{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-holmesgpt--holmesgpt","slug":"holmesgpt--holmesgpt","name":"holmesgpt","type":"agent","url":"https://holmesgpt.dev/","page_url":"https://unfragile.ai/holmesgpt--holmesgpt","categories":["ai-agents"],"tags":["aiops","chatbot","chatops","devops","devops-tools","incident","incident-management","incident-response","jira","kubernetes","llm","llm-agent","llm-framework","llms","monitoring","observability","prometheus","site-reliability-engineering","slack","sre"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-holmesgpt--holmesgpt__cap_0","uri":"capability://planning.reasoning.agentic.loop.orchestration.with.tool.calling","name":"agentic-loop-orchestration-with-tool-calling","description":"Executes a closed-loop reasoning cycle that alternates between LLM inference and tool execution, using structured tool-calling APIs (OpenAI, Anthropic native function calling) to invoke observability and infrastructure tools. The loop maintains conversation state across iterations, processes tool outputs through transformers, and implements context window management to handle large observability datasets. Tool execution is gated by an approval/security model that validates tool calls before execution against configured RBAC policies.","intents":["I want an AI agent to autonomously investigate production incidents by querying monitoring systems and executing diagnostic commands","I need the agent to reason about tool outputs and decide what to query next without human intervention between steps","I want to control which tools the agent can execute and require approval for sensitive operations"],"best_for":["SRE teams automating incident investigation workflows","DevOps teams integrating AI-driven troubleshooting into existing incident response processes","Organizations with complex multi-tool observability stacks requiring unified AI access"],"limitations":["Context window constraints limit the amount of observability data that can be analyzed in a single loop iteration; large datasets require pagination or summarization","Tool approval model adds latency for sensitive operations requiring human review","Loop depth is bounded by token limits and model context window; very deep investigation chains may require external state persistence"],"requires":["Python 3.9+","API key for OpenAI (GPT-4/3.5) or Anthropic (Claude) or compatible LLM provider","At least one configured toolset (Kubernetes, Prometheus, cloud provider, etc.)","Network access to observability platforms and infrastructure APIs"],"input_types":["natural language queries","alert payloads (Prometheus AlertManager format)","issue descriptions (Jira, GitHub Issues)","structured investigation requests"],"output_types":["investigation results (structured JSON)","reasoning traces (chain-of-thought explanations)","tool execution logs","recommended remediation actions"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_1","uri":"capability://data.processing.analysis.multi.source.observability.data.aggregation","name":"multi-source-observability-data-aggregation","description":"Aggregates real-time observability data from heterogeneous sources (Kubernetes API, Prometheus, Grafana, Loki, Tempo, DataDog, cloud provider APIs) through a pluggable toolset architecture. Each toolset encapsulates source-specific query logic, authentication, and data transformation. The system uses a factory-based loader (holmes/plugins/toolsets/__init__.py) to dynamically instantiate toolsets from configuration, and applies tool output transformers to normalize disparate data formats into a consistent schema for LLM processing.","intents":["I want to query multiple observability platforms from a single agent without writing custom integrations","I need to correlate metrics, logs, and traces across different systems to understand root causes","I want to extend the agent with custom data sources without modifying core code"],"best_for":["Organizations with polyglot observability stacks (Prometheus + DataDog + cloud-native tools)","Teams building custom observability integrations for proprietary monitoring systems","SRE teams needing unified incident investigation across infrastructure, applications, and business metrics"],"limitations":["Query latency depends on slowest data source; no built-in query parallelization or timeout management","Data transformation overhead adds ~50-200ms per toolset invocation depending on result set size","Custom toolsets require Python development; no low-code toolset builder UI","Authentication credentials must be managed externally (environment variables, Kubernetes secrets); no built-in credential rotation"],"requires":["Python 3.9+","Valid credentials/API keys for each observability platform (Prometheus, Grafana, DataDog, etc.)","Network connectivity to observability platform APIs","For Kubernetes toolsets: kubectl access and kubeconfig or in-cluster service account"],"input_types":["observability platform credentials (API keys, bearer tokens, kubeconfig)","query parameters (time ranges, label selectors, resource names)","custom toolset Python modules"],"output_types":["time-series metrics (Prometheus format)","structured logs (JSON)","distributed traces (Tempo/Jaeger format)","Kubernetes resource manifests (YAML)","cloud resource metadata (JSON)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_10","uri":"capability://text.generation.language.interactive.cli.and.conversational.interface","name":"interactive-cli-and-conversational-interface","description":"Provides an interactive CLI interface (holmes/interactive.py) for conversational investigation with multi-turn dialogue support. The CLI maintains conversation history, supports tool execution with user approval workflows, displays investigation results with formatting, and integrates with the agentic loop for iterative investigation. Supports both interactive mode (human-in-the-loop) and batch mode (automated investigation) through the same codebase.","intents":["I want to interactively investigate incidents through a conversational interface","I need to approve tool executions before they run (for sensitive operations)","I want to iterate on investigation with follow-up questions and refinements"],"best_for":["SRE teams preferring interactive investigation workflows","Organizations requiring human approval for sensitive tool executions","Teams using HolmesGPT as a troubleshooting assistant rather than fully automated incident response"],"limitations":["Interactive mode requires human presence; not suitable for fully automated incident response","Tool approval workflow adds latency; not suitable for time-critical incidents","CLI interface is text-based; no graphical visualization of investigation results","Conversation history is not persisted; restarting the CLI loses investigation context"],"requires":["Python 3.9+","Terminal with ANSI color support (for formatted output)","At least one observability toolset configured"],"input_types":["natural language queries (typed in CLI)","tool approval responses (yes/no)","follow-up questions"],"output_types":["formatted investigation results (text with colors)","tool execution logs","conversation history"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_11","uri":"capability://tool.use.integration.rest.api.and.streaming.server.interface","name":"rest-api-and-streaming-server-interface","description":"Exposes investigation capabilities through a REST API (server.py) with streaming support for long-running investigations. The API supports investigation triggering (alerts, issues, custom queries), result polling or streaming via Server-Sent Events (SSE), and webhook integration for alert/issue sources. Implements authentication, rate limiting, and request validation. Supports both synchronous (request-response) and asynchronous (streaming) investigation patterns.","intents":["I want to trigger investigations programmatically via REST API","I need to stream investigation results in real-time as they're discovered","I want to integrate HolmesGPT into existing incident response platforms via webhooks"],"best_for":["Teams integrating HolmesGPT into incident response platforms (PagerDuty, Opsgenie, custom systems)","Organizations building custom UIs or dashboards on top of HolmesGPT","Teams requiring programmatic access to investigation capabilities"],"limitations":["Streaming adds complexity; SSE connections may be dropped by proxies or load balancers","API rate limiting may throttle high-volume investigation requests","No built-in authentication beyond API keys; requires external auth layer for fine-grained access control","Webhook delivery is not guaranteed; failed deliveries require external retry logic"],"requires":["Python 3.9+","HTTP server (Flask, FastAPI, or similar; included in dependencies)","Network access to HolmesGPT server","Optional: TLS/HTTPS for secure communication"],"input_types":["HTTP POST requests with investigation parameters (alert payload, issue ID, custom query)","webhook payloads from alert/issue sources"],"output_types":["HTTP JSON responses with investigation results","Server-Sent Events (SSE) stream with investigation progress","webhook deliveries to configured destinations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_12","uri":"capability://safety.moderation.tool.approval.and.security.model","name":"tool-approval-and-security-model","description":"Implements a tool approval and security model that gates tool execution based on RBAC policies and approval workflows. The system supports multiple approval modes: auto-approve (for safe tools), require-approval (for sensitive operations like pod deletion), and deny (for prohibited tools). Integrates with Kubernetes RBAC and custom authorization providers. Logs all tool executions for audit trails and supports dry-run mode for previewing tool effects without execution.","intents":["I want to prevent the agent from executing dangerous operations (e.g., deleting pods) without approval","I need to enforce RBAC policies on tool execution based on user roles","I want to audit all tool executions for compliance and troubleshooting"],"best_for":["Organizations with strict security and compliance requirements","Teams requiring human approval for sensitive infrastructure operations","Enterprises needing audit trails for tool execution"],"limitations":["Approval workflows add latency; not suitable for time-critical operations","RBAC enforcement depends on correct policy configuration; misconfigured policies may allow unintended access","Dry-run mode may not accurately predict tool effects (e.g., side effects not visible in dry-run)","Audit logging adds storage overhead; large-scale deployments may require external log aggregation"],"requires":["Python 3.9+","RBAC policy configuration (Kubernetes RBAC or custom authorization provider)","Optional: external audit log storage (Elasticsearch, S3, etc.)"],"input_types":["tool execution requests (tool name, parameters)","user identity and roles","RBAC policies"],"output_types":["approval decisions (approved, denied, requires-approval)","audit logs (tool execution, approval status, user identity)","dry-run results (predicted tool effects)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_13","uri":"capability://automation.workflow.scheduled.investigation.and.automation","name":"scheduled-investigation-and-automation","description":"Implements scheduled investigation capabilities for proactive health checks and periodic analysis. The system supports cron-like scheduling (e.g., daily health checks on critical services), automatic investigation triggering based on conditions (e.g., investigate when error rate exceeds threshold), and result persistence to external systems (Jira, Slack, databases). Integrates with the agentic loop for investigation execution and supports custom investigation templates per schedule.","intents":["I want to run daily health checks on critical services and create tickets for degradation","I need to automatically investigate when metrics exceed thresholds","I want to schedule periodic analysis of system behavior and create trend reports"],"best_for":["SRE teams performing proactive health monitoring","Organizations automating routine investigation tasks","Teams creating trend reports and historical analysis"],"limitations":["Scheduled investigations require external scheduler (Kubernetes CronJob, GitHub Actions, etc.); no built-in scheduler","Investigation results are created even if no issues are found; may create noise in issue tracking systems","Condition-based triggering requires custom logic; no built-in condition evaluation engine","Scheduled investigations may consume significant LLM API quota; cost optimization required"],"requires":["Python 3.9+","External scheduler (Kubernetes CronJob, GitHub Actions, cron, etc.)","At least one observability toolset configured","Optional: Jira, Slack, or database integration for result persistence"],"input_types":["schedule definitions (cron expressions or custom schedules)","investigation templates (service names, query parameters)","condition definitions (metric thresholds, error rates)"],"output_types":["investigation results (health status, recommendations)","Jira tickets or Slack messages (if configured)","trend reports and historical data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_14","uri":"capability://tool.use.integration.custom.toolset.development.and.plugin.system","name":"custom-toolset-development-and-plugin-system","description":"Provides a plugin system for developing custom toolsets that extend HolmesGPT with domain-specific tools. The system uses a base Toolset class and factory pattern (holmes/plugins/toolsets/__init__.py) to enable custom tool definitions without modifying core code. Custom toolsets can integrate with proprietary systems (internal APIs, custom databases, specialized monitoring tools) and are loaded dynamically from configuration. Includes documentation and examples for common integration patterns.","intents":["I want to integrate HolmesGPT with proprietary monitoring or incident management systems","I need to add custom tools for domain-specific investigation (e.g., database query tools, payment system health checks)","I want to extend HolmesGPT without forking the codebase"],"best_for":["Organizations with proprietary systems requiring custom integrations","Teams building domain-specific investigation tools","Enterprises extending HolmesGPT for specialized use cases"],"limitations":["Custom toolset development requires Python programming; no low-code toolset builder","Toolset testing and validation are the responsibility of the developer; no built-in testing framework","Custom toolsets must handle their own authentication and error handling","Toolset performance impacts overall investigation latency; poorly optimized toolsets may slow investigations"],"requires":["Python 3.9+","Understanding of HolmesGPT toolset architecture and base classes","Access to external systems being integrated (APIs, databases, etc.)"],"input_types":["custom toolset Python modules","toolset configuration (tool names, parameters, authentication)"],"output_types":["custom tool outputs (domain-specific data structures)","tool execution logs and errors"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_15","uri":"capability://tool.use.integration.mcp.server.integration.and.deployment","name":"mcp-server-integration-and-deployment","description":"Implements Model Context Protocol (MCP) server support, enabling HolmesGPT to be deployed as an MCP server and integrated with other MCP clients (Claude Desktop, other LLM applications). The MCP integration exposes HolmesGPT tools as MCP resources, enabling external LLM applications to invoke investigations without direct API calls. Supports both standalone MCP server deployment and embedded MCP server within HolmesGPT.","intents":["I want to use HolmesGPT tools from Claude Desktop or other MCP-compatible applications","I need to integrate HolmesGPT investigations into other LLM workflows","I want to expose HolmesGPT as a service that other AI agents can invoke"],"best_for":["Teams using Claude Desktop or other MCP-compatible LLM applications","Organizations building multi-agent systems with HolmesGPT as a specialized agent","Enterprises integrating HolmesGPT into broader AI/LLM platforms"],"limitations":["MCP server deployment adds operational complexity; requires separate server instance and management","MCP protocol overhead may add latency compared to direct API calls","MCP client support varies across LLM applications; not all applications support all MCP features","Authentication and authorization must be configured at the MCP server level"],"requires":["Python 3.9+","MCP-compatible client (Claude Desktop, custom MCP client, etc.)","Network access to MCP server endpoint"],"input_types":["MCP resource requests (investigation parameters)","MCP tool calls (investigation triggering)"],"output_types":["MCP resource responses (investigation results)","MCP tool execution results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_16","uri":"capability://tool.use.integration.robusta.platform.integration.and.automation","name":"robusta-platform-integration-and-automation","description":"Integrates with Robusta platform (the company behind HolmesGPT) for enhanced automation, alerting, and incident management capabilities. The integration enables bi-directional data flow between HolmesGPT and Robusta platform, automatic alert routing to HolmesGPT for investigation, and result synchronization back to Robusta for incident tracking. Supports Robusta-specific features like playbook execution and alert enrichment.","intents":["I want to integrate HolmesGPT investigations with Robusta platform for unified incident management","I need to automatically route Robusta alerts to HolmesGPT for investigation","I want to synchronize investigation results back to Robusta for incident tracking"],"best_for":["Organizations using Robusta platform for incident management","Teams leveraging Robusta playbooks and alert enrichment with HolmesGPT investigations","Enterprises with Robusta-based incident response workflows"],"limitations":["Robusta integration requires Robusta platform subscription; not available in open-source deployments","Integration complexity depends on Robusta platform version and features","Bi-directional sync may introduce consistency issues if not carefully managed"],"requires":["Python 3.9+","Robusta platform instance (cloud or self-hosted)","Robusta API credentials and integration configuration"],"input_types":["Robusta alert payloads","Robusta incident metadata"],"output_types":["investigation results synchronized to Robusta","incident updates and enrichment"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_2","uri":"capability://tool.use.integration.kubernetes.native.investigation.toolset","name":"kubernetes-native-investigation-toolset","description":"Provides a specialized toolset for Kubernetes cluster investigation with tools for querying cluster state (nodes, pods, deployments, services), analyzing resource health, executing diagnostic commands in containers, and retrieving logs. The toolset uses the Kubernetes Python client library to interact with the cluster API, supports both in-cluster (service account) and out-of-cluster (kubeconfig) authentication, and implements resource filtering by namespace, label selectors, and resource type. Tool outputs are transformed into structured formats suitable for LLM analysis.","intents":["I want the agent to investigate pod crashes by querying pod status, events, and logs without manual kubectl commands","I need the agent to diagnose node issues by checking node conditions, resource utilization, and pod scheduling status","I want the agent to correlate Kubernetes events with application behavior to identify root causes"],"best_for":["Kubernetes cluster operators and SREs automating incident investigation","Teams running microservices on Kubernetes needing AI-driven troubleshooting","Organizations with multi-cluster Kubernetes deployments requiring unified investigation"],"limitations":["Limited to Kubernetes API capabilities; cannot execute arbitrary commands in containers (requires separate exec toolset)","Large clusters (1000+ pods) may experience query latency or timeout issues when listing resources without label filters","No built-in support for multi-cluster investigation; requires separate agent instances per cluster or custom federation logic","RBAC enforcement depends on service account permissions; agent can only access resources the service account is authorized for"],"requires":["Python 3.9+","Kubernetes cluster (1.20+)","kubeconfig file or in-cluster service account with read permissions on cluster resources","Python kubernetes client library (included in dependencies)"],"input_types":["Kubernetes resource names (pod names, deployment names, node names)","label selectors (e.g., 'app=myapp,env=prod')","namespace names","time ranges for log queries"],"output_types":["Kubernetes resource manifests (YAML/JSON)","pod logs (text)","cluster events (structured JSON)","node status and metrics","deployment/statefulset status"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_3","uri":"capability://data.processing.analysis.prometheus.metrics.querying.and.analysis","name":"prometheus-metrics-querying-and-analysis","description":"Integrates Prometheus as a native observability data source through a specialized toolset that executes PromQL queries, retrieves time-series metrics, and analyzes metric trends. The toolset handles Prometheus API authentication, query execution with configurable time ranges and step intervals, and transforms metric results into structured formats. Supports both instant queries (current metric values) and range queries (time-series data), enabling the agent to analyze metric patterns and anomalies.","intents":["I want the agent to query CPU, memory, and network metrics to diagnose resource contention issues","I need the agent to analyze metric trends over time to identify performance degradation patterns","I want the agent to correlate metrics from multiple services to find the root cause of latency spikes"],"best_for":["SRE teams using Prometheus for infrastructure and application monitoring","Teams needing AI-driven metric analysis and anomaly detection","Organizations correlating Prometheus metrics with logs and traces for root cause analysis"],"limitations":["PromQL query complexity is bounded by agent reasoning capability; complex queries may require human refinement","Query performance depends on Prometheus instance size and cardinality; high-cardinality metrics may cause timeouts","No built-in support for cross-Prometheus-instance queries; requires federation or custom toolset","Metric retention depends on Prometheus configuration; historical data may be unavailable for old incidents"],"requires":["Python 3.9+","Prometheus instance (2.0+) with HTTP API enabled","Network access to Prometheus HTTP API endpoint","Optional: Prometheus authentication credentials if instance is protected"],"input_types":["PromQL query strings","metric names and label selectors","time ranges (start/end timestamps or relative durations)","query step intervals"],"output_types":["time-series data (metric name, labels, values with timestamps)","instant query results (current metric values)","metric metadata (available metrics, label names)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_4","uri":"capability://planning.reasoning.alert.investigation.workflow","name":"alert-investigation-workflow","description":"Implements a specialized investigation workflow triggered by alert payloads (Prometheus AlertManager format, Grafana alerts, or custom alert sources). The workflow extracts alert metadata (firing rules, labels, annotations), automatically constructs investigation context (related metrics, logs, Kubernetes resources), and executes the agentic loop to determine root causes and recommend remediation. Integrates with issue sources/destinations to create tickets in Jira, GitHub Issues, or Slack notifications.","intents":["I want the agent to automatically investigate alerts and provide root cause analysis without manual triage","I need the agent to create incident tickets with investigation results and recommended actions","I want to correlate alert metadata with observability data to understand alert context"],"best_for":["SRE teams automating alert triage and investigation","Organizations reducing MTTR through AI-driven incident response","Teams integrating AI investigation into existing alert routing workflows (PagerDuty, Opsgenie)"],"limitations":["Alert investigation quality depends on alert quality; poorly configured alerts produce low-quality investigations","No built-in deduplication; duplicate alerts trigger duplicate investigations unless external deduplication is configured","Investigation results are advisory; no automatic remediation execution (requires separate automation layer)","Alert context is limited to alert metadata; requires integration with observability toolsets to gather supporting data"],"requires":["Python 3.9+","Alert source integration (Prometheus AlertManager webhook, Grafana alert webhook, or custom HTTP endpoint)","At least one observability toolset configured (Kubernetes, Prometheus, etc.) for context gathering","Optional: Jira, GitHub, or Slack integration for issue creation/notification"],"input_types":["Prometheus AlertManager webhook payloads","Grafana alert webhook payloads","custom alert JSON with required fields (alert name, labels, annotations)"],"output_types":["investigation results (root cause analysis, supporting evidence)","remediation recommendations","Jira tickets or GitHub issues (if configured)","Slack messages (if configured)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_5","uri":"capability://planning.reasoning.issue.and.ticket.investigation.workflow","name":"issue-and-ticket-investigation-workflow","description":"Implements an investigation workflow triggered by issues/tickets from external sources (Jira, GitHub Issues, linear.app) or direct API calls. The workflow extracts issue metadata (title, description, labels, assignee), automatically constructs investigation context from observability data sources, and executes the agentic loop to analyze the issue and provide recommendations. Results can be written back to the issue as comments or create linked tickets. Supports scheduled investigation of issues (e.g., daily health checks on critical services).","intents":["I want the agent to investigate open issues by gathering relevant observability data and suggesting root causes","I need the agent to provide daily health checks on critical services and create tickets for degradation","I want the agent to correlate issue descriptions with observability data to understand context"],"best_for":["SRE teams automating issue investigation and triage","Product teams integrating AI-driven troubleshooting into issue tracking workflows","Organizations performing scheduled health checks on critical services"],"limitations":["Issue investigation quality depends on issue description quality; vague issues produce low-quality investigations","No built-in issue deduplication; duplicate issues trigger duplicate investigations","Investigation results are advisory; no automatic issue resolution or remediation","Scheduled investigations require external scheduler (Kubernetes CronJob, GitHub Actions, etc.)"],"requires":["Python 3.9+","Issue source integration (Jira API, GitHub API, linear.app API, or custom HTTP endpoint)","At least one observability toolset configured for context gathering","Optional: Jira, GitHub, or linear.app API credentials for writing results back to issues"],"input_types":["Jira issue JSON (via API or webhook)","GitHub issue JSON (via API or webhook)","custom issue JSON with required fields (title, description, labels)"],"output_types":["investigation results (analysis, recommendations)","issue comments (if writing back to issue source)","linked tickets (if creating new issues)","structured investigation data (JSON)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_6","uri":"capability://memory.knowledge.runbook.and.knowledge.integration","name":"runbook-and-knowledge-integration","description":"Integrates organizational knowledge sources (Confluence, Notion, Runbooks, internal wikis) into the investigation context through a knowledge retrieval system. The system indexes runbooks and documentation, retrieves relevant knowledge based on investigation context (alert type, service name, error patterns), and injects retrieved knowledge into the LLM prompt to guide investigation and remediation recommendations. Supports both static knowledge (indexed documents) and dynamic knowledge (API-based retrieval).","intents":["I want the agent to reference runbooks and internal documentation when investigating incidents","I need the agent to provide remediation steps from organizational knowledge bases","I want the agent to learn from past incident resolutions stored in Confluence or Notion"],"best_for":["SRE teams with mature runbook libraries and incident documentation","Organizations standardizing incident response procedures through documented runbooks","Teams leveraging institutional knowledge to improve investigation quality"],"limitations":["Knowledge retrieval quality depends on indexing and relevance ranking; poorly indexed knowledge may not be retrieved","Static knowledge indexing requires periodic updates; real-time knowledge changes may not be reflected immediately","Knowledge injection adds tokens to LLM context; large knowledge bases may exceed context window limits","No built-in knowledge validation; outdated or incorrect runbooks may mislead the agent"],"requires":["Python 3.9+","Knowledge source integration (Confluence API, Notion API, or custom HTTP endpoint)","Optional: Vector database or embedding service for semantic knowledge retrieval (if using RAG)","Knowledge source credentials (API tokens, authentication)"],"input_types":["Confluence pages (via API)","Notion databases (via API)","custom runbook documents (markdown, HTML, PDF)","investigation context (alert type, service name, error patterns)"],"output_types":["retrieved runbook excerpts (text)","remediation steps (structured list)","knowledge-augmented investigation results","links to source documentation"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_7","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.model.registry","name":"multi-provider-llm-abstraction-with-model-registry","description":"Implements a provider-agnostic LLM abstraction layer (holmes/core/llm.py) that supports multiple LLM providers (OpenAI, Anthropic, Ollama, custom providers) through a factory pattern and model registry. The system abstracts provider-specific API differences (function calling schemas, token counting, streaming formats) behind a unified interface, enabling seamless provider switching via configuration. Supports both cloud-hosted models (GPT-4, Claude) and self-hosted models (Ollama, vLLM) with configurable parameters (temperature, max tokens, system prompts).","intents":["I want to switch between different LLM providers without code changes","I need to use self-hosted models for data privacy or cost optimization","I want to configure model parameters (temperature, max tokens) per investigation type"],"best_for":["Organizations evaluating multiple LLM providers for incident investigation","Teams with data privacy requirements needing self-hosted model support","Cost-conscious teams optimizing LLM provider selection based on performance/cost tradeoffs"],"limitations":["Provider abstraction adds ~50-100ms latency per request due to adapter overhead","Not all providers support identical feature sets (e.g., function calling, streaming); fallback behavior may differ","Token counting is provider-specific; context window management may be inaccurate for non-OpenAI models","Custom provider integration requires Python development; no low-code provider builder"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, or self-hosted Ollama/vLLM instance)","Network access to LLM provider API or self-hosted instance"],"input_types":["LLM provider configuration (provider name, API key, model name, parameters)","investigation prompts (text)","tool definitions (for function calling)"],"output_types":["LLM responses (text)","tool calls (structured JSON)","token usage metrics"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_8","uri":"capability://data.processing.analysis.context.window.management.for.observability.data","name":"context-window-management-for-observability-data","description":"Implements intelligent context window management to handle large observability datasets that exceed LLM token limits. The system uses multiple strategies: summarization of large tool outputs, pagination of results, prioritization of recent/relevant data, and automatic context trimming based on token budgets. Supports configurable context window sizes per LLM provider and implements token counting to track context usage across conversation history, tool outputs, and system prompts.","intents":["I want the agent to investigate large datasets (1000+ log lines, 100+ metrics) without hitting token limits","I need the agent to prioritize recent and relevant data when context is limited","I want to configure context budgets per investigation to control costs"],"best_for":["SRE teams investigating high-volume services with large observability datasets","Organizations optimizing LLM costs by controlling context window usage","Teams with strict token budgets needing intelligent context prioritization"],"limitations":["Summarization of tool outputs may lose important details; trade-off between context size and information fidelity","Pagination requires multiple LLM calls; investigation latency increases with dataset size","Token counting is provider-specific and may be inaccurate for some models","Context prioritization heuristics are fixed; no adaptive prioritization based on investigation progress"],"requires":["Python 3.9+","LLM provider with known context window size (or configurable context window parameter)","Observability toolsets configured to return paginated results"],"input_types":["tool outputs (metrics, logs, Kubernetes resources)","context window budget (token count)","prioritization hints (time ranges, resource types)"],"output_types":["trimmed/summarized tool outputs","context usage metrics (tokens used, remaining budget)","pagination tokens (for retrieving additional results)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-holmesgpt--holmesgpt__cap_9","uri":"capability://data.processing.analysis.structured.output.processing.and.validation","name":"structured-output-processing-and-validation","description":"Implements structured output processing to extract and validate investigation results from LLM responses. The system uses JSON schema validation to ensure LLM outputs conform to expected structures (root cause analysis, remediation steps, confidence scores), handles partial or malformed outputs gracefully, and provides fallback mechanisms for invalid responses. Supports custom output schemas per investigation type and integrates with issue sources/destinations for structured result writing.","intents":["I want to extract structured investigation results (root cause, remediation steps) from LLM responses","I need to validate investigation results before writing them to issue tracking systems","I want to define custom output schemas for different investigation types"],"best_for":["Teams automating issue creation and ticket updates based on investigation results","Organizations requiring structured incident data for analytics and reporting","Teams with strict output validation requirements for compliance or quality assurance"],"limitations":["LLM output quality affects structured extraction; hallucinations or off-topic responses may fail validation","JSON schema validation is strict; minor formatting differences may cause validation failures","Fallback mechanisms may produce incomplete or low-quality results","Custom output schemas require schema definition and validation logic"],"requires":["Python 3.9+","JSON schema definitions for expected output structures","LLM provider supporting structured output (e.g., OpenAI function calling, Anthropic structured output)"],"input_types":["LLM responses (text or JSON)","JSON schema definitions","validation rules and constraints"],"output_types":["validated structured results (JSON)","validation errors and fallback results","confidence scores and metadata"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for OpenAI (GPT-4/3.5) or Anthropic (Claude) or compatible LLM provider","At least one configured toolset (Kubernetes, Prometheus, cloud provider, etc.)","Network access to observability platforms and infrastructure APIs","Valid credentials/API keys for each observability platform (Prometheus, Grafana, DataDog, etc.)","Network connectivity to observability platform APIs","For Kubernetes toolsets: kubectl access and kubeconfig or in-cluster service account","Terminal with ANSI color support (for formatted output)","At least one observability toolset configured","HTTP server (Flask, FastAPI, or similar; included in dependencies)"],"failure_modes":["Context window constraints limit the amount of observability data that can be analyzed in a single loop iteration; large datasets require pagination or summarization","Tool approval model adds latency for sensitive operations requiring human review","Loop depth is bounded by token limits and model context window; very deep investigation chains may require external state persistence","Query latency depends on slowest data source; no built-in query parallelization or timeout management","Data transformation overhead adds ~50-200ms per toolset invocation depending on result set size","Custom toolsets require Python development; no low-code toolset builder UI","Authentication credentials must be managed externally (environment variables, Kubernetes secrets); no built-in credential rotation","Interactive mode requires human presence; not suitable for fully automated incident response","Tool approval workflow adds latency; not suitable for time-critical incidents","CLI interface is text-based; no graphical visualization of investigation results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5221932155974452,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-05-03T13:50:21Z"},"community":{"stars":2351,"forks":328,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=holmesgpt--holmesgpt","compare_url":"https://unfragile.ai/compare?artifact=holmesgpt--holmesgpt"}},"signature":"4OErgMQESuR+lbA1G/vd2SyDiQiXBU2slFfkIeDxKs48ThmSLWDxIdfYoudZk4Goh1CfTmZat1NzCN9g/kIGCw==","signedAt":"2026-06-21T17:28:02.368Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/holmesgpt--holmesgpt","artifact":"https://unfragile.ai/holmesgpt--holmesgpt","verify":"https://unfragile.ai/api/v1/verify?slug=holmesgpt--holmesgpt","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"}}