{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ai21-labs-api","slug":"ai21-labs-api","name":"AI21 Labs API","type":"api","url":"https://studio.ai21.com","page_url":"https://unfragile.ai/ai21-labs-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ai21-labs-api__cap_0","uri":"capability://text.generation.language.hybrid.ssm.transformer.language.modeling.with.256k.context.window","name":"hybrid ssm-transformer language modeling with 256k context window","description":"Jamba models combine State Space Models (SSM) with Transformer architecture to enable efficient processing of 256K token context windows. The hybrid approach uses SSM layers for linear-time sequence processing in early layers and Transformer attention selectively in later layers, reducing computational overhead while maintaining long-range dependency modeling. This architecture enables cost-effective inference on long documents without the quadratic memory scaling of pure Transformer models.","intents":["Process documents longer than 100K tokens without hitting memory or latency constraints","Build RAG systems that can ingest entire books or codebases in a single context window","Reduce inference costs for long-context applications compared to pure Transformer models","Maintain reasoning quality over extended sequences without context truncation"],"best_for":["Enterprise teams processing legal documents, research papers, or large codebases","RAG system builders needing efficient long-context retrieval and reasoning","Cost-conscious builders scaling to production with high-volume long-document workloads"],"limitations":["SSM components may have different attention patterns than pure Transformers — some specialized reasoning tasks may require fine-tuning to match performance","256K context window is fixed; cannot extend beyond this limit without model retraining","Hybrid architecture adds complexity to fine-tuning — requires understanding of both SSM and Transformer components"],"requires":["API key from AI21 Labs (obtained via studio.ai21.com)","HTTP/REST client or SDK (Python, JavaScript, or language-agnostic via REST)","Understanding of token counting for 256K window management"],"input_types":["text (UTF-8 encoded strings)","structured prompts with system/user message roles"],"output_types":["text (generated completions)","structured JSON (when using function calling or schema-based outputs)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_1","uri":"capability://text.generation.language.contextual.question.answering.with.document.grounding","name":"contextual question-answering with document grounding","description":"API endpoint that accepts a document or context passage and a question, returning answers grounded in the provided text with citation support. The system uses the 256K context window to embed full documents and perform retrieval-augmented generation internally, eliminating the need for external RAG infrastructure. Responses include confidence scores and source span references indicating which parts of the input document support the answer.","intents":["Answer questions about uploaded documents without building a separate RAG pipeline","Extract specific information from long documents with source attribution","Build chatbots that reference documents while preventing hallucinations","Verify that answers are grounded in provided context rather than model knowledge"],"best_for":["Teams building document Q&A systems without dedicated vector database infrastructure","Enterprise applications requiring audit trails and source attribution for compliance","Rapid prototyping of document-based assistants before investing in full RAG systems"],"limitations":["Grounding is limited to provided context — cannot augment with external knowledge sources without explicit inclusion in the prompt","Performance degrades if document contains contradictory information — model may struggle to reconcile conflicting statements","Citation accuracy depends on model's ability to identify relevant spans; edge cases with paraphrased content may produce imprecise citations"],"requires":["API key from AI21 Labs","Document content as plain text (max 256K tokens)","Question formatted as natural language query"],"input_types":["text (document content)","text (question)"],"output_types":["text (answer)","structured JSON (answer + confidence score + source spans)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_10","uri":"capability://safety.moderation.enterprise.api.authentication.and.rate.limiting","name":"enterprise api authentication and rate limiting","description":"Enterprise-grade authentication system supporting API keys, OAuth 2.0, and service accounts, with configurable rate limiting, quota management, and usage monitoring. The system enforces per-user, per-organization, and per-endpoint rate limits, provides real-time usage dashboards, and supports burst allowances for batch processing. Includes audit logging for compliance and security monitoring.","intents":["Secure API access with multiple authentication methods for different deployment scenarios","Manage API usage across teams with per-user and per-organization quotas","Monitor API consumption for billing and cost optimization","Implement audit trails for compliance and security investigations"],"best_for":["Enterprise organizations with multi-team deployments and compliance requirements","Teams needing granular usage monitoring and quota management","Applications requiring audit trails for regulatory compliance"],"limitations":["Rate limiting is enforced at API gateway level — may introduce latency for requests near rate limit boundaries","Quota resets are time-based (hourly, daily, monthly) — no support for custom reset schedules","Audit logs are retained for limited period (typically 30-90 days) — long-term compliance requires external archival","OAuth 2.0 setup requires external identity provider configuration"],"requires":["API key or OAuth 2.0 credentials from AI21 Labs","Optional: service account setup for programmatic access","Optional: external identity provider (for OAuth 2.0)"],"input_types":["authentication credentials (API key, OAuth token, service account)"],"output_types":["JSON (authentication response, rate limit headers)","JSON (usage metrics, quota status)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_11","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"API feature that constrains model outputs to match provided JSON schemas, ensuring responses are valid structured data. The system uses schema-guided decoding to enforce schema compliance during generation, preventing invalid JSON or missing required fields. Supports complex nested schemas, enums, and conditional fields, with validation errors returned if the model cannot satisfy the schema.","intents":["Extract structured data from unstructured text with guaranteed schema compliance","Generate API responses that conform to predefined data models","Build data pipelines that require structured outputs without post-processing validation","Ensure consistency of model outputs across multiple requests"],"best_for":["Data extraction pipelines requiring structured outputs","API services that need to return consistent data models","Applications where schema compliance is critical (e.g., database ingestion)"],"limitations":["Schema-guided decoding adds latency (10-20% slower than unconstrained generation) due to validation overhead","Complex schemas may be difficult for the model to satisfy — generation may fail if schema is too restrictive","No support for dynamic schema generation — schemas must be predefined","Schema size impacts token usage — large schemas consume significant context"],"requires":["API key from AI21 Labs","JSON schema definition for desired output structure"],"input_types":["text (prompt)","JSON schema (output structure definition)"],"output_types":["JSON (response conforming to provided schema)","error message (if schema cannot be satisfied)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_2","uri":"capability://data.processing.analysis.automatic.text.segmentation.and.structural.analysis","name":"automatic text segmentation and structural analysis","description":"API that analyzes input text to automatically identify logical segments (paragraphs, sections, chapters) and extract structural metadata (headings, hierarchies, topic boundaries). Uses the model's understanding of document structure to segment text without relying on heuristic rules or regex patterns. Returns segment boundaries with confidence scores and inferred structural relationships between segments.","intents":["Split long documents into semantically meaningful chunks for downstream processing","Identify document structure (chapters, sections) without manual annotation","Prepare documents for indexing by respecting natural content boundaries","Extract hierarchical structure from unstructured text for knowledge graph construction"],"best_for":["Document processing pipelines that need semantic chunking instead of fixed-size splitting","Knowledge management systems extracting structure from heterogeneous document formats","Teams building document understanding systems without manual annotation"],"limitations":["Segmentation quality varies with document type — works best on well-structured documents (reports, articles) and may struggle with conversational or mixed-format content","No support for multi-modal documents (images, tables) — text-only analysis","Segment boundaries are probabilistic; confidence scores may be low for ambiguous content boundaries"],"requires":["API key from AI21 Labs","Text input (UTF-8 encoded, max 256K tokens)"],"input_types":["text (unstructured or semi-structured documents)"],"output_types":["structured JSON (segment boundaries, confidence scores, inferred hierarchy)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_3","uri":"capability://text.generation.language.abstractive.and.extractive.summarization.with.customizable.length","name":"abstractive and extractive summarization with customizable length","description":"Summarization API that generates concise summaries of input text with configurable length targets (short, medium, long) and summary type (abstractive synthesis or extractive key sentences). The system uses the 256K context to summarize entire documents in a single pass without chunking, maintaining coherence across long source material. Supports both generic summaries and domain-specific summarization (e.g., legal, technical) via prompt engineering.","intents":["Generate executive summaries of long documents for quick review","Extract key points from research papers or reports for knowledge base indexing","Create multi-length summaries (abstract, detailed, full) from single source","Summarize domain-specific content (legal contracts, technical specs) with specialized terminology"],"best_for":["Enterprise document management systems requiring automated summary generation","Content platforms (news, research) needing scalable summarization at volume","Teams building knowledge bases that need multi-level summaries for discoverability"],"limitations":["Abstractive summaries may introduce subtle semantic shifts or minor inaccuracies — not suitable for legal/compliance use without human review","Extractive summaries are limited to sentences present in source; cannot paraphrase or synthesize across sentences","Summary quality degrades with very dense technical content (e.g., mathematical proofs, code-heavy documentation)","No support for multi-document summarization — each document summarized independently"],"requires":["API key from AI21 Labs","Text input (max 256K tokens)","Optional: length target and summary type parameters"],"input_types":["text (documents to summarize)"],"output_types":["text (summary)","structured JSON (summary + metadata about compression ratio, key entities)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_4","uri":"capability://text.generation.language.fine.tuning.with.custom.datasets.and.domain.adaptation","name":"fine-tuning with custom datasets and domain adaptation","description":"Enterprise fine-tuning service that allows customers to adapt Jamba models to domain-specific tasks using custom training data. The system handles data preparation, training loop management, and model versioning, returning a fine-tuned model endpoint accessible via the same API interface. Supports both instruction-following fine-tuning and continued pretraining on domain corpora, with monitoring dashboards for training metrics and inference performance.","intents":["Adapt Jamba models to specialized domains (legal, medical, finance) with proprietary data","Improve performance on domain-specific tasks without retraining from scratch","Create private model versions that don't expose training data to shared infrastructure","Maintain model consistency across custom use cases while leveraging base model capabilities"],"best_for":["Enterprise teams with proprietary domain data and regulatory requirements for model isolation","Organizations needing specialized model behavior (e.g., legal document analysis, medical coding)","Teams with sufficient data volume (1000+ examples) to justify fine-tuning investment"],"limitations":["Fine-tuning requires significant data preparation and quality control — poor training data degrades model performance","Training time and cost scale with dataset size; large datasets (100K+ examples) may be expensive","Fine-tuned models inherit base model limitations (e.g., SSM architecture constraints) — cannot fundamentally change model behavior","No multi-task fine-tuning support — each fine-tuned model optimized for single task","Requires manual evaluation to determine if fine-tuning improved performance vs. prompt engineering"],"requires":["API key from AI21 Labs with fine-tuning tier access","Training dataset in JSONL format (minimum 100-1000 examples depending on task)","Validation dataset for monitoring training progress","Understanding of model evaluation metrics for your domain"],"input_types":["JSONL (training examples with input/output pairs)","text (domain corpus for continued pretraining)"],"output_types":["model endpoint (accessible via standard API)","training metrics (loss curves, validation performance)","model artifacts (weights, configuration)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_5","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.invocation","name":"function calling with schema-based tool invocation","description":"API feature that enables structured function calling through JSON schema definitions, allowing the model to invoke external tools or APIs based on user requests. The system parses user intent, matches it against registered function schemas, and returns structured function calls with parameters. Supports chaining multiple function calls in sequence and includes validation against provided schemas to ensure parameter correctness.","intents":["Build AI agents that can call external APIs (weather, database, payment systems) based on user requests","Create structured workflows where the model decides which tools to use and in what order","Enforce parameter validation and type safety for tool invocations","Integrate language models into existing tool ecosystems without custom parsing logic"],"best_for":["Developers building AI agents that need to interact with external systems","Teams creating structured workflows with deterministic tool selection","Applications requiring parameter validation and type safety for tool calls"],"limitations":["Function calling accuracy depends on schema clarity — ambiguous or poorly-documented schemas lead to incorrect tool selection","No built-in error handling for failed function calls — requires external retry logic and fallback mechanisms","Chain-of-thought reasoning for tool selection is implicit; no visibility into model's decision-making process","Schema size impacts token usage — complex schemas with many functions consume significant context"],"requires":["API key from AI21 Labs","JSON schema definitions for each function/tool","Client-side implementation to execute returned function calls"],"input_types":["text (user request)","JSON schema (function definitions)"],"output_types":["structured JSON (function name + parameters)","text (reasoning or explanation of tool choice)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_6","uri":"capability://automation.workflow.batch.processing.api.for.high.volume.inference","name":"batch processing api for high-volume inference","description":"Asynchronous batch processing endpoint that accepts large numbers of requests (100s to 1000s) in a single batch job, processes them with optimized throughput, and returns results via callback or polling. The system queues requests, schedules them across available compute resources, and provides job status tracking and result retrieval. Significantly reduces per-request overhead compared to individual API calls, enabling cost-effective processing of large document collections.","intents":["Process thousands of documents (summarization, QA, segmentation) in a single batch job","Reduce per-request API costs by amortizing overhead across multiple requests","Integrate language model processing into data pipelines without real-time latency constraints","Monitor batch job progress and retrieve results asynchronously"],"best_for":["Data teams processing large document collections (10K+ documents)","Cost-conscious builders with non-real-time processing requirements","ETL pipelines integrating language model processing into data workflows"],"limitations":["Batch processing introduces latency — results available after minutes to hours, not milliseconds","No priority queuing — all batch jobs processed in FIFO order without SLA guarantees","Batch size limits (e.g., max 1000 requests per batch) require splitting very large jobs","Callback mechanisms may be unreliable; polling adds complexity to client code"],"requires":["API key from AI21 Labs","JSONL file with batch requests (one request per line)","Callback endpoint (for webhook delivery) or polling mechanism"],"input_types":["JSONL (batch requests with parameters)"],"output_types":["JSONL (batch results with request ID mapping)","JSON (job status and metadata)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_7","uri":"capability://data.processing.analysis.token.counting.and.context.window.management.utilities","name":"token counting and context window management utilities","description":"Utility functions that accurately count tokens in input text according to Jamba's tokenizer, enabling precise context window management and cost estimation. The system provides token counts for prompts, completions, and full requests, supporting both synchronous queries and batch token counting. Includes utilities to truncate text to fit within the 256K context window while preserving semantic coherence.","intents":["Estimate API costs before making requests based on token counts","Ensure prompts fit within the 256K context window without trial-and-error","Implement smart context truncation that preserves important information","Monitor token usage across applications for billing and optimization"],"best_for":["Developers building cost-conscious applications with variable input sizes","Teams implementing context window management in RAG systems","Applications requiring accurate cost estimation before API calls"],"limitations":["Token counts are estimates based on tokenizer behavior — actual counts may vary slightly with model updates","Truncation utilities are heuristic-based; may not preserve semantic coherence in all cases","No support for multi-language token counting — counts may be inaccurate for non-English text"],"requires":["API key from AI21 Labs","Text input (UTF-8 encoded)"],"input_types":["text (to count tokens)"],"output_types":["JSON (token count, estimated cost)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_8","uri":"capability://text.generation.language.streaming.response.generation.for.real.time.output","name":"streaming response generation for real-time output","description":"Streaming API that returns model outputs token-by-token as they are generated, enabling real-time display of responses without waiting for full completion. Uses HTTP Server-Sent Events (SSE) or WebSocket protocols to deliver tokens incrementally, reducing perceived latency and enabling interactive applications. Supports streaming for all text generation tasks (completion, QA, summarization) with optional token metadata (confidence, alternatives).","intents":["Build chat interfaces that display responses as they are generated","Reduce perceived latency in interactive applications by showing partial results","Enable early termination of long-running generations if user cancels","Implement real-time monitoring of token generation for debugging or analytics"],"best_for":["Interactive chat applications and user-facing interfaces","Real-time dashboards displaying model outputs","Applications where perceived latency is critical to user experience"],"limitations":["Streaming adds complexity to client implementation — requires handling partial responses and connection management","Token-by-token delivery may expose model uncertainty or reasoning artifacts to users","Streaming connections consume resources; not suitable for high-concurrency scenarios without load balancing","No support for streaming function calls — structured outputs require buffering until completion"],"requires":["API key from AI21 Labs","HTTP client supporting SSE or WebSocket","Client-side implementation to handle streaming responses"],"input_types":["text (prompt)"],"output_types":["stream of text tokens (via SSE or WebSocket)","optional: JSON metadata per token"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__cap_9","uri":"capability://text.generation.language.multi.turn.conversation.management.with.stateful.context","name":"multi-turn conversation management with stateful context","description":"Conversation API that maintains conversation state across multiple turns, automatically managing context history and token limits. The system tracks conversation history, applies sliding window context management to stay within the 256K limit, and supports system prompts for conversation behavior customization. Enables building stateful chatbots without manual context management on the client side.","intents":["Build multi-turn chatbots that maintain conversation context without client-side state management","Implement conversation memory that automatically respects the 256K context window","Create personalized assistants with system prompts that guide conversation behavior","Track conversation metrics (turn count, token usage) for analytics"],"best_for":["Teams building chatbot applications without custom conversation management","Interactive assistants requiring stateful context across turns","Applications needing automatic context window management"],"limitations":["Conversation state is server-side; requires session management and authentication to prevent cross-user context leakage","Context truncation (when exceeding 256K) may lose early conversation history — no configurable retention policies","No support for branching conversations or conversation forking","Session timeout policies may discard conversation state unexpectedly"],"requires":["API key from AI21 Labs","Session management (to track conversation state)","Authentication mechanism (to prevent unauthorized access to conversations)"],"input_types":["text (user message)","optional: system prompt"],"output_types":["text (assistant response)","JSON (conversation metadata: turn count, token usage, context window status)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ai21-labs-api__headline","uri":"capability://llm.apis.llm.api.for.enterprise.applications","name":"llm api for enterprise applications","description":"An API that provides access to advanced Jamba models featuring a hybrid SSM-Transformer architecture, enabling contextual answers, text segmentation, and summarization tailored for enterprise use.","intents":["best LLM API","LLM API for enterprise applications","top API for contextual text processing","API for summarization and text segmentation","best API for fine-tuning language models"],"best_for":["enterprise applications","contextual text processing"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key from AI21 Labs (obtained via studio.ai21.com)","HTTP/REST client or SDK (Python, JavaScript, or language-agnostic via REST)","Understanding of token counting for 256K window management","API key from AI21 Labs","Document content as plain text (max 256K tokens)","Question formatted as natural language query","API key or OAuth 2.0 credentials from AI21 Labs","Optional: service account setup for programmatic access","Optional: external identity provider (for OAuth 2.0)","JSON schema definition for desired output structure"],"failure_modes":["SSM components may have different attention patterns than pure Transformers — some specialized reasoning tasks may require fine-tuning to match performance","256K context window is fixed; cannot extend beyond this limit without model retraining","Hybrid architecture adds complexity to fine-tuning — requires understanding of both SSM and Transformer components","Grounding is limited to provided context — cannot augment with external knowledge sources without explicit inclusion in the prompt","Performance degrades if document contains contradictory information — model may struggle to reconcile conflicting statements","Citation accuracy depends on model's ability to identify relevant spans; edge cases with paraphrased content may produce imprecise citations","Rate limiting is enforced at API gateway level — may introduce latency for requests near rate limit boundaries","Quota resets are time-based (hourly, daily, monthly) — no support for custom reset schedules","Audit logs are retained for limited period (typically 30-90 days) — long-term compliance requires external archival","OAuth 2.0 setup requires external identity provider configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:19.836Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ai21-labs-api","compare_url":"https://unfragile.ai/compare?artifact=ai21-labs-api"}},"signature":"IBT3ocbzgWDhD1Hf+0rxMQ4+hzKBKFZ589HJBM0/gQMOUYyMEc0i+VgnZbPH/ngLl+2naYNL5iBLiFlR5I0mAw==","signedAt":"2026-06-20T14:07:02.254Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai21-labs-api","artifact":"https://unfragile.ai/ai21-labs-api","verify":"https://unfragile.ai/api/v1/verify?slug=ai21-labs-api","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"}}