An autonomous state manager for agentic research that handles persistent storage, reasoning branches, and automated verification.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install luka-vault
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install luka-vault using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is ResearchVault?
ResearchVault is a high-velocity research orchestration engine designed to provide AI agents with a robust framework for managing complex, long-running investigations. By acting as a persistent state manager, it allows agents to store findings, manage artifacts, and perform autonomous verification missions. This tool is a critical addition to the library of Openclaw Skills for developers building advanced agentic workflows that require more than just ephemeral context.
The engine is built around The Vault, a local SQLite-based persistence layer that tracks findings and links. It supports divergent reasoning, enabling agents to explore multiple hypotheses in parallel through dedicated branches. With its integrated synthesis engine and native support for the Model Context Protocol (MCP), ResearchVault transforms how agents interact with information, moving from simple retrieval to deep, structured knowledge synthesis.
ResearchVault Use Cases
- Creating self-correcting research agents that perform active verification of low-confidence data.
- Orchestrating multi-source data ingestion for deep-dive market or academic research.
- Managing complex research projects that require parallel reasoning branches and hypothesis testing.
- Establishing a persistent knowledge base for AI agents to reference across multiple sessions.
- Implementing cross-agent collaboration using the built-in MCP server capabilities.
How ResearchVault Works
- Project Initialization: The researcher defines a project ID, name, and core objective to set the research boundaries.
- Multi-Source Ingestion: Agents use the scuttle functionality to ingest data from URLs and community platforms into the local vault.
- Synthesis: The local synthesis engine uses embeddings to automatically discover and map links between disparate findings.
- Verification: The system identifies low-confidence data and generates active verification missions for agents to self-correct findings.
- Inter-Agent Communication: The vault state is exposed via an MCP server, allowing other agents in the Openclaw Skills ecosystem to query and contribute to the research.
ResearchVault Setup
To deploy ResearchVault, you must have Python 3.13 and the uv package manager installed. Use the following commands to initialize the environment:
uv venv
uv pip install -e .
To start a new research project, use the initialization script:
uv run python scripts/vault.py init --id "project-id" --name "Project Name" --objective "Define your research goal"
ResearchVault Data Schema & Taxonomy
ResearchVault utilizes a structured SQLite schema to maintain research integrity. The data is organized into several key components:
| Entity | Function |
|---|---|
| Artifacts | Storage for raw data, files, and documents collected during research. |
| Findings | Granular pieces of information or facts extracted from artifacts. |
| Links | Metadata connections and relationships between different findings. |
| Branches | Isolated environments for testing specific hypotheses or parallel paths. |
| Missions | Records of verification tasks and agentic self-correction logs. |
ResearchVault Advanced Features
- Divergent Reasoning: Ability to spawn and manage parallel research branches and hypothesis states.
- Synthesis Engine: Automated discovery of semantic relationships using local embeddings.
- Watchdog Mode: Provides continuous background monitoring of target URLs and search queries for real-time updates.
- Active Verification: A self-correcting mechanism where agents plan and execute missions to validate information.
- Native MCP Server: Built-in support for the Model Context Protocol to facilitate seamless integration within the broader Openclaw Skills framework.
SKILL.md
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