{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llm-simon-willison","slug":"llm-simon-willison","name":"llm (Simon Willison)","type":"cli","url":"https://github.com/simonw/llm","page_url":"https://unfragile.ai/llm-simon-willison","categories":["cli-tools"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llm-simon-willison__cap_0","uri":"capability://tool.use.integration.provider.agnostic.model.abstraction.with.unified.interface","name":"provider-agnostic model abstraction with unified interface","description":"Implements a dual sync/async base class hierarchy (Model, AsyncModel, KeyModel, AsyncKeyModel) defined in llm/models.py that abstracts away provider-specific details. Any model—whether OpenAI, Anthropic, local, or plugin-provided—inherits from these base classes and implements prompt() and execute() methods, allowing identical code to work across all providers without conditional logic or provider detection.","intents":["I want to write code that works with OpenAI today but can switch to Anthropic or a local model tomorrow without refactoring","I need to support multiple LLM providers in my application without duplicating prompt/response handling logic","I want to add a new model provider via plugin without modifying core application code"],"best_for":["developers building multi-provider LLM applications","teams wanting to avoid vendor lock-in to a single model provider","plugin developers extending llm with custom models"],"limitations":["Abstractions add minimal overhead but require all models to implement the full interface even if some methods are no-ops","Provider-specific features (e.g., vision capabilities, function calling schemas) must be normalized to a common interface, potentially losing nuanced control","Async/sync duality requires maintaining two code paths, increasing maintenance burden"],"requires":["Python 3.9+","llm package installed","API keys or local model setup for at least one provider"],"input_types":["text prompts","system prompts","attachments (images, audio, files)","tool definitions","schema specifications"],"output_types":["text responses","structured JSON (via schema)","usage statistics (tokens, cost)","async iterators for streaming"],"categories":["tool-use-integration","abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_1","uri":"capability://memory.knowledge.persistent.conversation.history.with.sqlite.logging","name":"persistent conversation history with sqlite logging","description":"Automatically logs all model interactions to a local SQLite database (logs.db) with full conversation state, including prompts, responses, model metadata, tokens used, and timestamps. The Conversation class in llm/models.py maintains multi-turn dialogue state and can be serialized/deserialized from the database, enabling conversation resumption, audit trails, and historical analysis without external services.","intents":["I want every LLM interaction my application makes to be logged for compliance and debugging","I need to resume a multi-turn conversation with an LLM after my application restarts","I want to analyze conversation patterns, token usage, and cost across all my LLM calls"],"best_for":["applications requiring audit trails and compliance logging","interactive CLI tools and chatbots with session management","teams analyzing LLM usage patterns and optimizing costs"],"limitations":["SQLite is single-writer, so high-concurrency scenarios (many simultaneous conversations) may experience lock contention","Database grows unbounded without manual pruning; no built-in retention policies or archival","Logging happens synchronously by default, adding latency to each LLM call (typically <10ms for local SQLite writes)"],"requires":["Python 3.9+","SQLite3 (included in Python stdlib)","Write permissions to ~/.llm/ directory (default logs location)","llm package with database schema initialized"],"input_types":["prompt text","system prompts","model name and parameters","attachments metadata"],"output_types":["SQLite database rows","Conversation objects with full history","JSON export of conversations","usage statistics (tokens, cost, latency)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_10","uri":"capability://text.generation.language.python.api.for.programmatic.llm.access","name":"python api for programmatic llm access","description":"Exposes a Python library interface (llm module) that allows developers to interact with models programmatically without using the CLI. Core functions like llm.get_model(), model.prompt(), and model.execute() provide a simple API for single-turn and multi-turn interactions. The API supports both sync and async patterns, enabling integration into web frameworks, scripts, and applications. Responses are returned as Response objects with methods for accessing text, JSON, and usage statistics.","intents":["I want to use LLMs in my Python application without shelling out to the CLI","I need to integrate LLM calls into a web framework (Flask, FastAPI, Django) with async support","I want to build a Python script that processes multiple prompts in parallel using async/await"],"best_for":["Python developers building LLM-powered applications","teams integrating LLMs into existing Python codebases","developers needing async support for high-concurrency scenarios"],"limitations":["Python-only; no official SDKs for other languages","Async API requires understanding of Python's asyncio; synchronous API is simpler but blocks on I/O","Error handling is minimal; API errors are raised as exceptions without built-in retry logic","No built-in rate limiting or quota management; caller must implement these"],"requires":["Python 3.9+","llm package installed","API keys for model providers","asyncio knowledge for async usage (optional but recommended)"],"input_types":["model names (strings)","prompts (text or Prompt objects)","system prompts","tools and schemas"],"output_types":["Response objects with text(), json(), usage() methods","AsyncResponse objects for async operations","Conversation objects for multi-turn interactions"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_11","uri":"capability://data.processing.analysis.batch.embedding.and.cost.estimation","name":"batch embedding and cost estimation","description":"Supports generating embeddings for large batches of text via the embed_batch() method on EmbeddingModel, which is more efficient than calling embed() repeatedly. The system tracks token usage and can estimate costs based on model pricing. Batch operations are optimized to minimize API calls and reduce costs, particularly useful for processing large document corpora.","intents":["I want to embed a large corpus of documents efficiently without making thousands of individual API calls","I need to estimate the cost of embedding a dataset before committing to it","I want to parallelize embedding generation to speed up processing"],"best_for":["teams building RAG systems with large document collections","applications requiring bulk embedding generation","cost-conscious teams optimizing LLM spending"],"limitations":["Batch operations are limited by API rate limits; very large batches may need to be split","Cost estimation is approximate and depends on accurate pricing data; actual costs may vary","No built-in parallelization; caller must use threading/multiprocessing for concurrent batches","Batch operations don't support streaming; full results are returned at once"],"requires":["Python 3.9+","llm package with embedding support","embedding model with batch support","API key for cloud embedding models"],"input_types":["list of text strings","metadata associated with each text"],"output_types":["list of embedding vectors","cost estimates (in dollars)","token usage statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_12","uri":"capability://tool.use.integration.model.capability.introspection.and.feature.detection","name":"model capability introspection and feature detection","description":"Provides methods to query model capabilities at runtime, such as whether a model supports function calling, vision, streaming, or structured output. The Model base class exposes properties and methods that describe supported features, enabling applications to adapt behavior based on model capabilities without hardcoding provider-specific logic. This enables graceful degradation when features are unavailable.","intents":["I want to check if a model supports vision before trying to send an image","I need to fall back to a different model if the selected model doesn't support function calling","I want to adapt my application's behavior based on what the model can do"],"best_for":["applications supporting multiple models with varying capabilities","developers building adaptive systems that degrade gracefully","teams implementing feature flags based on model capabilities"],"limitations":["Capability information must be manually maintained for each model; no automatic detection","Capabilities may change as models are updated; cached capability info can become stale","No standardized capability schema; different providers use different terminology","Introspection adds overhead; capabilities should be cached rather than queried repeatedly"],"requires":["Python 3.9+","llm package with capability introspection support","model instance"],"input_types":["model instance"],"output_types":["boolean flags for supported features (vision, function_calling, streaming, etc.)","metadata about model (max_tokens, context_window, etc.)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_2","uri":"capability://tool.use.integration.plugin.based.model.and.tool.discovery.with.entry.points","name":"plugin-based model and tool discovery with entry points","description":"Implements a plugin system using Python entry points (setuptools) that auto-discovers and registers custom models, tools, and templates at runtime. The plugin manager in llm/cli.py scans installed packages for llm.models, llm.tools, and llm.templates entry points, dynamically loading them without modifying core code. Plugins can extend functionality by subclassing Model, Tool, or Template base classes.","intents":["I want to package a custom LLM provider (e.g., a proprietary API) as a plugin that users can install with pip","I need to add domain-specific tools (e.g., database queries, API calls) that my LLM can invoke without modifying the core llm codebase","I want to distribute reusable prompt templates as a plugin that teams can share"],"best_for":["plugin developers extending llm with custom models or tools","teams building internal LLM tools and wanting to distribute them as packages","open-source contributors adding new provider support"],"limitations":["Entry point discovery happens at CLI startup, so new plugins require reinstalling/restarting the CLI","No built-in versioning or compatibility checking; breaking changes in plugin APIs can silently fail","Plugin isolation is minimal—malicious or buggy plugins can crash the entire llm process"],"requires":["Python 3.9+","setuptools with entry_points support","llm package installed in the same Python environment","Plugin package must define entry_points in pyproject.toml or setup.py"],"input_types":["Python class definitions (Model, Tool, Template subclasses)","entry_points metadata in pyproject.toml"],"output_types":["registered model instances available via llm.get_model()","tool callables available to models","template strings available via llm templates command"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_3","uri":"capability://tool.use.integration.tool.execution.and.function.calling.with.schema.validation","name":"tool execution and function calling with schema validation","description":"Enables models to invoke Python functions by defining a Tool class with a function() decorator and optional JSON schema. The Toolbox class collects related tools and prepares them for model consumption via prepare() method, which generates tool schemas compatible with OpenAI and Anthropic function-calling APIs. When a model invokes a tool, llm executes the corresponding Python function and returns the result to the model, enabling multi-step reasoning and external action.","intents":["I want my LLM to call Python functions (e.g., database queries, API calls) as part of its reasoning process","I need to constrain what functions an LLM can call and validate their inputs using JSON schemas","I want to build an agentic system where the LLM decides which tools to use and in what order"],"best_for":["developers building LLM agents with external tool access","teams implementing retrieval-augmented generation (RAG) with tool-based document fetching","applications requiring LLM-driven automation with guardrails (schema validation)"],"limitations":["Tool execution is synchronous by default; long-running tools block the LLM response loop","Schema validation is optional—if no schema is provided, the model can pass arbitrary arguments","No built-in timeout or resource limits on tool execution; runaway functions can hang the process","Tool output must be serializable to JSON for the model to consume it"],"requires":["Python 3.9+","llm package with Tool and Toolbox classes","model that supports function calling (OpenAI, Anthropic, or plugin-provided)","JSON schema library (jsonschema) for validation"],"input_types":["Python function definitions with type hints","JSON schema specifications for tool parameters","tool descriptions (docstrings)"],"output_types":["tool invocation results (any JSON-serializable type)","tool schemas compatible with OpenAI/Anthropic APIs","model responses that reference tool calls"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.enforcement","name":"structured output generation with json schema enforcement","description":"Supports constrained generation where models must return JSON matching a provided schema. The Prompt class accepts a schema parameter, and the Response class provides a json() method that parses and validates the model output against the schema. Some providers (e.g., OpenAI with JSON mode) enforce this at the API level; others validate client-side. This enables reliable extraction of structured data (e.g., entities, classifications) from unstructured model outputs.","intents":["I want the LLM to always return valid JSON matching my data model, not arbitrary text","I need to extract structured information (e.g., person name, email, phone) from unstructured text reliably","I want to enforce that the model returns one of a fixed set of options (e.g., sentiment: positive/negative/neutral)"],"best_for":["applications requiring reliable data extraction from LLM outputs","teams building LLM-powered APIs that need to return structured responses","systems where downstream processing depends on consistent JSON structure"],"limitations":["Not all models support schema enforcement; some only validate client-side after generation, wasting tokens","Complex schemas may confuse models or cause generation failures; simpler schemas are more reliable","Schema enforcement adds latency (validation overhead) and may reduce output quality if the schema is too restrictive","No built-in retry logic if the model fails to match the schema; caller must implement retries"],"requires":["Python 3.9+","llm package with schema support","model that supports JSON mode or schema validation (OpenAI, Anthropic, or plugin-provided)","valid JSON schema (JSON Schema Draft 7 or compatible)"],"input_types":["JSON schema definitions (dict or JSON string)","prompt text","system prompts"],"output_types":["JSON-parsed Python objects (dict, list, etc.)","validated against provided schema","raw model text if validation fails"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_5","uri":"capability://image.visual.multi.modal.input.handling.with.attachments.and.fragments","name":"multi-modal input handling with attachments and fragments","description":"Supports attaching images, audio, files, and other media to prompts via the Prompt class's attachments parameter. The Fragments system encapsulates different media types and their metadata, allowing models to process multi-modal inputs. Attachments are serialized and logged to the SQLite database, enabling conversation history to preserve media references. Different models support different attachment types (e.g., OpenAI supports images, Anthropic supports images and PDFs).","intents":["I want to send an image to the LLM and ask it questions about the image content","I need to process PDFs or documents with an LLM that supports document understanding","I want to include screenshots or diagrams in a conversation with the LLM for analysis"],"best_for":["applications requiring vision capabilities (image analysis, OCR, diagram understanding)","document processing pipelines that feed PDFs or images to LLMs","interactive tools where users can attach files for analysis"],"limitations":["Not all models support all attachment types; e.g., local models may not support images","Large attachments increase API costs and latency; no built-in compression or optimization","Attachment handling is provider-specific; the same attachment may be processed differently by OpenAI vs Anthropic","Attachments are logged to SQLite, which can bloat the database for large media files"],"requires":["Python 3.9+","llm package with attachment support","model that supports the desired attachment type (image, PDF, audio, etc.)","file path or bytes for the attachment"],"input_types":["image files (PNG, JPEG, GIF, WebP)","PDF documents","audio files (for some models)","raw bytes or file paths"],"output_types":["model responses analyzing the attachment","attachment metadata in conversation logs","serialized attachment references in database"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_6","uri":"capability://data.processing.analysis.embedding.generation.and.semantic.search.with.vector.storage","name":"embedding generation and semantic search with vector storage","description":"Provides an EmbeddingModel base class for generating vector embeddings from text. The embedding system stores vectors in a separate SQLite database (embeddings.db) with associated metadata, enabling semantic search and similarity operations. Plugins can provide embedding models (e.g., OpenAI's text-embedding-3-small, local models via Ollama). The embed() and embed_batch() methods support both single and bulk embedding generation.","intents":["I want to generate embeddings for a corpus of documents and search them semantically","I need to find similar documents or prompts based on semantic similarity, not keyword matching","I want to use embeddings for clustering, classification, or recommendation tasks"],"best_for":["teams building RAG systems with semantic search","applications requiring similarity-based document retrieval","developers implementing semantic clustering or recommendation engines"],"limitations":["Embedding quality depends on the model; different models produce incompatible vector spaces","Vector storage in SQLite is not optimized for high-dimensional search; use a vector database (Pinecone, Weaviate) for production scale","Embedding generation costs tokens/money with cloud providers; bulk operations can be expensive","No built-in indexing or approximate nearest neighbor search; full-table scans are required for similarity queries"],"requires":["Python 3.9+","llm package with embedding support","embedding model (OpenAI, local via Ollama, or plugin-provided)","API key for cloud embedding models"],"input_types":["text strings","lists of text for batch embedding","metadata associated with embeddings"],"output_types":["vector embeddings (lists of floats)","stored in embeddings.db with metadata","queryable for similarity search"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_7","uri":"capability://text.generation.language.interactive.cli.chat.with.streaming.responses","name":"interactive cli chat with streaming responses","description":"Provides an interactive chat interface via the llm chat command that maintains conversation state, handles multi-turn interactions, and streams model responses to the terminal in real-time. The CLI uses the Conversation class to manage history and the AsyncModel interface for non-blocking streaming. Responses are displayed incrementally as tokens arrive, improving perceived latency and enabling early interruption.","intents":["I want to have a multi-turn conversation with an LLM in the terminal without writing code","I need to see model responses stream in real-time instead of waiting for the full response","I want to resume a previous conversation by loading it from the conversation history"],"best_for":["developers and non-technical users interacting with LLMs via CLI","rapid prototyping and testing of prompts","interactive debugging and exploration of model behavior"],"limitations":["Terminal-based interface limits formatting and media display; images and complex layouts are not well-supported","Streaming adds complexity to error handling; partial responses may be displayed if the model fails mid-stream","No built-in syntax highlighting or code formatting in responses","Conversation history is stored locally; no cloud sync or multi-device access"],"requires":["Python 3.9+","llm package installed and configured with at least one model","API key for the model provider (OpenAI, Anthropic, etc.)","terminal/shell environment"],"input_types":["text prompts typed in the terminal","system prompts via --system flag","model selection via --model flag"],"output_types":["streamed text responses in the terminal","conversation history saved to logs.db","usage statistics (tokens, cost) displayed after each response"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_8","uri":"capability://text.generation.language.prompt.templating.with.variable.substitution.and.reusability","name":"prompt templating with variable substitution and reusability","description":"Supports defining reusable prompt templates with variable placeholders that can be instantiated with different values. Templates are stored as files or registered via the plugin system, and can include system prompts, tools, and schemas. The template system uses simple string substitution (e.g., {variable_name}) to inject values at runtime, enabling prompt reuse across different contexts without code duplication.","intents":["I want to define a prompt template once and reuse it for different inputs without copying and pasting","I need to share prompt templates across my team so everyone uses the same prompt structure","I want to version control and iterate on prompts separately from application code"],"best_for":["teams managing multiple prompts and wanting to avoid duplication","applications with domain-specific prompt patterns (e.g., summarization, classification)","prompt engineering workflows where templates are iterated and versioned"],"limitations":["Template system uses simple string substitution, not a full templating engine (e.g., Jinja2); complex logic requires custom code","No built-in variable validation; missing or incorrect variables fail at runtime","Templates are stored as files or in the plugin system; no built-in UI for managing templates","No versioning or rollback mechanism for templates; changes are immediate"],"requires":["Python 3.9+","llm package with template support","template files in ~/.llm/templates/ or registered via plugin entry points"],"input_types":["template files (text with {variable} placeholders)","variable values (strings, lists, dicts)"],"output_types":["instantiated prompts with variables substituted","full Prompt objects with system prompts, tools, and schemas"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__cap_9","uri":"capability://automation.workflow.model.aliasing.and.configuration.management","name":"model aliasing and configuration management","description":"Allows users to define aliases for models (e.g., 'default' -> 'gpt-4-turbo') and configure model parameters (temperature, max_tokens, system prompts) via configuration files in ~/.llm/. The configuration system supports per-model settings and global defaults, enabling users to customize model behavior without modifying code. Aliases are stored in a configuration file and loaded at CLI startup.","intents":["I want to set a default model so I don't have to specify --model every time","I need to configure different temperature settings for different use cases (e.g., creative vs analytical)","I want to define a system prompt that applies to all conversations with a specific model"],"best_for":["CLI users wanting to customize model behavior without code changes","teams standardizing on specific model configurations across projects","developers managing multiple model aliases for A/B testing"],"limitations":["Configuration is stored in plain text files; no encryption for sensitive settings like API keys","No built-in validation of configuration values; invalid settings fail at runtime","Configuration changes require CLI restart to take effect; no hot-reloading","Limited to simple key-value settings; complex configurations require custom code"],"requires":["Python 3.9+","llm package installed","configuration files in ~/.llm/ directory","write permissions to ~/.llm/"],"input_types":["configuration files (YAML, TOML, or JSON format)","command-line flags (--model, --temperature, etc.)"],"output_types":["model instances with configured parameters","default settings applied to all prompts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-simon-willison__headline","uri":"capability://tool.use.integration.cli.tool.for.interacting.with.large.language.models","name":"cli tool for interacting with large language models","description":"A command-line interface and Python library designed for seamless interaction with various Large Language Models, including OpenAI and Anthropic, featuring a plugin system for extensibility and multi-modal support.","intents":["best CLI tool for LLMs","LLM integration for Python","how to use CLI for language models","top command-line tools for AI models","best plugins for LLM CLI tools"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","llm package installed","API keys or local model setup for at least one provider","SQLite3 (included in Python stdlib)","Write permissions to ~/.llm/ directory (default logs location)","llm package with database schema initialized","API keys for model providers","asyncio knowledge for async usage (optional but recommended)","llm package with embedding support","embedding model with batch support"],"failure_modes":["Abstractions add minimal overhead but require all models to implement the full interface even if some methods are no-ops","Provider-specific features (e.g., vision capabilities, function calling schemas) must be normalized to a common interface, potentially losing nuanced control","Async/sync duality requires maintaining two code paths, increasing maintenance burden","SQLite is single-writer, so high-concurrency scenarios (many simultaneous conversations) may experience lock contention","Database grows unbounded without manual pruning; no built-in retention policies or archival","Logging happens synchronously by default, adding latency to each LLM call (typically <10ms for local SQLite writes)","Python-only; no official SDKs for other languages","Async API requires understanding of Python's asyncio; synchronous API is simpler but blocks on I/O","Error handling is minimal; API errors are raised as exceptions without built-in retry logic","No built-in rate limiting or quota management; caller must implement these","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.692Z","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=llm-simon-willison","compare_url":"https://unfragile.ai/compare?artifact=llm-simon-willison"}},"signature":"DRBdgTCiCAg5pq3nx/8zJAiCf1kHw4qZkKJG+y3941WMNMPDP76Rnvb3hh5TcAATN6k/+pdIDBuM4qtHNzyJBw==","signedAt":"2026-06-20T15:57:10.501Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-simon-willison","artifact":"https://unfragile.ai/llm-simon-willison","verify":"https://unfragile.ai/api/v1/verify?slug=llm-simon-willison","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"}}