> ## Documentation Index
> Fetch the complete documentation index at: https://clawmind.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> ClawMind is a multi-agent Web3 due diligence system that anchors every report on 0G Chain — so audit results can be verified later instead of silently rewritten.

# ClawMind

**Multi-Agent Web3 Cognitive Backbone**

<Info>
  ClawMind runs production LLM agent inference through the 0G Compute network using `deepseek/deepseek-chat-v3-0324`, and anchors reports on 0G Chain (Chain 16661).
</Info>

***

## The Problem

Web3 investors, DAO contributors, and protocol teams routinely make high-stakes decisions — approving grants, listing tokens, deploying integrations — based on scattered notes, subjective risk calls, and reports that nobody can reproduce later.

Three things break this process:

* **No audit trail.** Who analyzed what, when, and why? There's no record.
* **No adversarial challenge.** A single analyst's blind spots become the report's blind spots.
* **No integrity guarantee.** A report stored in Notion or Google Docs can be quietly edited after the fact.

***

## The Solution

ClawMind turns due diligence into a structured, verifiable, adversarial pipeline.

A task enters the system and flows through **8 specialized agents** — each with a distinct role. The Critic agent challenges the other agents' conclusions before a final report is produced. That report is then stored on **0G Storage** and its hash is anchored on **0G Chain**, creating a tamper-evident receipt that anyone can verify at any time.

```
User task → Memory → Planner → Researcher → Risk + Architect → Critic → Final → 0G Storage + 0G Chain
```

Every run produces:

* A structured due diligence report with a score and recommendation (`GO` / `INVESTIGATE_MORE` / `NO_GO`)
* A `0g://` storage URI pointing to the full report JSON
* An on-chain transaction with the report hash, score, and an EIP-712 operator signature
* A memory index entry that improves future analyses on similar projects

***

## Key Differentiators

<CardGroup cols={2}>
  <Card title="Adversarial by design" icon="shield-halved">
    The Critic agent actively challenges weak assumptions from the Researcher, Risk, and Architect agents before the Final agent produces a verdict. Unresolved challenges reduce the score by severity.
  </Card>

  <Card title="On-chain integrity" icon="link">
    Report hashes are recorded in `AnalysisRegistry.sol` on 0G Chain with an EIP-712 operator signature. The result cannot be altered without breaking the on-chain proof.
  </Card>

  <Card title="Persistent memory" icon="brain">
    ClawMind stores a semantic memory index on 0G Storage. Each run retrieves relevant context from past analyses — improving accuracy on similar projects over time.
  </Card>

  <Card title="OpenClaw compatible" icon="plug">
    ClawMind exposes an OpenClaw manifest describing the 8-step pipeline, artifacts, and security policies — making it composable with other agent systems in the 0G ecosystem.
  </Card>
</CardGroup>

***

## How Scoring Works

Every analysis produces a score from 0 to 100 and maps to one of three recommendations.

| Score range | Recommendation     | Meaning                               |
| ----------- | ------------------ | ------------------------------------- |
| 75 – 100    | `GO`               | Mature, audited, low-risk protocol    |
| 40 – 74     | `INVESTIGATE_MORE` | Promising but unresolved risk factors |
| 0 – 39      | `NO_GO`            | Critical risk — do not proceed        |

The Critic applies score penalties based on the severity of unresolved challenges:

| Challenge severity | Score adjustment |
| ------------------ | ---------------- |
| High               | −15              |
| Medium             | −7               |
| Low                | −3               |

***

## 0G Integration at a Glance

ClawMind uses four 0G components in production:

| Component                | Role                                                            |
| ------------------------ | --------------------------------------------------------------- |
| **0G Compute**           | Routes LLM agent inference through the 0G Router                |
| **0G Storage — reports** | Stores the final report JSON and returns a `0g://` URI          |
| **0G Storage — memory**  | Stores the persistent memory index across runs                  |
| **0G Chain**             | Anchors the report hash, score, and operator signature on-chain |

***

## Live Links

| Surface           | URL                                                                                                      |
| ----------------- | -------------------------------------------------------------------------------------------------------- |
| App               | [clawmind-puce.vercel.app](https://clawmind-puce.vercel.app)                                             |
| Live stats        | [clawmind-puce.vercel.app/stats](https://clawmind-puce.vercel.app/stats)                                 |
| Judge mode        | [clawmind-puce.vercel.app/judge](https://clawmind-puce.vercel.app/judge)                                 |
| Judge API         | [clawmind-puce.vercel.app/api/judge](https://clawmind-puce.vercel.app/api/judge)                         |
| OpenClaw manifest | [/api/openclaw/manifest?format=json](https://clawmind-puce.vercel.app/api/openclaw/manifest?format=json) |
| 0G contract       | [0x24bA…E121 on 0G Explorer](https://chainscan.0g.ai/address/0x24bAAC6720ae5B01A1CC90eCC1C15AFcb903E121) |
| GitHub            | [github.com/ILYUTKICK/clawmind](https://github.com/ILYUTKICK/clawmind)                                   |

***

## Safety Notice

ClawMind is a **due diligence aid**, not a formal security audit or exploit detector. The LLM pipeline produces structured analysis; on-chain receipts make results verifiable. Any deployment touching custody, signing keys, protocol upgrades, or automated execution must still require deterministic policy gates and human approval outside the model.