{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llm","slug":"llm","name":"LLM","type":"cli","url":"https://llm.datasette.io/","page_url":"https://unfragile.ai/llm","categories":["llm-apis"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llm__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.invocation.via.unified.cli.interface","name":"multi-provider llm invocation via unified cli interface","description":"Abstracts away provider-specific API differences (OpenAI, Anthropic, Ollama, local models) behind a single `llm prompt` command, routing requests to configured model providers and normalizing response handling. Uses a plugin-based provider registry pattern where each provider implements a standard interface for authentication, request formatting, and response parsing, enabling seamless switching between remote APIs and local model servers without changing invocation syntax.","intents":["quickly test prompts against different LLM providers without writing provider-specific code","switch between OpenAI, Claude, and local Ollama models without changing scripts","integrate LLM calls into shell pipelines and automation workflows","avoid vendor lock-in by maintaining provider-agnostic prompt scripts"],"best_for":["DevOps engineers building LLM-powered CLI tools and scripts","researchers comparing model outputs across providers","solo developers prototyping LLM features before committing to a provider"],"limitations":["No built-in request batching — each invocation is a separate API call, adding latency for high-volume scenarios","Provider-specific features (vision, function calling, structured output) require manual provider selection and may not be uniformly exposed","Streaming responses require explicit flag (`--stream`) and may not work identically across all providers","No automatic retry logic or fallback provider support — failures require manual intervention"],"requires":["Python 3.8+","API keys for desired providers (OpenAI, Anthropic, etc.) or local Ollama instance running on default port","pip or uv package manager"],"input_types":["text prompts (stdin or command-line argument)","file paths (via `--input` flag)","structured JSON (for multi-turn conversations)"],"output_types":["plain text (stdout)","JSON (with `--json` flag)","streamed text (with `--stream` flag)"],"categories":["tool-use-integration","cli-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_1","uri":"capability://memory.knowledge.conversation.history.management.with.multi.turn.context","name":"conversation history management with multi-turn context","description":"Maintains conversation state across multiple CLI invocations using a local SQLite database, storing messages, model metadata, and conversation metadata. Each conversation is identified by a unique key, and the CLI automatically appends new user messages and retrieves prior context before sending to the LLM provider, enabling natural multi-turn interactions from the command line without manual context juggling.","intents":["maintain a persistent conversation with an LLM across multiple shell commands","review conversation history without re-running the model","branch conversations at specific points and explore alternative paths","export conversation logs for documentation or auditing"],"best_for":["interactive developers using LLMs for debugging or brainstorming via CLI","teams building LLM-powered automation that requires stateful interactions","researchers tracking model behavior across conversation turns"],"limitations":["SQLite storage is local-only — no built-in cloud sync or multi-machine conversation sharing","Context window management is manual — users must monitor token count and manually truncate old messages to avoid exceeding provider limits","No automatic conversation pruning — old conversations accumulate in the database and must be manually deleted","Branching conversations requires explicit CLI commands; no visual conversation tree UI"],"requires":["Python 3.8+","SQLite3 (included with Python)","Write permissions to `~/.llm/` directory for storing conversation database"],"input_types":["text prompts (appended to existing conversation)","conversation IDs (to resume or branch existing conversations)","JSON conversation exports (for import/restore)"],"output_types":["conversation history (JSON or formatted text)","new LLM response (appended to conversation)","conversation metadata (timestamps, model used, token counts)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_10","uri":"capability://safety.moderation.api.key.and.credential.management.with.secure.storage","name":"api key and credential management with secure storage","description":"Manages API keys and credentials for multiple LLM providers using secure local storage (encrypted files or OS credential stores like macOS Keychain, Windows Credential Manager). Supports both environment variables and interactive prompts for credential entry, with automatic credential rotation and expiration tracking.","intents":["securely store API keys without hardcoding them in scripts or config files","manage credentials for multiple providers in a single place","share LLM CLI configurations across team members without exposing secrets","audit and rotate API keys for compliance purposes"],"best_for":["security-conscious developers and teams","organizations with credential management policies","teams sharing LLM configurations via version control","developers building LLM-powered applications with multiple providers"],"limitations":["Credential storage is local-only — no built-in cloud sync or backup","OS credential stores vary in security and availability — Keychain/Credential Manager may not be available on all systems","No built-in credential rotation — users must manually update keys","Credential expiration tracking is manual — no automatic alerts or enforcement","Compromised local system exposes all stored credentials — no additional encryption layer"],"requires":["Python 3.8+","OS credential store (Keychain on macOS, Credential Manager on Windows, or fallback to encrypted files)","Write permissions to `~/.llm/` directory"],"input_types":["API keys (from environment variables or interactive prompts)","provider names (to associate credentials)","credential metadata (expiration dates, rotation schedules)"],"output_types":["stored credentials (used internally by CLI)","credential status reports (for auditing)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_11","uri":"capability://tool.use.integration.python.library.api.for.programmatic.llm.access","name":"python library api for programmatic llm access","description":"Exposes the CLI functionality as a Python library with a high-level API for invoking LLMs, managing conversations, and accessing plugins. The library wraps the CLI's provider abstraction and conversation management, enabling developers to build Python applications that leverage the same multi-provider support and configuration system as the CLI.","intents":["use the LLM CLI's multi-provider abstraction in Python applications without reimplementing provider logic","access conversation history and management from Python code","build Python tools that share configuration with the CLI","integrate LLM functionality into larger Python applications without external dependencies"],"best_for":["Python developers building LLM-powered applications","teams using both CLI and Python for LLM tasks","developers wanting to avoid dependency on langchain or llamaindex","researchers building custom LLM workflows in Python"],"limitations":["Library API is less mature than the CLI — fewer features and less documentation","No async/await support — all operations are synchronous, blocking the event loop","Limited integration with popular Python LLM frameworks (langchain, llamaindex) — requires custom adapters","No built-in support for advanced features like function calling or vision — requires manual provider-specific code","Documentation is sparse — requires reading source code or CLI documentation to understand API"],"requires":["Python 3.8+","llm package installed via pip","Same provider credentials and configuration as CLI"],"input_types":["prompts (strings or template objects)","model names (strings or model objects)","conversation IDs (for resuming conversations)"],"output_types":["LLM responses (strings or structured objects)","conversation objects (with history and metadata)","model metadata (capabilities, pricing, etc.)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_2","uri":"capability://automation.workflow.model.aliasing.and.configuration.management","name":"model aliasing and configuration management","description":"Allows users to define named aliases for model configurations (e.g., `gpt4-vision` → `gpt-4-turbo` with specific system prompts and parameters), stored in a YAML or JSON config file. The CLI resolves aliases at invocation time, enabling users to swap model implementations globally without changing scripts, and supports per-alias configuration of temperature, max tokens, system prompts, and provider-specific parameters.","intents":["define a shorthand alias for a complex model configuration (e.g., `llm prompt --model my-coder` instead of specifying all parameters)","globally switch all scripts from GPT-4 to Claude by updating a single config entry","maintain different model configurations for different use cases (e.g., `fast` vs `accurate`)","share model configurations across team members via version control"],"best_for":["teams standardizing on model configurations across multiple scripts and projects","individual developers managing multiple model setups for different tasks","organizations wanting to enforce model choices and parameters via config"],"limitations":["No built-in validation of alias configurations — invalid parameters fail silently at invocation time","Aliases are stored locally; no built-in mechanism for syncing configurations across machines or team members","No version control for alias history — changes to aliases overwrite previous definitions","Parameter inheritance is flat — no support for composing aliases (e.g., base alias + overrides)"],"requires":["Python 3.8+","Write permissions to `~/.llm/` config directory","YAML or JSON knowledge for editing config files"],"input_types":["YAML/JSON config files (defining aliases)","command-line flags (overriding alias parameters at invocation time)"],"output_types":["resolved model configuration (used internally)","config file (for export/sharing)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_3","uri":"capability://text.generation.language.prompt.templating.with.variable.substitution","name":"prompt templating with variable substitution","description":"Supports Jinja2-style templating in prompts, allowing users to define variables (e.g., `{{filename}}`, `{{user_input}}`) that are substituted at invocation time from command-line arguments, environment variables, or stdin. Templates can include conditional logic and loops, enabling dynamic prompt generation without writing custom code.","intents":["generate prompts dynamically based on file contents or user input without shell escaping gymnastics","create reusable prompt templates that adapt to different inputs","build parameterized prompts for code generation or analysis tasks","avoid prompt injection vulnerabilities by separating template logic from user input"],"best_for":["developers building LLM-powered CLI tools with dynamic prompts","teams creating standardized prompt templates for common tasks","security-conscious users wanting to separate template logic from untrusted input"],"limitations":["Jinja2 templating adds complexity — users must learn template syntax beyond basic variable substitution","No built-in validation of template syntax — invalid templates fail at runtime","Template rendering happens client-side only — no server-side template evaluation or caching","Large templates with complex logic can become difficult to maintain and debug"],"requires":["Python 3.8+","Jinja2 library (installed as dependency)","Understanding of Jinja2 template syntax"],"input_types":["Jinja2 template strings (inline or from files)","variable values (from CLI args, environment, or stdin)","file contents (for inclusion in templates)"],"output_types":["rendered prompt text (sent to LLM)","template validation errors (if syntax is invalid)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_4","uri":"capability://tool.use.integration.local.model.execution.via.ollama.integration","name":"local model execution via ollama integration","description":"Provides native integration with Ollama, a local LLM runtime, allowing users to run open-source models (Llama 2, Mistral, etc.) on their machine without cloud API calls. The CLI auto-detects Ollama instances running on localhost:11434, manages model downloads and caching, and routes requests to the appropriate local model with full streaming support.","intents":["run LLM inference locally without sending data to cloud providers","experiment with open-source models without API costs","maintain privacy for sensitive prompts and data","integrate local LLMs into offline-first workflows"],"best_for":["privacy-conscious developers and organizations","researchers experimenting with open-source model variants","teams with limited cloud budgets or API rate limits","developers building offline-capable LLM applications"],"limitations":["Inference speed depends on local hardware — GPU acceleration requires CUDA/Metal support and proper driver setup","Model quality and capability are lower than frontier cloud models (GPT-4, Claude) for most tasks","Ollama must be running separately — no built-in process management or auto-start","No built-in model versioning or rollback — updating a model overwrites the previous version","Memory usage scales with model size — large models (70B+) require significant RAM or swap"],"requires":["Python 3.8+","Ollama installed and running (https://ollama.ai)","Sufficient disk space for model downloads (varies by model, typically 4-40GB)","GPU with CUDA/Metal support recommended for reasonable inference speed"],"input_types":["text prompts","model names (e.g., `llama2`, `mistral`)","streaming request flags"],"output_types":["streamed or buffered text responses","model metadata (name, size, parameters)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_5","uri":"capability://data.processing.analysis.batch.prompt.execution.with.result.aggregation","name":"batch prompt execution with result aggregation","description":"Processes multiple prompts in sequence or parallel, reading from a file or stdin (one prompt per line or JSON array), and aggregates results into a structured output format (JSON, CSV, or plain text). Supports batching across different models and configurations, with built-in progress reporting and error handling for individual prompt failures.","intents":["classify or analyze a list of documents/texts using a single LLM prompt","generate variations or alternatives for multiple inputs in a single operation","run A/B tests comparing model outputs across different configurations","process large datasets through an LLM without manual iteration"],"best_for":["data analysts processing datasets through LLMs","teams running batch classification or tagging jobs","researchers comparing model outputs at scale","developers building data pipelines with LLM steps"],"limitations":["No built-in rate limiting — batch jobs may hit API rate limits; users must implement external throttling","Failures in individual prompts don't halt the batch — partial results may be incomplete or inconsistent","No automatic retry logic for failed prompts — requires manual re-running of failed items","Memory usage scales with batch size — very large batches (10k+ items) may exhaust available RAM","No built-in cost estimation — users cannot predict API costs before running batch jobs"],"requires":["Python 3.8+","Input file with prompts (one per line or JSON array)","Sufficient API quota for batch size × model cost"],"input_types":["newline-delimited text (one prompt per line)","JSON array of prompt objects","CSV file with prompt column"],"output_types":["JSON array of results (with model, prompt, response)","CSV file (one result per row)","plain text (one response per line)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_6","uri":"capability://data.processing.analysis.response.formatting.and.structured.output.extraction","name":"response formatting and structured output extraction","description":"Parses LLM responses and formats them into structured outputs (JSON, YAML, CSV, markdown tables) using pattern matching and optional JSON schema validation. Supports extracting specific fields from free-form responses via regex or JSON path queries, enabling downstream tools to consume LLM outputs without manual parsing.","intents":["extract structured data (e.g., entities, classifications) from unstructured LLM responses","validate LLM outputs against a schema before using them in automation","convert LLM responses into formats compatible with downstream tools (databases, APIs)","ensure consistent output format across different models and prompts"],"best_for":["developers building LLM-powered data pipelines","teams integrating LLM outputs into structured workflows","data engineers extracting information from unstructured text","automation engineers ensuring LLM outputs are machine-readable"],"limitations":["Schema validation requires explicit schema definition — no automatic schema inference","Regex and JSON path extraction are brittle — changes to LLM output format may break parsing","No built-in error recovery — invalid outputs fail rather than attempting repair or fallback","Complex transformations require custom Python code — CLI flags support only basic formatting","No streaming support for structured output — entire response must be buffered before parsing"],"requires":["Python 3.8+","JSON schema (optional, for validation)","Understanding of regex or JSON path syntax (for extraction)"],"input_types":["raw LLM response text","JSON schema (for validation)","regex patterns or JSON path queries (for extraction)"],"output_types":["JSON (structured or extracted fields)","YAML","CSV","markdown tables","validation errors (if schema validation fails)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_7","uri":"capability://tool.use.integration.plugin.system.for.custom.providers.and.extensions","name":"plugin system for custom providers and extensions","description":"Allows users to extend the CLI with custom LLM providers, output formatters, and commands by writing Python plugins that implement standard interfaces. Plugins are discovered from a plugins directory and registered at runtime, enabling third-party integrations without modifying core code. Supports both built-in plugins (OpenAI, Anthropic, Ollama) and user-defined plugins with full access to CLI context.","intents":["add support for a custom LLM provider or internal API without forking the project","implement organization-specific prompt templates or output formatting","integrate with internal tools or services (logging, monitoring, secret management)","build domain-specific LLM workflows on top of the CLI foundation"],"best_for":["organizations with custom LLM infrastructure or APIs","developers building LLM-powered tools on top of the CLI","teams wanting to extend the CLI without maintaining a fork","researchers implementing novel LLM interaction patterns"],"limitations":["Plugin API is not versioned — breaking changes to core interfaces may require plugin updates","No built-in plugin dependency management — plugins must manage their own dependencies","Plugin discovery is filesystem-based — no central registry or package manager integration","Limited plugin sandboxing — plugins have full access to the CLI context and system resources","Documentation for plugin development is minimal — requires reading source code to understand interfaces"],"requires":["Python 3.8+","Understanding of Python class inheritance and decorators","Knowledge of the plugin interface (documented in source code)"],"input_types":["Python plugin files (implementing standard interfaces)","plugin configuration (via CLI args or config files)"],"output_types":["extended CLI commands","custom provider implementations","custom output formatters"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_8","uri":"capability://text.generation.language.streaming.response.output.with.real.time.display","name":"streaming response output with real-time display","description":"Streams LLM responses token-by-token to stdout as they arrive, rather than buffering the entire response before display. Supports both raw streaming (tokens printed as-is) and formatted streaming (with progress indicators, timing information, and token counts), enabling real-time feedback for long-running inferences.","intents":["see LLM responses in real-time without waiting for the full response to complete","monitor inference progress and estimate completion time","integrate streaming responses into interactive CLI applications","reduce perceived latency for user-facing LLM interactions"],"best_for":["interactive developers using LLMs for brainstorming or debugging","users with slow network connections or long-running inferences","teams building real-time LLM-powered applications","researchers analyzing token-level model behavior"],"limitations":["Streaming requires provider support — not all providers or models support streaming responses","Token counts are unavailable until the response completes — real-time cost estimation is impossible","Streaming output cannot be easily captured and parsed — structured output extraction is difficult","Terminal rendering may be slow for very high token rates — output may lag behind actual generation","Interrupting a stream (Ctrl+C) may leave partial tokens in the output"],"requires":["Python 3.8+","LLM provider with streaming support (OpenAI, Anthropic, Ollama)","Terminal with ANSI escape sequence support (for formatted output)"],"input_types":["prompts (with `--stream` flag)","formatting options (progress indicators, timing)"],"output_types":["streamed text (to stdout)","timing and token count information (if requested)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm__cap_9","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"Calculates token counts for prompts and responses using provider-specific tokenizers (e.g., tiktoken for OpenAI, claude-tokenizer for Anthropic), and estimates API costs based on current pricing. Supports both pre-execution estimation (for prompt planning) and post-execution reporting (for cost tracking and auditing).","intents":["estimate API costs before running expensive prompts or batch jobs","track cumulative API spending across multiple invocations","optimize prompts to reduce token usage and costs","audit LLM usage for billing and compliance purposes"],"best_for":["teams managing LLM API budgets and costs","developers optimizing prompts for cost efficiency","organizations tracking LLM spending for chargeback or compliance","researchers comparing cost-effectiveness of different models"],"limitations":["Token counts are approximate — actual provider counts may differ slightly due to tokenizer differences","Pricing data must be manually updated as providers change rates — no automatic price synchronization","Cost estimation assumes standard pricing — custom pricing agreements or discounts are not reflected","Token counting adds latency — pre-execution estimation requires a separate API call or local tokenizer","No built-in cost alerts or spending limits — users must manually monitor costs"],"requires":["Python 3.8+","Provider-specific tokenizer libraries (tiktoken for OpenAI, etc.)","Current pricing information (manually configured or fetched from provider)"],"input_types":["prompts (for token counting)","model names (to determine pricing)","responses (for post-execution cost calculation)"],"output_types":["token counts (prompt, response, total)","cost estimates (in USD or other currency)","cost reports (aggregated across multiple invocations)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API keys for desired providers (OpenAI, Anthropic, etc.) or local Ollama instance running on default port","pip or uv package manager","SQLite3 (included with Python)","Write permissions to `~/.llm/` directory for storing conversation database","OS credential store (Keychain on macOS, Credential Manager on Windows, or fallback to encrypted files)","Write permissions to `~/.llm/` directory","llm package installed via pip","Same provider credentials and configuration as CLI","Write permissions to `~/.llm/` config directory"],"failure_modes":["No built-in request batching — each invocation is a separate API call, adding latency for high-volume scenarios","Provider-specific features (vision, function calling, structured output) require manual provider selection and may not be uniformly exposed","Streaming responses require explicit flag (`--stream`) and may not work identically across all providers","No automatic retry logic or fallback provider support — failures require manual intervention","SQLite storage is local-only — no built-in cloud sync or multi-machine conversation sharing","Context window management is manual — users must monitor token count and manually truncate old messages to avoid exceeding provider limits","No automatic conversation pruning — old conversations accumulate in the database and must be manually deleted","Branching conversations requires explicit CLI commands; no visual conversation tree UI","Credential storage is local-only — no built-in cloud sync or backup","OS credential stores vary in security and availability — Keychain/Credential Manager may not be available on all systems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.25,"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":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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","compare_url":"https://unfragile.ai/compare?artifact=llm"}},"signature":"EZ8AAZ3GjAt9iNvRt2bzN74TuNy/dCWJVubszWn4wcxWaT5Xm3oOKqQyjuVp3ayRV7YoioUYt7rgXvNvRL+QBA==","signedAt":"2026-06-21T22:14:19.731Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm","artifact":"https://unfragile.ai/llm","verify":"https://unfragile.ai/api/v1/verify?slug=llm","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"}}