An autonomous state manager and orchestration engine for persistent, high-velocity AI research missions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-mind
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 research-mind 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 manage persistent state, synthesis, and autonomous verification for AI agents. It serves as a sophisticated backend for Openclaw Skills, providing agents with the ability to maintain long-term context through a local SQLite-based persistence layer. By enabling divergent reasoning and self-correcting verification loops, it transforms raw data ingestion into actionable, verified intelligence.
The tool is built for developers who need their agents to do more than just fetch information; it allows for the creation of research branches and hypotheses, ensuring that parallel paths of inquiry can be explored without losing state. With native support for the Model Context Protocol (MCP), ResearchVault integrates seamlessly into multi-agent ecosystems, making it a robust choice for complex research automation.
ResearchVault Use Cases
- Orchestrating long-running research projects that require persistent memory across sessions.
- Creating self-correcting AI workflows that autonomously verify low-confidence data points.
- Managing multi-source data ingestion and finding relationships through automated link discovery.
- Implementing divergent reasoning where an agent explores multiple parallel hypotheses for a single objective.
- Building collaborative agent environments using the Model Context Protocol to share research states.
How ResearchVault Works
- Project Initialization: The user initializes a vault with a specific objective, which creates a structured SQLite database for tracking findings.
- Multi-Source Ingestion: Agents or users ingest data from various sources (like URLs or social feeds) using the scuttle command.
- Synthesis: The local engine uses embeddings to discover links and relationships between disparate findings and artifacts.
- Verification: The system identifies low-confidence information and plans verification missions to self-correct and validate data.
- Inter-Agent Communication: The vault state is exposed via an MCP server, allowing other Openclaw Skills to query and contribute to the research findings.
ResearchVault Setup
To get started with ResearchVault, ensure you have Python 3.13 and the uv package manager installed. Follow these steps to initialize the environment:
uv venv && uv pip install -e .
Once installed, you can initialize a new research project:
uv run python scripts/vault.py init --id "research-id" --name "Project Name" --objective "Define your goal"
ResearchVault Data Schema & Taxonomy
ResearchVault organizes research data into a structured schema to maintain high-velocity retrieval and cross-referencing:
| Table | Description |
|---|---|
| artifacts | Stores raw source data, files, and ingested content. |
| findings | Contains extracted insights and synthesized research points. |
| links | Maps relationships between artifacts and findings using local embeddings. |
| missions | Tracks active and completed verification tasks for agents. |
| branches | Manages divergent reasoning paths and hypotheses for non-linear research. |
ResearchVault Advanced Features
- Divergent Reasoning: Create parallel branches to test different hypotheses within the same research project.
- Watchdog Mode: Enable continuous background monitoring of specific URLs and search queries for real-time updates.
- Local Embeddings: Utilize high-performance local synthesis for discovery without relying on external vector databases.
- Native MCP Support: Standardized transport layers for seamless integration with other Openclaw Skills and agentic tools.
- Active Verification: Automated planning and execution of verification loops to ensure data integrity.
SKILL.md
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