{"id":"simonw-llm","name":"llm","homepage":"https://llm.datasette.io","repo_url":"https://github.com/simonw/llm","category":"ai-ml","subcategories":[],"tags":["ai","llms","cli","python-library","embeddings","tool-calling","schemas","sqlite-logging","plugins"],"what_it_does":"llm is a Python library and command-line tool for interacting with large language models (both via remote APIs like OpenAI/Anthropic/Gemini and via locally installed/self-hosted models through plugins). It supports running prompts from the CLI, managing API keys, chat, embeddings, structured extraction (schemas), tool execution, and logging to SQLite.","use_cases":["CLI and scripted prompt execution","Interactive chat with multiple model providers","Generating and storing embeddings","Structured extraction from text/images using schemas","Embedding/using model tool-calling capabilities (granting tools to models)","Local/self-hosted model workflows via plugins","Logging prompts/responses to SQLite for later inspection"],"not_for":["Use as a general-purpose web service API without wrapping (primarily a CLI/library)","Environments requiring formal enterprise governance features (SLA, data residency guarantees) without additional infrastructure","Cases where you need a single standardized HTTP API/contract across all providers (it relies on provider-specific backends/plugins)"],"best_when":"You want a single CLI/Python interface to orchestrate multiple LLM providers and local models, including structured outputs and optional local execution via plugins.","avoid_when":"You require a turnkey hosted service with centralized auth, SLAs, or guaranteed data residency/compliance controls; you instead use a local CLI/library that will send prompts to whatever upstream providers you configure.","alternatives":["OpenAI/Anthropic/Gemini provider SDKs directly","LangChain","LlamaIndex","OpenAI-compatible clients such as openai-python + provider-specific wrappers","Ollama CLI / libraries for purely local model use","Datasette/SQLite tooling for separate logging pipelines"],"af_score":52.8,"security_score":56.5,"reliability_score":40.0,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T14:54:27.391009+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":true,"sdk_languages":["Python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Stored API keys managed via llm keys set (e.g., OpenAI/Gemini/Anthropic)","Passing keys via CLI options","Providing keys via environment variables"],"oauth":false,"scopes":false,"notes":"Authentication is primarily handled by the tool for upstream providers (typically API keys). No evidence of OAuth or fine-grained scopes in the provided material."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"The package itself is distributed as open-source; costs depend on the upstream LLM providers and any locally hosted infrastructure."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":52.8,"security_score":56.5,"reliability_score":40.0,"mcp_server_quality":0.0,"documentation_accuracy":80.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":80.0,"rate_limit_clarity":20.0,"tls_enforcement":70.0,"auth_strength":70.0,"scope_granularity":20.0,"dependency_hygiene":60.0,"secret_handling":60.0,"security_notes":"Security posture is partially inferable: it uses upstream APIs over network (likely HTTPS, but not explicitly stated in provided content). The tool supports storing and managing API keys and an option for env vars, which can be secure if implemented safely; however, no explicit evidence here is provided about secret redaction/logging or strict scope limitation. The package also supports SQLite logging, which may persist prompts/responses locally—this is a data-handling concern for sensitive workloads.","uptime_documented":0.0,"version_stability":70.0,"breaking_changes_history":50.0,"error_recovery":40.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["This is a CLI-first tool; as an agent you may need to call the Python API or shell out to the CLI (or rely on provider/plugin availability).","Behavior depends on installed plugins (e.g., llm-ollama, llm-gemini, llm-anthropic), so supported models/tools/headers can vary.","If you enable SQLite logging or schema/tool features, ensure your agent handles sensitive content appropriately to avoid unintended storage or leakage."]}}