{"id":"dnhkng-glados","name":"GLaDOS","homepage":null,"repo_url":"https://github.com/dnhkng/GLaDOS","category":"ai-ml","subcategories":[],"tags":["ai-ml","voice-assistant","multimodal","mcp","self-hosted","python","tool-use","vision","memory","autonomy"],"what_it_does":"GLaDOS is an on-device (Python) voice assistant/agent framework that combines speech recognition, voice activity detection, text-to-speech, vision processing, an LLM core, and an MCP-based tool system to enable proactive/autonomous behavior (e.g., responding to camera/audio/time events) with long-term memory and configurable LLM backends (e.g., Ollama or OpenAI-compatible APIs).","use_cases":["Building a voice assistant with proactive (non-wake-word) behavior","Home automation or system control via MCP tools","Adding local vision (VLM) and reacting to scene changes","Creating an LLM-driven multi-module assistant with memory and tool use","Running an interactive text UI or voice UI for experimentation"],"not_for":["Production deployments requiring a managed hosted service with strong operational guarantees","Environments where collecting/storing user audio, vision frames, or conversation history is unacceptable","Organizations needing standardized compliance/SOC2-like assurances from a vendor-hosted API","Security-sensitive integrations without careful threat modeling and sandboxing of tool execution"],"best_when":"You want a self-hosted, hackable multimodal agent you can run locally (often with Ollama) and extend via MCP tools, and you can accept that tool execution and model backends require careful configuration.","avoid_when":"You need a hardened, turnkey assistant service with strong governance, minimal risk from tool calls, and well-specified API contracts; or you cannot control what tools can do once the agent is allowed to execute them.","alternatives":["Home Assistant + local voice (e.g., Whisper) + an LLM integration","OpenAI/Anthropic tool-using agents with custom function calling","LangChain/LangGraph agent frameworks (self-hosted) with MCP/Tool patterns","Ollama-compatible agent demos that provide REST/SDK-based tool calling"],"af_score":40.2,"security_score":37.5,"reliability_score":20.0,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T14:58:15.629710+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":true,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Optional API key for OpenAI-compatible completion backend (glados_config.yaml 'api_key')","Potential MCP server authentication depends on the MCP server implementation (not specified in README excerpt)"],"oauth":false,"scopes":false,"notes":"Authentication is primarily delegated to the configured LLM backend (e.g., OpenAI-compatible API key if needed) and to any MCP servers used for tools; the project configuration mentions an api_key field but does not describe OAuth/scopes or auth flows for MCP within the provided content."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No hosted pricing described; costs would depend on local compute and/or the selected LLM backend (Ollama local vs external OpenAI-compatible API)."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":40.2,"security_score":37.5,"reliability_score":20.0,"mcp_server_quality":55.0,"documentation_accuracy":60.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":70.0,"rate_limit_clarity":0.0,"tls_enforcement":40.0,"auth_strength":35.0,"scope_granularity":20.0,"dependency_hygiene":45.0,"secret_handling":50.0,"security_notes":"Security posture cannot be fully determined from the provided README/manifest. TLS enforcement for any HTTP-based MCP or model calls is not specified (depends on configured URLs). Tool execution capability introduces substantial risk if MCP tools can access sensitive systems; the README does not describe permissioning, allowlists, or sandboxing. Dependency list is extensive; no vulnerability/SBOM/CVE posture is provided. An api_key field exists for the LLM backend, but secret handling practices (no logging, redaction) are not described.","uptime_documented":0.0,"version_stability":30.0,"breaking_changes_history":30.0,"error_recovery":20.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Tool execution is powerful (home automation/system actions); agent behavior and tool permissions should be constrained/sandboxed.","Concurrency/autonomy loop may issue tool calls while user speech is handled via a priority lane; race conditions or unintended simultaneous actions are possible.","LLM backend configuration (Ollama vs OpenAI-compatible) and streaming/latency behavior may affect timing-sensitive audio/vision flows."]}}