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AgentDB Query

Free

Manage and query AgentDB efficiently through a controller bridge.

by ruvnet67.6k stars on ruvnet/ruflo
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Updated Aug 10, 2026
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Free · Opens the source repo

What AgentDB Query does

AgentDB Query is a skill designed for developers and designers who need to manage and query knowledge stored across agent sessions. This skill leverages the capabilities of AgentDB, which includes hierarchical storage, causal knowledge graphs, and semantic routing. By using this skill, users can effectively store, retrieve, and search for knowledge, ensuring that their agents have access to the most relevant information.

The skill operates through a series of standardized MCP tools, allowing users to perform a variety of tasks. For instance, users can check the health of the AgentDB, start a session, store knowledge in a structured manner, recall specific information, and synthesize context from multiple memories. Additionally, it enables users to build causal graphs that link related knowledge, facilitating a deeper understanding of how different pieces of information relate to one another.

With its focus on hierarchical storage and context synthesis, AgentDB Query is particularly useful in scenarios where maintaining a coherent knowledge base is crucial. Whether you are developing AI agents that need to access historical data or designing systems that require contextual awareness, this skill provides the necessary tools for effective knowledge management. The structured approach to storing and retrieving data ensures that users can maximize the potential of their agents.

However, it is important to note that this skill is not a standalone solution for all memory-related tasks. While it excels in querying and managing knowledge, users may need to utilize additional tools for specific memory operations that are not covered by the AgentDB framework. Understanding the operational fallbacks and namespace handling is also crucial to avoid potential issues during implementation.

When to use it

Use this skill when you need to efficiently store, retrieve, and search knowledge within agent sessions.

When not to use it

Avoid this skill if you require a comprehensive solution for all memory operations, as it focuses specifically on querying and managing AgentDB.

What you can build with it

Storing Agent Knowledge

Use this skill to store structured knowledge in AgentDB, ensuring that your AI agents have access to relevant information.

Retrieving Historical Data

Quickly recall specific pieces of information from previous agent sessions to enhance decision-making and context awareness.

Building Knowledge Graphs

Utilize the causal graph features to link related knowledge, enabling a deeper understanding of how different data points interact.

How to install AgentDB Query

View source

1. Install with the skills CLI

npx skills add ruvnet/ruflo/agentdb-query --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by ruvnet

AgentDB Query

Query and manage AgentDB through the controller bridge. AgentDB exposes 15 agentdb_* MCP tools; this skill enumerates the standard usage path.

When to use

When you need to store, retrieve, or search knowledge across agent sessions. AgentDB provides hierarchical storage, causal knowledge graphs, semantic routing, and context synthesis.

Steps

  1. Check health — mcp__plugin_ruflo-core_ruflo__agentdb_health. Sanity-check available: true.
  2. Start session — mcp__plugin_ruflo-core_ruflo__agentdb_session-start if not already active.
  3. Store knowledge — mcp__plugin_ruflo-core_ruflo__agentdb_hierarchical-store for structured tier-keyed data (tiers: working|episodic|semantic).
  4. Recall knowledge — mcp__plugin_ruflo-core_ruflo__agentdb_hierarchical-recall with a query.
  5. Search patterns — mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search for learned patterns (ReasoningBank-routed).
  6. Synthesize context — mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize to combine multiple memories.
  7. Build causal graph — mcp__plugin_ruflo-core_ruflo__agentdb_causal-edge to link related knowledge.

Available controller groups

Call mcp__plugin_ruflo-core_ruflo__agentdb_controllers to list the runtime registry. Functional categories surfaced via the 15 MCP tools:

  • Hierarchical — agentdb_hierarchical-store, _recall (tier-routed)
  • Pattern — agentdb_pattern-store, _search (ReasoningBank-routed)
  • Semantic — agentdb_semantic-route, _context-synthesize
  • Causal — agentdb_causal-edge (graph-node backend with bridge fallback)
  • Lifecycle — agentdb_health, _controllers, _session-start, _session-end
  • Bulk — agentdb_batch (≤500 entries), _consolidate
  • Quality — agentdb_feedback

Important: namespace handling

Namespace strings apply to memory_* and embeddings_search only. The agentdb_hierarchical-*, agentdb_pattern-*, and agentdb_causal-edge tools route by tier or controller, not namespace. Don't pass namespace: 'foo' to those tools — it will be silently ignored. See plugin README "Namespace convention".

Operational fallbacks (branch on these)

  • controller: 'memory-store-fallback' — pattern persisted via memory_store --namespace pattern. NOT a failure.
  • _graphNodeBackend: true — causal-edge handled by @ruvector/graph-node.
  • success: false, error: '...Use memory_store/memory_search instead.' — bridge unavailable; switch to memory_* tools per the README replacement table.

CLI alternative

npx @claude-flow/cli@latest memory search --query "your query" --namespace patterns
npx @claude-flow/cli@latest memory store --key "key" --value "value" --namespace patterns
npx @claude-flow/cli@latest memory list --namespace patterns

Frequently asked questions about AgentDB Query

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