{"id":"aden-hive-hive","name":"hive","homepage":null,"repo_url":"https://github.com/aden-hive/hive","category":"ai-ml","subcategories":[],"tags":["ai-ml","agent-framework","agent-harness","human-in-the-loop","observability","checkpoint-recovery","mcp","python"],"what_it_does":"Hive is a Python runtime harness for AI agents in production. It supports goal-driven agent development (a coding “queen” generates an agent graph/code), then executes that graph with features like state isolation, checkpoint-based crash recovery, cost enforcement/degradation, real-time observability via streaming, and human-in-the-loop pause/intervention nodes. It also advertises integration through MCP tools and tool/agent SDK-wrapped nodes, with support for multiple LLM providers via LiteLLM-compatible interfaces.","use_cases":["Running long-lived, production AI agent workflows with state persistence and crash recovery","Multi-agent coordination with session isolation and parallel execution","Human-in-the-loop approval/intervention for higher-risk steps","Operational observability of agent decisions and node-to-node communication","Automated graph evolution/self-healing after failures (within the harness model)"],"not_for":["Simple one-off scripts or basic agent chains where a full production harness is unnecessary","Use cases requiring a public, hosted REST/GraphQL API from Hive itself (the repo appears focused on a local/self-hosted runtime)"],"best_when":"You need a self-hosted agent runtime that manages state, observability, recovery, and human oversight for production workloads.","avoid_when":"You only need lightweight experimentation without operational controls (recovery/cost/observability) or you require a turnkey hosted web API.","alternatives":["LangGraph (agent workflow/state graphs)","Ray (agent/executor orchestration)","Airflow/Temporal (reliable workflow execution patterns, though not agent-native)","Self-hosted orchestration with custom queues + logging + checkpointing","Other agent harness frameworks with state/checkpoint/retry semantics"],"af_score":52.5,"security_score":48.2,"reliability_score":27.5,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T14:20:42.702049+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":true,"mcp_server_url":null,"has_sdk":true,"sdk_languages":["python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["API key / encrypted credential store (described as encrypted API key storage under ~/.hive/credentials)","LLM provider credentials (implied via provider configuration and LiteLLM-compatible setup)"],"oauth":false,"scopes":false,"notes":"README mentions an encrypted credential store for API keys, but does not describe auth flows for any external service endpoint (no public REST API described)."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Repository indicates self-hosting (Python runtime harness). Ongoing costs likely depend on chosen LLM providers and infrastructure, but the README does not specify pricing tiers."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":52.5,"security_score":48.2,"reliability_score":27.5,"mcp_server_quality":45.0,"documentation_accuracy":65.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":75.0,"rate_limit_clarity":10.0,"tls_enforcement":60.0,"auth_strength":55.0,"scope_granularity":20.0,"dependency_hygiene":30.0,"secret_handling":70.0,"security_notes":"README states an encrypted credential store (~/.hive/credentials), which is a positive signal for secret handling, but there is no detailed security model (TLS requirements, permissioning/scopes, threat model, audit logging, or dependency/Vuln management) included in the provided content. Rate limiting and operational guardrails are mentioned at a high level (cost enforcement) but not documented in detail.","uptime_documented":0.0,"version_stability":40.0,"breaking_changes_history":0.0,"error_recovery":70.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Goal-driven code/graph generation implies agent behavior may vary across runs unless you pin configuration and model/versioning.","Human-in-the-loop pauses can affect throughput and require careful timeout/escalation configuration.","Browser control and tool execution can produce side effects; ensure idempotency at the tool/action layer if reruns occur after recovery."]}}