{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-tiktoken","slug":"pypi-tiktoken","name":"tiktoken","type":"repo","url":"https://pypi.org/project/tiktoken/","page_url":"https://unfragile.ai/pypi-tiktoken","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-tiktoken__cap_0","uri":"capability://data.processing.analysis.bpe.tokenization.with.openai.model.encoding","name":"bpe tokenization with openai model encoding","description":"Implements Byte-Pair Encoding (BPE) tokenization specifically optimized for OpenAI's language models (GPT-3, GPT-4, etc.). Uses pre-trained vocabulary files and encoding schemes that match OpenAI's internal tokenization, enabling accurate token counting and text-to-token conversion for billing, context window management, and prompt optimization. The implementation leverages Rust bindings compiled to native code for 10-100x performance improvement over pure Python tokenizers.","intents":["Count tokens in text before sending to OpenAI API to estimate costs and stay within context limits","Split long documents into token-bounded chunks that fit within model context windows","Verify token counts match OpenAI's billing to audit API costs","Optimize prompts by understanding exact token consumption of different phrasings"],"best_for":["Python developers building applications with OpenAI's GPT models","Teams managing LLM costs and needing accurate token accounting","Prompt engineers optimizing for token efficiency","AI product builders requiring deterministic token counting before API calls"],"limitations":["Encoding schemes are model-specific — cl100k_base for GPT-4/3.5-turbo, p50k_base for older models; using wrong encoding produces incorrect counts","Requires pre-downloaded encoding files (tiktoken_data) which add ~10-50MB to disk; lazy loading available but first call incurs download latency","No support for custom vocabularies or fine-tuned model tokenizers — only OpenAI's official encodings","Token counts may drift slightly if OpenAI updates their tokenizer without releasing new encoding files"],"requires":["Python 3.8+","pip or poetry for package installation","Network access for initial encoding file download (cached locally after first use)","~50MB disk space for encoding data files"],"input_types":["text (UTF-8 strings)","bytes (raw binary data)"],"output_types":["integer (token count)","list of integers (token IDs)","list of strings (decoded tokens)"],"categories":["data-processing-analysis","tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tiktoken__cap_1","uri":"capability://data.processing.analysis.multi.model.encoding.scheme.selection","name":"multi-model encoding scheme selection","description":"Provides a registry of pre-configured encoding schemes for different OpenAI model families, allowing automatic selection based on model name or manual specification. Supports cl100k_base (GPT-4, GPT-3.5-turbo), p50k_base (text-davinci-003), r50k_base (GPT-3), and legacy encodings. The implementation uses lazy-loading of encoding files and caches them in-memory after first access, minimizing startup latency while avoiding redundant file I/O.","intents":["Automatically get the correct tokenizer for a given OpenAI model without manual configuration","Switch between different model encodings when comparing token counts across model families","Handle legacy models that use older tokenization schemes without code changes","Ensure compatibility when OpenAI releases new model versions with updated tokenizers"],"best_for":["Multi-model applications that support GPT-3, GPT-3.5, and GPT-4 simultaneously","Teams migrating between OpenAI model versions and needing backward compatibility","Frameworks and libraries wrapping OpenAI API that need model-agnostic tokenization"],"limitations":["Encoding selection is static at initialization — cannot dynamically switch encodings within a single process without creating new tokenizer instances","Model name matching is string-based and brittle; custom model names or fine-tuned variants may not auto-map to correct encoding","No automatic fallback if encoding file is missing or corrupted — raises explicit error rather than degrading gracefully"],"requires":["Python 3.8+","Knowledge of OpenAI model names and their corresponding encoding schemes"],"input_types":["string (model name, e.g., 'gpt-4', 'gpt-3.5-turbo')","string (encoding name, e.g., 'cl100k_base')"],"output_types":["Encoding object (configured tokenizer instance)"],"categories":["data-processing-analysis","configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tiktoken__cap_2","uri":"capability://data.processing.analysis.batch.token.encoding.and.decoding","name":"batch token encoding and decoding","description":"Converts sequences of text strings to token ID lists and vice versa in a single operation, with support for both single-string and batch processing. Uses vectorized Rust operations to encode/decode multiple texts efficiently without Python-level iteration overhead. Handles edge cases like special tokens, BOS/EOS markers, and multi-byte UTF-8 sequences transparently.","intents":["Convert a batch of documents to token IDs for embedding or fine-tuning dataset preparation","Decode token sequences back to human-readable text for debugging or inspection","Process large datasets of text with minimal per-item overhead","Handle special tokens and control characters correctly in batch operations"],"best_for":["Data engineers preparing datasets for fine-tuning or evaluation","Batch processing pipelines that tokenize thousands of documents","Debugging and inspection tools that need bidirectional token-text conversion","ML training loops that require efficient token ID generation"],"limitations":["Batch operations are not truly parallel — Rust implementation is single-threaded; no GPU acceleration","Memory usage scales linearly with batch size; very large batches (>1M texts) may cause memory pressure","Decoding is lossy for some special tokens — round-trip (text → tokens → text) may not preserve original formatting exactly","No streaming API for processing unbounded text streams; entire input must fit in memory"],"requires":["Python 3.8+","Sufficient RAM for batch size (typically 1-10MB per 1M tokens)"],"input_types":["list of strings (for batch encoding)","list of integers (for batch decoding)","single string (for single encoding)"],"output_types":["list of lists of integers (batch token IDs)","list of strings (batch decoded text)","list of integers (single token IDs)"],"categories":["data-processing-analysis","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tiktoken__cap_3","uri":"capability://data.processing.analysis.special.token.and.control.sequence.handling","name":"special token and control sequence handling","description":"Recognizes and correctly tokenizes OpenAI's special tokens (e.g., <|endoftext|>, <|im_start|>, <|im_end|> for chat models) and control sequences without treating them as regular text. Maintains a special token registry per encoding scheme and ensures these tokens are preserved during encode/decode operations. Supports explicit special token injection for prompt construction and message formatting.","intents":["Correctly count tokens in chat prompts that include special formatting markers like <|im_start|> and <|im_end|>","Construct multi-turn conversation prompts with proper special token placement for GPT-4 chat models","Ensure special tokens are not accidentally split or corrupted during tokenization","Validate that prompt structure includes required special tokens before sending to API"],"best_for":["Chat application developers using GPT-4 or GPT-3.5-turbo with special chat tokens","Prompt engineers constructing complex multi-turn conversations","Teams building custom chat interfaces that need to match OpenAI's token accounting","Fine-tuning workflows that require precise special token placement"],"limitations":["Special token set is fixed per encoding — cannot add custom special tokens without modifying the encoding file","Special token handling is encoding-specific; using wrong encoding may miscount special tokens","No validation that special tokens are used in correct positions — library tokenizes them correctly but doesn't enforce prompt structure rules"],"requires":["Python 3.8+","Knowledge of which special tokens are valid for the target model (e.g., <|im_start|> for chat models only)"],"input_types":["text string containing special tokens (e.g., '<|im_start|>user\\nHello<|im_end|>')"],"output_types":["list of integers (token IDs including special token IDs)","integer (count including special tokens)"],"categories":["data-processing-analysis","text-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tiktoken__cap_4","uri":"capability://data.processing.analysis.token.id.to.string.mapping.and.inspection","name":"token id to string mapping and inspection","description":"Provides bidirectional mapping between token IDs and their string representations, enabling inspection and debugging of tokenization. Exposes the underlying vocabulary as a queryable dictionary and supports reverse lookups (token ID → string) for understanding what each token represents. Useful for analyzing tokenization artifacts and understanding model behavior.","intents":["Inspect what text a specific token ID represents for debugging tokenization issues","Analyze the vocabulary to understand how the model breaks down text","Verify that tokenization is working as expected by spot-checking token IDs","Generate human-readable token sequences for logging and monitoring"],"best_for":["Prompt engineers debugging unexpected tokenization behavior","Researchers analyzing how models tokenize different text patterns","Developers building tokenization visualization or inspection tools","Teams troubleshooting token count discrepancies"],"limitations":["Vocabulary is read-only — cannot inspect or modify token mappings after initialization","Some tokens represent whitespace or control characters that may not display clearly in logs","Vocabulary size is large (~100k tokens for cl100k_base) — full vocabulary dump is memory-intensive","No semantic information about tokens — only string representation, not meaning or usage patterns"],"requires":["Python 3.8+","Basic understanding of BPE tokenization and token IDs"],"input_types":["integer (token ID)","string (token string, for reverse lookup)"],"output_types":["string (token representation)","integer (token ID)","dictionary (full vocabulary mapping)"],"categories":["data-processing-analysis","inspection-debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tiktoken__cap_5","uri":"capability://data.processing.analysis.efficient.in.memory.encoding.caching","name":"efficient in-memory encoding caching","description":"Automatically caches loaded encoding files in memory after first access, eliminating repeated disk I/O or network downloads for subsequent tokenization calls. Uses a thread-safe singleton pattern to ensure only one copy of each encoding is loaded per process. Supports explicit cache control (clear, reload) for testing or memory-constrained environments.","intents":["Minimize latency on first tokenization call by caching encoding data after download","Reduce memory overhead in long-running applications by sharing encoding instances across multiple tokenizer objects","Enable testing and development workflows that require switching between encodings without restarting the process","Optimize resource usage in serverless or containerized environments with strict memory limits"],"best_for":["Long-running server applications that tokenize many requests sequentially","Batch processing pipelines that reuse the same encoding across multiple jobs","Development and testing workflows that require encoding reloads","Memory-constrained environments like AWS Lambda or edge devices"],"limitations":["Cache is process-local and not shared across multiple Python processes — each process loads its own copy","No cache invalidation mechanism if encoding files are updated on disk — requires process restart to pick up changes","Cache size is fixed once loaded — cannot evict encodings to free memory without clearing entire cache","Thread-safety adds minimal overhead but may cause contention in highly concurrent scenarios"],"requires":["Python 3.8+","Sufficient RAM to hold encoding files (~10-50MB per encoding)"],"input_types":["encoding name or model name (for cache lookup)"],"output_types":["Encoding object (from cache or freshly loaded)"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","pip or poetry for package installation","Network access for initial encoding file download (cached locally after first use)","~50MB disk space for encoding data files","Knowledge of OpenAI model names and their corresponding encoding schemes","Sufficient RAM for batch size (typically 1-10MB per 1M tokens)","Knowledge of which special tokens are valid for the target model (e.g., <|im_start|> for chat models only)","Basic understanding of BPE tokenization and token IDs","Sufficient RAM to hold encoding files (~10-50MB per encoding)"],"failure_modes":["Encoding schemes are model-specific — cl100k_base for GPT-4/3.5-turbo, p50k_base for older models; using wrong encoding produces incorrect counts","Requires pre-downloaded encoding files (tiktoken_data) which add ~10-50MB to disk; lazy loading available but first call incurs download latency","No support for custom vocabularies or fine-tuned model tokenizers — only OpenAI's official encodings","Token counts may drift slightly if OpenAI updates their tokenizer without releasing new encoding files","Encoding selection is static at initialization — cannot dynamically switch encodings within a single process without creating new tokenizer instances","Model name matching is string-based and brittle; custom model names or fine-tuned variants may not auto-map to correct encoding","No automatic fallback if encoding file is missing or corrupted — raises explicit error rather than degrading gracefully","Batch operations are not truly parallel — Rust implementation is single-threaded; no GPU acceleration","Memory usage scales linearly with batch size; very large batches (>1M texts) may cause memory pressure","Decoding is lossy for some special tokens — round-trip (text → tokens → text) may not preserve original formatting exactly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:17.402Z","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=pypi-tiktoken","compare_url":"https://unfragile.ai/compare?artifact=pypi-tiktoken"}},"signature":"7jACii5RZL0h+oCF/Yxsrmoo+MJOklFB6J/V9MLA9dy5WorAB01IALmWZF7nduxTT87uh/RMJd3g+eQKkFrQAQ==","signedAt":"2026-06-15T21:39:52.769Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-tiktoken","artifact":"https://unfragile.ai/pypi-tiktoken","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-tiktoken","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"}}