Decision comparison
AgentVault vs Hashgrid DNA
AgentVault and Hashgrid DNA do not compete; they sit at different points in an agent stack. AgentVault guards one OpenClaw agent on its host: it stops reads of key files and destructive commands until a person approves them, and records every action. Hashgrid DNA answers a ranking question, such as which tool or agent fits a request, and learns from the scores you send back so later answers need fewer judge calls. Pick by the problem in front of you. If an agent with shell and credential access is the risk, AgentVault addresses it and DNA does not. If an agent or app keeps choosing among many options and you can score its choices, DNA addresses it and AgentVault does not. A wrapped agent that calls DNA would show those calls in AgentVault's network log, because AgentVault logs every outbound connection.
Used together. These are normally used together rather than chosen between. The comparison explains what each one does in the stack.
These are different kinds of product — AI Security and Agent Protocol.
Quick Comparison
| Decision factor | AgentVault | Hashgrid DNA |
|---|---|---|
| Best For | A developer running an OpenClaw agent on a machine that holds real credentials, a shell and network access, who wants a hard stop before the agent reads a key file, runs a destructive command or sends data out. AgentVault was built in response to OpenClaw's documented vulnerabilities and takes the view that prompt injection will get through, so it guards the actions that follow. | A team that keeps asking a search question with a judgeable answer, such as which tool an agent should call, which agent should take a task or which document answers a question, and already scores results with an LLM, a smaller model or user feedback. Hashgrid DNA learns from those scores so later searches need the judge less often. |
| Core Job | Action-level security monitoring for one agent: it wraps the agent, intercepts risky file reads and commands, and asks a person to approve each risky action before it runs. | A hosted search engine that learns from feedback: you send embeddings of items and queries, it returns the best item it has not yet shown that query, and the score you send back from -1 to 1 trains it. |
| How It Works | A Node.js, TypeScript and Express proxy sits between the agent and its host. File system hooks catch reads of ~/.aws/credentials or ~/.ssh/id_rsa, command parsing blocks rm -rf, curl | bash and privilege escalation attempts, and every outbound connection is logged. | Items and queries are nodes in a grid; a search creates an edge to the best node under a key prefix such as /tools/ or /agents/, and only scored edges train DNA. It is reached through a REST API at dna.hashgrid.ai/api/v1, a Python SDK, or an MCP server at dna.hashgrid.ai/mcp. |
| Pricing Model | AgentVault is free and open source under the MIT license: a GitHub project that wraps AI agents with action-level security monitoring for OpenClaw. It has no paid plans and publishes no prices; the agentvault.ai domain now belongs to a different company. Verified 2026-10-05 against https://github.com/hugoventures1-glitch/agentvault. | Creating a grid needs no sign-up: one API call returns the grid and its key. A self-created grid holds up to 1,024 nodes, and each address can create 5 grids an hour and 20 a day. Larger grids are arranged by email (office@hashgrid.ai); no paid plan or price is published. Official docs checked October 31, 2026. |
| Setup | The README's quick start is three commands: npm install -g agentvault, agentvault wrap openclaw, then agentvault dashboard. Check the first one before running it: the npm package named agentvault is published by a different maintainer and is described as infrastructure for AI agents on Internet Computer canisters. | One call creates a grid and returns its API key, which is shown only once. In Python, pip install 'hashgrid[local]' then Grid.create; agents connect with claude mcp add --transport http dna https://dna.hashgrid.ai/mcp, or the same URL in Cursor's JSON config. |
| Data Handling | Keeps a SQLite database of every agent action for forensics, and a Next.js web dashboard shows what the agent is doing in real time. The record lives with the operator who runs AgentVault. | DNA receives only vectors, random IDs, the keys you choose and scores, never your items, prompts or users. Keys are stored as sent, so the docs advise opaque keys like /items/42 when names are sensitive. |
| Maturity and Support | An early single-repository project: the README calls it day 1, the repository was last updated on February 10, 2026 and has 2 GitHub stars. Multi-agent monitoring and custom security policies are roadmap items. Support is through GitHub issues and pull requests. | The docs describe themselves as a work in progress and the homepage invites early-access requests. The MIT-licensed Python SDK on GitHub was last updated on October 23, 2026, and PyPI package hashgrid reached 1.3.0 on October 24, 2026. Questions go to office@hashgrid.ai. |
AgentVault
- Best For:
- A developer running an OpenClaw agent on a machine that holds real credentials, a shell and network access, who wants a hard stop before the agent reads a key file, runs a destructive command or sends data out. AgentVault was built in response to OpenClaw's documented vulnerabilities and takes the view that prompt injection will get through, so it guards the actions that follow.
- Core Job:
- Action-level security monitoring for one agent: it wraps the agent, intercepts risky file reads and commands, and asks a person to approve each risky action before it runs.
- How It Works:
- A Node.js, TypeScript and Express proxy sits between the agent and its host. File system hooks catch reads of ~/.aws/credentials or ~/.ssh/id_rsa, command parsing blocks rm -rf, curl | bash and privilege escalation attempts, and every outbound connection is logged.
- Pricing Model:
- AgentVault is free and open source under the MIT license: a GitHub project that wraps AI agents with action-level security monitoring for OpenClaw. It has no paid plans and publishes no prices; the agentvault.ai domain now belongs to a different company. Verified 2026-10-05 against https://github.com/hugoventures1-glitch/agentvault.
- Setup:
- The README's quick start is three commands: npm install -g agentvault, agentvault wrap openclaw, then agentvault dashboard. Check the first one before running it: the npm package named agentvault is published by a different maintainer and is described as infrastructure for AI agents on Internet Computer canisters.
- Data Handling:
- Keeps a SQLite database of every agent action for forensics, and a Next.js web dashboard shows what the agent is doing in real time. The record lives with the operator who runs AgentVault.
- Maturity and Support:
- An early single-repository project: the README calls it day 1, the repository was last updated on February 10, 2026 and has 2 GitHub stars. Multi-agent monitoring and custom security policies are roadmap items. Support is through GitHub issues and pull requests.
Hashgrid DNA
- Best For:
- A team that keeps asking a search question with a judgeable answer, such as which tool an agent should call, which agent should take a task or which document answers a question, and already scores results with an LLM, a smaller model or user feedback. Hashgrid DNA learns from those scores so later searches need the judge less often.
- Core Job:
- A hosted search engine that learns from feedback: you send embeddings of items and queries, it returns the best item it has not yet shown that query, and the score you send back from -1 to 1 trains it.
- How It Works:
- Items and queries are nodes in a grid; a search creates an edge to the best node under a key prefix such as /tools/ or /agents/, and only scored edges train DNA. It is reached through a REST API at dna.hashgrid.ai/api/v1, a Python SDK, or an MCP server at dna.hashgrid.ai/mcp.
- Pricing Model:
- Creating a grid needs no sign-up: one API call returns the grid and its key. A self-created grid holds up to 1,024 nodes, and each address can create 5 grids an hour and 20 a day. Larger grids are arranged by email (office@hashgrid.ai); no paid plan or price is published. Official docs checked October 31, 2026.
- Setup:
- One call creates a grid and returns its API key, which is shown only once. In Python, pip install 'hashgrid[local]' then Grid.create; agents connect with claude mcp add --transport http dna https://dna.hashgrid.ai/mcp, or the same URL in Cursor's JSON config.
- Data Handling:
- DNA receives only vectors, random IDs, the keys you choose and scores, never your items, prompts or users. Keys are stored as sent, so the docs advise opaque keys like /items/42 when names are sensitive.
- Maturity and Support:
- The docs describe themselves as a work in progress and the homepage invites early-access requests. The MIT-licensed Python SDK on GitHub was last updated on October 23, 2026, and PyPI package hashgrid reached 1.3.0 on October 24, 2026. Questions go to office@hashgrid.ai.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | AgentVault | Hashgrid DNA |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 0 | Not available |
| GitHub stars(Product adoption) | 2 | Not available |
| Product Hunt comments(Community interest) | 1 | 10 |
| Product Hunt reviews(Community interest) | 0 | 0 |
| Product Hunt votes(Community interest) | 2 | 13 |
| GitHub commits, 90d(Developer adoption) | Not available | 11 |
| GitHub stars(Developer adoption) | Not available | 4 |
| PyPI weekly downloads(Developer adoption) | Not available | 131 |
As of November 9, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
AgentVault
Package vulnerabilities
Not available
Repository security score
Not available
Hashgrid DNA
November 9, 2026Package vulnerabilities
PyPI · hashgrid@2.0.0
0 vulnerabilities
across 1 package
Repository security score
Not available
Feature Comparison
| Feature | AgentVault | Hashgrid DNA |
|---|---|---|
| Security controls | ||
| Blocking dangerous actions | Blocks rm -rf, curl | bash and privilege escalation attempts before they run | Not a security tool; it ranks search results |
| Credential protection | Catches agent reads of ~/.aws/credentials and ~/.ssh/id_rsa | Never receives your items or credentials; it sees vectors, keys and scores |
| Human approval | Every risky action waits for explicit permission | No approval step; your judge's scores steer later results |
| Network visibility | Logs all outbound connections and data exfiltration attempts | Not applicable; it is the remote service your code or agent calls |
| Search and learning | ||
| Core primitive | A wrapped agent and the actions it attempts | Grids, nodes (items and queries) and edges (results with scores) |
| What improves over time | Fixed detection rules today; custom security policies are on the roadmap | Ranking learns from every score from -1 to 1; unscored results teach nothing |
| Warm-up | None needed; rules apply from the first action | Send known scores from labels or past clicks, or search and score warm-up queries |
| Vendor-reported efficiency | No benchmark published | Hashgrid reports 5 LLM calls to find the top 3 of 100 items, against 36 with RAG plus a reranker and 100 when an LLM checks every item, averaged over its three test tasks |
| Integration | ||
| Agents supported | Documents OpenClaw; multi-agent monitoring is a roadmap item | Any MCP client; the docs show Claude Code and Cursor |
| Interfaces | CLI commands and a real-time web dashboard | REST API, Python SDK and an HTTP MCP server with one tool per API route |
| Embedding model | Not applicable | Any model, one per grid; the Python SDK embeds text on your machine |
| Deployment and terms | ||
| Where it runs | On your machine, next to the agent it wraps | Hosted by Hashgrid; you keep the embedder, the judge and the items |
| License and price | MIT, no paid plans | SDK under MIT; the hosted service publishes no price, and grids beyond the self-created limit are arranged by email |
| Capacity limits | No limits stated in the README | 1,024 nodes per self-created grid; 5 grids an hour and 20 a day per address |
| Alerts | Slack and email alerts are on the roadmap | Not applicable |
Security controls
Blocking dangerous actions
Credential protection
Human approval
Network visibility
Search and learning
Core primitive
What improves over time
Warm-up
Vendor-reported efficiency
Integration
Agents supported
Interfaces
Embedding model
Deployment and terms
Where it runs
License and price
Capacity limits
Alerts
How they fit together
AgentVault and Hashgrid DNA do not compete; they sit at different points in an agent stack. AgentVault guards one OpenClaw agent on its host: it stops reads of key files and destructive commands until a person approves them, and records every action. Hashgrid DNA answers a ranking question, such as which tool or agent fits a request, and learns from the scores you send back so later answers need fewer judge calls. Pick by the problem in front of you. If an agent with shell and credential access is the risk, AgentVault addresses it and DNA does not. If an agent or app keeps choosing among many options and you can score its choices, DNA addresses it and AgentVault does not. A wrapped agent that calls DNA would show those calls in AgentVault's network log, because AgentVault logs every outbound connection.
What each one handles
Use AgentVault for:
Choose AgentVault when your agent runs OpenClaw on a machine with real secrets and you want each risky action held for approval. It intercepts reads of ~/.aws/credentials and ~/.ssh/id_rsa, blocks rm -rf, curl | bash and privilege escalation, logs outbound connections, and keeps a SQLite audit trail with a live dashboard, all under the MIT license with no paid plan. Weigh three limits before relying on it: the repository has not changed since February 10, 2026, multi-agent monitoring and custom policies are still roadmap items, and the README's npm install command names a package another maintainer now publishes for a different project. Hashgrid DNA loses this case outright: it never sees the agent's actions, so it cannot block a file read or a command.
Use Hashgrid DNA for:
Choose Hashgrid DNA when an agent or application repeatedly picks one option from many, such as a tool, an agent, a product or a document, and you already judge the picks with an LLM, a smaller model or user feedback. You send embeddings and scores; DNA trains on its side, so you host no model, and it never receives the items themselves. Plan for its limits: a self-created grid holds 1,024 nodes counting queries, more capacity is arranged by email with no published price, early results stay rough until a warm-up adds scores, and the docs are a work in progress. AgentVault loses this case because it ranks nothing; it guards one agent on one machine.
These roles reflect the available product evidence. Most teams run both; which one owns a given job depends on your stack and team.
Frequently Asked Questions
Can AgentVault and Hashgrid DNA be used in the same agent stack?
Yes, because they do different jobs. AgentVault wraps an OpenClaw agent and holds risky file reads, commands and network calls for approval. Hashgrid DNA is a remote search service the agent or your code calls to rank options. Because AgentVault logs every outbound connection, calls to dna.hashgrid.ai from a wrapped agent will appear in its log. Neither project documents running with the other, so test the pair before relying on it.
What does each tool cost?
AgentVault is MIT-licensed open source with no paid plans; you pay for the machine it runs on and the time spent on approvals. Hashgrid DNA publishes no price: a grid is created with no sign-up and holds up to 1,024 nodes, and the docs say to write to office@hashgrid.ai for more. With DNA, the costs you control are your embedding model and the judge calls that score results.
Does Hashgrid DNA see my data?
No. DNA receives vectors, random IDs, the keys you choose and scores. Your items, prompts and users stay with you, and the Python SDK embeds text on your machine. Keys are stored as you send them, so use opaque keys such as /items/42 when names are sensitive.
Is AgentVault safe to install from npm?
Check first. The README's quick start runs npm install -g agentvault, but the npm package with that name is published by a different maintainer and described as infrastructure for AI agents on Internet Computer canisters. Read the AgentVault repository itself before installing anything under that name.
Which one should a team with a single agent start with?
If that agent runs OpenClaw with access to real credentials or a shell, start with AgentVault, because it addresses the immediate risk. Hashgrid DNA earns its place when the agent, or your product, must choose among many tools, agents or documents and you can score those choices; with one fixed tool set and nothing to rank, it has little to learn from.