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<img src="https://capsule-render.vercel.app/api?type=rect&color=0:6b46c1,100:2b6cb0&height=120&section=header&text=LEDGERMIND&fontSize=48&fontColor=ffffff&fontAlignY=58" width="100%" alt="LEDGERMIND"/>

# LEDGERMIND

### Local LLM cost & token forensics proxy with anomaly detection

<img src="https://readme-typing-svg.demolab.com?font=Fira+Code&size=18&duration=3500&pause=1000&color=6B46C1&center=true&vCenter=true&width=720&lines=Local+LLM+cost++token+forensics+proxy+with+anomaly+detection;Self-hostable+%C2%B7+MCP-native+%C2%B7+CI-ready+%C2%B7+polyglot" width="720"/>

[![PyPI](https://img.shields.io/pypi/v/cognis-ledgermind.svg?color=6b46c1)](https://pypi.org/project/cognis-ledgermind/) [![CI](https://github.com/cognis-digital/ledgermind/actions/workflows/ci.yml/badge.svg)](https://github.com/cognis-digital/ledgermind/actions) [![License: COCL 1.0](https://img.shields.io/badge/License-COCL%201.0-2b6cb0.svg)](LICENSE) [![Suite](https://img.shields.io/badge/Cognis-Neural%20Suite-6b46c1.svg)](https://github.com/cognis-digital)

*AI Security & Governance — securing LLMs, agents, and the MCP supply chain.*

</div>

```bash
pip install cognis-ledgermind
ledgermind scan .            # → prioritized findings in seconds
```


<!-- cognis:example:start -->
## 🔎 Example output

Real, reproducible output from the tool — runs offline:

```console
$ ledgermind-emit --version
ledgermind 0.1.0
```

```console
$ ledgermind-emit --help
usage: ledgermind [-h] [--version] {audit} ...

Local LLM cost & token forensics with anomaly detection.

positional arguments:
  {audit}
    audit     Audit an LLM request log (JSONL or JSON array).

options:
  -h, --help  show this help message and exit
  --version   show program's version number and exit
```

> Blocks above are real `ledgermind` output — reproduce them from a clone.

**Sample result format** _(illustrative values — run on your own data for real findings):_

```
{
"finding": {
"id": "1234567890",
"category": "vulnerability",
"name": "Apache Log4j RCE",
"description": "A remote code execution vulnerability in Apache Log4j.",
"severity": "high",
"created_at": "2021-12-10T14:30:00Z"
},
"indicators": [
{
"type": "ip",
"value": "192.0.2.1"
},
{
"type": "domain",
"value": "example.com"
}
],
"recommendations": [
{
"text": "Update Apache Log4j to version 2.16.0 or later."
}
]
}
```

<!-- cognis:example:end -->

## Usage — step by step

1. **Install** (Python 3.8+, stdlib only):
   ```bash
   pip install ledgermind
   ```
2. **Audit an LLM request log** (JSONL or JSON array) for cost, tokens, and anomalies:
   ```bash
   ledgermind audit logs.jsonl
   ```
   Reports totals plus cost-by-model and cost-by-API-key breakdowns.
3. **Override pricing** and tune anomaly sensitivity:
   ```bash
   ledgermind audit logs.jsonl --pricing custom_pricing.json --mad-threshold 3.0
   ```
   (`--pricing` is merged over the built-in defaults; `--mad-threshold` is the modified z-score cutoff.)
4. **Read the output as JSON**:
   ```bash
   ledgermind audit logs.jsonl --format json | jq '.total_cost_usd, .anomalies[]'
   ```
5. **Gate CI / cron** on spend anomalies — exit `2` when any anomaly is detected:
   ```bash
   ledgermind audit logs.jsonl --fail-on-anomaly || echo "LLM spend anomaly detected"
   ```


## Contents

- [Why ledgermind?](#why) · [Features](#features) · [Quick start](#quick-start) · [Example](#example) · [Architecture](#architecture) · [AI stack](#ai-stack) · [How it compares](#how-it-compares) · [Integrations](#integrations) · [Install anywhere](#install-anywhere) · [Related](#related) · [Contributing](#contributing)

<a name="why"></a>
## Why ledgermind?

Local LLM cost & token forensics proxy with anomaly detection — without standing up heavyweight infrastructure.

`ledgermind` is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

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<a name="features"></a>
## Features

- ✅ Price Call
- ✅ Load Events
- ✅ Detect Anomalies
- ✅ Build Report
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (`ports/`)

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<a name="quick-start"></a>
## Quick start

```bash
pip install cognis-ledgermind
ledgermind --version
ledgermind scan .                       # scan current project
ledgermind scan . --format json         # machine-readable
ledgermind scan . --fail-on high        # CI gate (non-zero exit)
```

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<a name="example"></a>
## Example

```text
$ ledgermind scan .
  [HIGH    ] LED-001  example finding             (./src/app.py)
  [MEDIUM  ] LED-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms
```

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<a name="architecture"></a>
## Architecture

```mermaid
flowchart LR
  IN[addresses + transactions] --> P[ledgermind<br/>cluster + trace]
  P --> OUT[sanctions xref / report]
```

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<a name="ai-stack"></a>
## Use it from any AI stack

`ledgermind` is interoperable with every popular way of using AI:

- **MCP server** — `ledgermind mcp` (Claude Desktop, Cursor, Cognis.Studio, [uncensored-fleet](https://github.com/cognis-digital/uncensored-fleet))
- **OpenAI-compatible / JSON** — pipe `ledgermind scan . --format json` into any agent or LLM
- **LangChain · CrewAI · AutoGen · LlamaIndex** — wrap the CLI/JSON as a tool in one line
- **CI / scripts** — exit codes + SARIF for non-AI pipelines

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<a name="how-it-compares"></a>
## How it compares

| | **Cognis ledgermind** | BerriAI |
|---|:---:|:---:|
| Self-hostable, no account | ✅ | varies |
| Single command, zero config | ✅ | ⚠️ |
| JSON + SARIF for CI | ✅ | varies |
| MCP-native (AI agents) | ✅ | ❌ |
| Polyglot ports (JS/Go/Rust) | ✅ | ❌ |
| Open license | ✅ COCL | varies |

*Built in the spirit of **BerriAI/litellm**, re-framed the Cognis way. Missing a credit? Open a PR.*

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<a name="integrations"></a>
## Integrations

Pipes into your stack: **SARIF** for code-scanning, **JSON** for anything, an **MCP server** (`ledgermind mcp`) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See [`docs/INTEGRATIONS.md`](docs/INTEGRATIONS.md).

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<a name="install-anywhere"></a>
## Install — every way, every platform

```bash
pip install "git+https://github.com/cognis-digital/ledgermind.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/ledgermind.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/ledgermind.git" # uv
pip install cognis-ledgermind                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/ledgermind:latest --help        # Docker
brew install cognis-digital/tap/ledgermind                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/ledgermind/main/install.sh | sh
```

| Linux | macOS | Windows | Docker | Cloud |
|---|---|---|---|---|
| `scripts/setup-linux.sh` | `scripts/setup-macos.sh` | `scripts/setup-windows.ps1` | `docker run ghcr.io/cognis-digital/ledgermind` | [DEPLOY.md](docs/DEPLOY.md) (AWS/Azure/GCP/k8s) |

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<a name="related"></a>
## Related Cognis tools

- [`aegis`](https://github.com/cognis-digital/aegis) — AI Agent Permission & Access Auditor — surfaces the lethal trifecta of credentials + injection + reach
- [`promptmirror`](https://github.com/cognis-digital/promptmirror) — Prompt-injection & indirect-injection scanner for any LLM context input
- [`adversa`](https://github.com/cognis-digital/adversa) — LLM red-team harness — OWASP LLM Top 10 + MITRE ATLAS attack packs
- [`guardpost`](https://github.com/cognis-digital/guardpost) — Runtime agent firewall — PII redaction, rate limits, policy enforcement
- [`hallumark`](https://github.com/cognis-digital/hallumark) — LLM hallucination & grounding auditor for RAG systems
- [`aicard`](https://github.com/cognis-digital/aicard) — Auto-generated NIST AI RMF / EU AI Act Annex IV model & system cards

**Explore the suite →** [🗂️ all 170+ tools](https://github.com/cognis-digital/cognis-neural-suite) · [⭐ awesome-cognis](https://github.com/cognis-digital/awesome-cognis) · [🔗 cognis-sources](https://github.com/cognis-digital/cognis-sources) · [🤖 uncensored-fleet](https://github.com/cognis-digital/uncensored-fleet) · [🧠 engram](https://github.com/cognis-digital/engram)

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<a name="contributing"></a>
## Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see [CONTRIBUTING.md](CONTRIBUTING.md) and [SECURITY.md](SECURITY.md).

> ### ⭐ If `ledgermind` saved you time, **star it** — it genuinely helps others find it.

## Interoperability

`{}` composes with the 300+ tool Cognis suite — JSON in/out and a shared
OpenAI-compatible `/v1` backbone. See **[INTEROP.md](INTEROP.md)** for the
suite map, composition patterns, and reference stacks.

## License

Source-available under the **Cognis Open Collaboration License (COCL) v1.0** — free for personal, internal-evaluation, research, and educational use; **commercial / production use requires a license** (licensing@cognis.digital). See [LICENSE](LICENSE).

---

<div align="center"><sub><b><a href="https://cognis.digital">Cognis Digital</a></b> · one of 170+ tools in the <a href="https://github.com/cognis-digital/cognis-neural-suite">Cognis Neural Suite</a> · <i>Making Tomorrow Better Today</i></sub></div>