Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and mor
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🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Enterprise AI agent orchestration plugin with 150+ commands, 74+ specialized agents, SPARC methodology, swarm coordination, GitHub integration, and neural training capabilities
Claude Code
Multi-agent swarm coordination, memory management, SPARC methodology, and intelligent hooks for Claude Code. Provides 60+ skills, 100+ commands, and 80+ specialized agents.
Claude Code
ADR lifecycle management — create, index, supersede, and link Architecture Decision Records to code
Claude Code
Agent runtimes — local WASM-sandboxed agents (rvagent) + Anthropic Claude Managed Agents (cloud); one interface, local-vs-cloud backends
Claude Code
AgentDB memory controllers with HNSW vector search, RuVector embeddings, and causal graphs
Claude Code
共 44 个插件包,通过下方列表选择查看。
共 476 项匹配内容,示例会单独标注;未声明归属的内容不代表插件自动包含。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and mor
其他仓库内容 · 未声明插件包归属
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when
其他仓库内容 · 未声明插件包归属
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing
其他仓库内容 · 未声明插件包归属
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing
其他仓库内容 · 未声明插件包归属
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems
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Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search
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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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Show AGNTCY/SLIM/CASA integration status — whether upstream AGNTCY packages are installed, which transport (local vs SLIM) is active, and whether CASA enforceme
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Generate API documentation from source code with JSDoc and OpenAPI support
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Run an autonomous /loop iteration -- check progress, work on next task, schedule next wake
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Use learned patterns and current state to predict the optimal next action
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Web browser automation with AI-optimized snapshots for claude-flow agents
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Web browser automation with AI-optimized snapshots for claude-flow agents
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Web browser automation with AI-optimized snapshots for claude-flow agents
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Probe a site's authentication flow for redirect leaks, missing CSRF, weak session cookies, and OAuth misconfiguration; produces an auth findings.md
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Extract structured data via stored browser-templates or one-shot DOM queries, with mandatory AIDefence PII + prompt-injection gates before content reaches the m
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Fill a web form by mapping field-name → value, with optional template lookup from browser-templates for known forms
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Execute a natural-language browser intent via page-agent (browser_act) when the target is easier to describe than to select — degrades gracefully when page-agen
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