images, PDFs)","numeric or text data"],"output_types":["execution results (stdout/stderr)","generated files (PNG, PDF, CSV)","matplotlib figures","structured data (JSON, tables)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_10","uri":"capability://tool.use.integration.tool.call.result.submission.and.iterative.refinement","name":"tool call result submission and iterative refinement","description":"When an assistant calls a tool, the run enters a 'requires_action' state. Clients must submit tool call results via the submit_tool_outputs API, which resumes the run with the tool results injected into context. This enables iterative workflows where assistants can call tools, receive results, and refine responses based on results. Tool results are stored in the thread and visible to subsequent runs, enabling multi-turn tool-assisted reasoning.","intents":["implement tool-use loops where assistants call tools, receive results, and refine responses","integrate external APIs and services into assistant workflows with result feedback","enable assistants to validate tool results and retry if needed","build complex workflows combining multiple tool calls with conditional logic"],"best_for":["agents requiring external tool integration (APIs, databases, services)","workflows with conditional branching based on tool results","applications where tool results must be processed before returning to user"],"limitations":["tool result submission is synchronous — clients must wait for submission before run resumes","no automatic retry logic — clients must implement retry and error handling","tool results are stored as-is; no transformation or filtering before injection into context","no timeout for waiting on tool results — runs can hang indefinitely if client doesn't submit results","tool call results are visible to users; sensitive data must be filtered by client"],"requires":["OpenAI API key","run in 'requires_action' state with tool_calls","tool_use_id for each tool call being submitted","output string for each tool result"],"input_types":["tool_use_id (from run.required_action.submit_tool_outputs)","output string (result of tool execution)"],"output_types":["updated run object with status","assistant response incorporating tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_11","uri":"capability://automation.workflow.assistant.cloning.and.template.based.creation","name":"assistant cloning and template-based creation","description":"Assistants can be created from scratch or cloned from existing assistants, copying all configuration (instructions, tools, model, file attachments). Cloning enables template-based assistant creation where a base assistant is configured once and then cloned for different use cases or users. Cloned assistants are independent — changes to one don't affect others. This reduces setup overhead for creating similar assistants.","intents":["create multiple specialized assistants from a base template without duplicating configuration","provision per-user or per-tenant assistants with consistent behavior","experiment with assistant variants by cloning and modifying specific properties","reduce setup time for common assistant patterns (e.g., customer support, data analysis)"],"best_for":["multi-tenant applications where each tenant gets a customized assistant","teams managing many similar assistants with slight variations","rapid prototyping where cloning is faster than manual configuration"],"limitations":["cloning copies configuration but not thread history — threads must be managed separately","no template versioning — cloned assistants are independent; updating the template doesn't affect clones","cloning is not atomic — if creation fails mid-clone, partial state may remain","no bulk cloning API — assistants must be cloned one at a time","cloned assistants incur separate storage costs; no deduplication of shared files"],"requires":["OpenAI API key","source assistant_id to clone from","optional overrides for specific properties (instructions, model, etc.)"],"input_types":["source assistant_id","optional property overrides (instructions, name, tools, etc.)"],"output_types":["new assistant object with cloned configuration","assistant_id for the cloned assistant"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_12","uri":"capability://memory.knowledge.file.management.and.lifecycle.with.automatic.cleanup","name":"file management and lifecycle with automatic cleanup","description":"Files uploaded to assistants are stored in OpenAI's managed file storage and associated with assistants or threads. Files can be deleted explicitly via API, and OpenAI automatically cleans up files after 30 days of inactivity. File storage is charged per file per assistant; deleting unused files reduces costs. Files can be reused across multiple assistants and threads, but each association incurs a separate storage charge.","intents":["upload and manage documents for assistants to analyze without managing external storage","delete sensitive files after use to reduce storage costs and privacy risk","reuse files across multiple assistants and threads without re-uploading","track file usage and clean up unused files to optimize costs"],"best_for":["applications with document-heavy workflows (legal, research, support)","teams without external file storage infrastructure","use cases where file lifecycle management is important for compliance"],"limitations":["file storage is charged per file per assistant; reusing files across assistants multiplies cost","no file versioning — overwriting a file reference updates all threads","automatic cleanup after 30 days is not configurable; no option for longer retention","no bulk file deletion API — files must be deleted one at a time","no file access controls or sharing — all files in an assistant are accessible to all threads","file storage quota is not exposed; no visibility into total storage usage"],"requires":["OpenAI API key with file management capability","file_id to delete files","supported file formats for upload"],"input_types":["file uploads (binary or text)","file_id for deletion"],"output_types":["file metadata (id, filename, size, created_at)","deletion confirmation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_2","uri":"capability://search.retrieval.semantic.file.search.with.vector.embeddings","name":"semantic file search with vector embeddings","description":"The File Search tool indexes uploaded files (PDFs, text, code) using OpenAI's embedding model and enables assistants to retrieve relevant passages via semantic search. Files are chunked, embedded, and stored in a managed vector index. When an assistant queries the index, it retrieves the most relevant chunks based on cosine similarity, then includes them in the prompt context. This enables RAG-style retrieval without managing embeddings or vector databases.","intents":["search large documents or knowledge bases for relevant information without loading entire files into context","build assistants that answer questions grounded in uploaded documents","enable multi-document search where assistants find relevant passages across many files","reduce token usage by retrieving only relevant chunks instead of full document context"],"best_for":["document-heavy applications (legal review, research, customer support)","teams without vector database infrastructure (Pinecone, Weaviate)","rapid prototyping of RAG systems without embedding pipeline setup"],"limitations":["no control over chunking strategy — OpenAI uses fixed chunk size; custom chunking not exposed","embedding model is fixed (text-embedding-3-small); cannot use alternative models or fine-tuned embeddings","search results are opaque — no visibility into chunk boundaries, similarity scores, or retrieval ranking","file indexing is asynchronous and can take minutes for large documents; no progress tracking","no bulk file operations — files must be uploaded individually; no batch indexing","pricing charges per file per assistant; storing same file across multiple assistants multiplies cost"],"requires":["OpenAI API key with File Search tool enabled","assistant configured with tools=[{\"type\": \"file_search\"}]","files uploaded via files API (max 512 MB per file)","supported file formats: PDF, TXT, DOCX, PPTX, XLSX, JSON, CSV, MD"],"input_types":["text files (TXT, MD, JSON, CSV)","documents (PDF, DOCX, PPTX, XLSX)","code files (PY, JS, TS, etc.)"],"output_types":["retrieved text passages","file references with citation metadata","ranked search results (implicit via context inclusion)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_3","uri":"capability://tool.use.integration.parallel.and.sequential.tool.execution.with.function.calling","name":"parallel and sequential tool execution with function calling","description":"Assistants can invoke multiple tools (Code Interpreter, File Search, custom functions) in parallel or sequence based on task requirements. Tool calls are defined via JSON schema (OpenAI function calling format), and the assistant decides which tools to invoke and in what order. Results from tool calls are fed back into the assistant's context, enabling iterative refinement. Supports both parallel execution (multiple tools called simultaneously) and sequential chaining (tool output feeds into next tool's input).","intents":["execute multiple independent operations concurrently (e.g., search files AND run code analysis simultaneously)","chain tool outputs together (e.g., search document, then analyze results with code interpreter)","integrate custom APIs and business logic into assistant workflows via function calling","enable assistants to autonomously decide which tools to use based on task context"],"best_for":["complex workflows requiring multiple tool invocations","teams integrating assistants with existing APIs and microservices","applications where tool selection and sequencing should be LLM-driven rather than hardcoded"],"limitations":["tool call latency is sequential in the API response loop — even parallel tool calls require round-trip to API","no explicit tool dependency graph or DAG support — assistant must infer sequencing from context","tool call failures are not automatically retried; assistant must handle and retry via context","no timeout or resource limits per tool call; long-running tools can block the entire execution","function schemas must be manually defined and kept in sync with actual function signatures","no built-in observability for tool call tracing; debugging requires logging at application level"],"requires":["OpenAI API key","assistant configured with tools array (built-in tools + custom functions)","custom functions defined as JSON schema matching OpenAI function calling spec","webhook or callback endpoint to handle function calls (if using custom functions)"],"input_types":["JSON schema function definitions","tool parameters (text, numbers, file references)","context from previous tool calls"],"output_types":["tool call results (text, structured data, files)","assistant response incorporating tool results","tool call metadata (execution time, status)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_4","uri":"capability://memory.knowledge.file.attachment.and.context.injection.into.conversations","name":"file attachment and context injection into conversations","description":"Assistants can receive file attachments (PDFs, images, code, data files) within messages, which are automatically indexed and made available for retrieval or analysis. Files are stored in OpenAI's managed file storage and can be referenced by subsequent messages in the thread. The assistant can analyze file content via Code Interpreter, search file content via File Search, or reference files in function calls. Files persist within a thread and are accessible across multiple turns.","intents":["upload documents or data files for assistants to analyze or answer questions about","provide images or screenshots for assistants to analyze or describe","share code files for assistants to review, debug, or refactor","enable multi-file workflows where assistants compare or correlate data across files"],"best_for":["document analysis and Q&A applications","code review and debugging workflows","data analysis where users upload CSVs or datasets","image analysis and OCR use cases"],"limitations":["file size limit of 512 MB per file; large datasets must be split or compressed","file storage is charged per file per assistant; deleting files requires explicit API call","no streaming file uploads — entire file must be uploaded before processing","file indexing is asynchronous; no progress tracking or ETA for completion","no file versioning or history — overwriting a file reference updates all threads","limited file format support — some formats (e.g., XLSX) have reduced functionality vs. CSV"],"requires":["OpenAI API key with file upload capability","file_ids parameter when creating messages with attachments","supported file formats: PDF, TXT, DOCX, PPTX, XLSX, JSON, CSV, MD, PNG, JPG, GIF, WEBP"],"input_types":["file uploads (binary or text)","file references (file_id from previous uploads)","file metadata (filename, MIME type)"],"output_types":["file analysis results (text, structured data)","file references in assistant responses","generated files (from Code Interpreter)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_5","uri":"capability://text.generation.language.streaming.response.generation.with.real.time.output","name":"streaming response generation with real-time output","description":"Assistants support server-sent events (SSE) streaming for real-time response generation, where message content is streamed token-by-token to the client as it's generated. Streaming includes events for message creation, content blocks, and tool calls, enabling clients to display responses incrementally. This reduces perceived latency and allows users to see assistant reasoning in real-time, including tool invocations and code execution.","intents":["display assistant responses incrementally to users without waiting for full completion","show real-time progress of tool execution (code running, files being searched)","reduce perceived latency in interactive chat applications","enable cancellation of long-running responses mid-generation"],"best_for":["interactive chat UIs where real-time feedback improves UX","applications with long-running tasks where users need progress visibility","mobile or low-bandwidth scenarios where incremental rendering reduces perceived delay"],"limitations":["streaming adds complexity to client implementation — requires SSE handling and event parsing","tool call results are not streamed; tool execution happens server-side before streaming resumes","no built-in cancellation API — client must close connection to stop streaming","streaming events are not persisted; clients must buffer events to reconstruct full response","pricing is the same as non-streaming; no cost savings for streaming responses"],"requires":["OpenAI API key with streaming support","client library or HTTP client supporting SSE (e.g., EventSource in JavaScript)","stream=true parameter in run creation request"],"input_types":["assistant instructions and context","user messages"],"output_types":["SSE events (message.created, content_block.delta, tool_calls.created, etc.)","streamed text tokens","tool call metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_6","uri":"capability://text.generation.language.instruction.based.assistant.customization.with.system.prompts","name":"instruction-based assistant customization with system prompts","description":"Assistants are configured with custom instructions (system prompts) that define behavior, tone, expertise, and constraints. Instructions are stored server-side and applied to all runs of the assistant without requiring clients to manage prompts. Instructions can be updated without creating new assistants, enabling behavior changes across all threads. Instructions are combined with user messages and tool results to form the complete context for each model invocation.","intents":["create specialized assistants with domain expertise (e.g., Python expert, legal advisor) without fine-tuning","enforce consistent behavior and tone across multiple conversations","update assistant behavior globally without redeploying client code","define guardrails and constraints (e.g., 'never provide medical advice') at the assistant level"],"best_for":["teams deploying multiple specialized assistants with different personas","applications requiring behavior changes without code deployment","use cases where instruction-based customization is sufficient (no fine-tuning needed)"],"limitations":["instructions are applied as system prompts; no guarantee of adherence (prompt injection risk)","no instruction versioning — updating instructions affects all future runs; no rollback","instruction length is limited (context window constraint); complex behaviors may not fit","no A/B testing framework — comparing instruction variants requires manual setup","instructions are visible to users if they request system prompt disclosure; no obfuscation"],"requires":["OpenAI API key","instructions parameter when creating assistant (max ~32k tokens)","model selection (gpt-4-turbo, gpt-3.5-turbo, etc.)"],"input_types":["text instructions (system prompt)","optional name and description for the assistant"],"output_types":["assistant metadata (id, instructions, model)","behavior changes reflected in assistant responses"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_7","uri":"capability://planning.reasoning.model.selection.and.version.management","name":"model selection and version management","description":"Assistants are bound to a specific model version (e.g., gpt-4-turbo, gpt-3.5-turbo) at creation time. The model can be updated via the API, enabling assistants to be upgraded to newer model versions without recreating threads or losing conversation history. Model selection affects capability, cost, and latency. OpenAI manages model versioning and deprecation; assistants can be migrated to new model versions as they become available.","intents":["choose the right model for cost vs. capability tradeoff (GPT-3.5 for simple tasks, GPT-4 for complex reasoning)","upgrade assistants to newer models as they're released without losing conversation history","manage model deprecation by migrating assistants before old models are retired","experiment with different models for the same assistant by updating the model parameter"],"best_for":["teams managing multiple assistants with different capability requirements","applications where model selection is a deployment decision","cost-conscious teams balancing model capability with API spend"],"limitations":["model changes are not retroactive — existing threads continue using the original model","no automatic model selection based on task complexity; clients must choose model upfront","model availability varies by region and API tier; some models may not be accessible","model deprecation is announced by OpenAI; no automatic migration or fallback","changing models mid-thread is not supported; new threads must be created with new model"],"requires":["OpenAI API key with access to desired model","model parameter in assistant creation (e.g., 'gpt-4-turbo-2024-04-09')","awareness of model capabilities, costs, and availability"],"input_types":["model identifier (string)","assistant configuration"],"output_types":["assistant metadata with model version","responses generated by selected model"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_8","uri":"capability://data.processing.analysis.response.format.enforcement.with.json.mode","name":"response format enforcement with json mode","description":"Assistants can be configured to enforce structured output via JSON mode, where the model is constrained to generate valid JSON matching a specified schema. This enables reliable extraction of structured data from assistant responses without post-processing or parsing. JSON mode uses the model's native structured output capability (available in gpt-4-turbo and later), ensuring schema compliance at generation time rather than post-hoc validation.","intents":["extract structured data from unstructured text (e.g., parse customer feedback into JSON fields)","ensure assistant responses are machine-readable and parseable without error handling","integrate assistant outputs directly into downstream systems expecting structured data","reduce token usage by constraining output format rather than relying on prompt engineering"],"best_for":["data extraction and ETL workflows","API integrations requiring structured responses","applications where response parsing reliability is critical"],"limitations":["JSON mode is only available in gpt-4-turbo and later; not supported in GPT-3.5","schema must be defined upfront; dynamic schema generation is not supported","JSON mode may reduce response quality or creativity compared to unconstrained generation","no schema validation beyond JSON syntax; custom validation logic must be implemented client-side","schema changes require assistant updates; no schema versioning or migration support"],"requires":["OpenAI API key with gpt-4-turbo or later model","response_format parameter set to {\"type\": \"json_object\"}","optional JSON schema definition (if using strict mode)"],"input_types":["user messages","optional JSON schema (as JSON Schema format)"],"output_types":["valid JSON string matching schema","structured data (parsed JSON)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__cap_9","uri":"capability://automation.workflow.run.lifecycle.management.with.status.tracking","name":"run lifecycle management with status tracking","description":"Assistants execute via 'runs' — stateful objects representing a single invocation of an assistant on a thread. Runs have explicit lifecycle states (queued, in_progress, completed, failed, expired) that clients can poll or stream. Run status includes metadata like created_at, started_at, completed_at, and failure reasons. Clients can cancel runs, retrieve run history, and inspect tool calls and usage within a run. This enables observability and error handling at the run level.","intents":["track the status of long-running assistant invocations without blocking","implement timeout and cancellation logic for assistants that exceed time/cost budgets","debug failures by inspecting run status, tool calls, and error messages","audit assistant usage by querying run history and token consumption"],"best_for":["applications with long-running tasks requiring progress tracking","systems needing observability and debugging capabilities","teams implementing SLAs or cost controls on assistant usage"],"limitations":["run status polling adds latency — clients must poll or use streaming to detect completion","no built-in timeout enforcement — clients must implement timeout logic","run history is not automatically pruned; old runs accumulate and may impact query performance","cancellation is not guaranteed to stop execution immediately; in-progress operations may complete","no run-level cost tracking; token usage must be calculated from run metadata"],"requires":["OpenAI API key","thread_id to create runs","assistant_id to specify which assistant to run","polling or streaming mechanism to track run status"],"input_types":["assistant_id and thread_id","optional additional_instructions for the run"],"output_types":["run object with status, timestamps, tool calls","run history (list of runs on a thread)","usage metadata (tokens, model)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openai-assistants__headline","uri":"capability://memory.knowledge.managed.ai.assistant.api","name":"managed ai assistant api","description":"OpenAI Assistants is a managed API that allows developers to create persistent AI assistants with advanced capabilities like tool integration, state management, and multi-threading, making it ideal for building complex conversational agents.","intents":["best AI assistant API","AI assistants for customer support","managed AI assistant solutions","create AI assistants with tools","persistent AI agents for applications"],"best_for":["developers building conversational agents"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":78,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with Assistants API access","thread_id parameter for resuming conversations","network connectivity for all state queries","OpenAI API key with Code Interpreter tool enabled","assistant configured with tools=[{\"type\": \"code_interpreter\"}]","Python 3.11 compatible code","OpenAI API key","run in 'requires_action' state with tool_calls","tool_use_id for each tool call being submitted","output string for each tool result"],"failure_modes":["thread state is immutable — message editing/deletion not supported, only append-only operations","no built-in conversation branching or forking — each thread is linear","thread storage counts toward API usage costs; 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