com.undercurrentholdings/aegis
Six-gate governance for AI agents: PROCEED/PAUSE/HALT decisions with hash-chained audit trails.
Open source Repository Open in the app JSON README (API)
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Six-gate governance for AI agents: PROCEED/PAUSE/HALT decisions with hash-chained audit trails.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- com.undercurrentholdings
- Origin
- official
- Category
- ferramentas
- Transport
- http
- Version
- 1.3.1
- Last push
- 2026-06-10T23:32:52Z
- Repository state
- ativo
- Language
- Dockerfile
- License
- MIT
- Added
- 2026-08-29 03:01:26
- Updated
- 2026-08-29 03:01:26
- Origin id
com.undercurrentholdings/aegis
README
# AEGIS Governance — MCP Server
[](https://pypi.org/project/aegis-governance/)
[](https://aegis.undercurrentholdings.com/docs)
**Quantitative governance for AI agents and engineering decisions.** AEGIS evaluates proposals through six quantitative gates — Risk, Profit, Novelty, Complexity, Quality, Utility — and returns a structured decision (`PROCEED` / `PAUSE` / `HALT` / `ESCALATE`) with confidence scores, rationale, and a hash-chained audit trail.
Give your agent a decision gate it can call before it acts — and an audit record compliance can actually read (NIST AI RMF, EU AI Act Annex IV).
- **Works immediately, no signup**: the local server runs in sandbox mode (10 evaluations/day).
- 6 local tools (evaluations, risk checks, health, decision history, usage) — 10 on the hosted server.
- Hosted server with hash-chained audit trails — [free Community tier](https://portal.undercurrentholdings.com/signup?product=aegis&utm_source=github&utm_medium=aegis-mcp) (100 evaluations/month, no credit card).
- Want to see it before connecting? [Try the Advisor in your browser](https://aegis.undercurrentholdings.com/advisor) — no install, no signup.
## Quickstart (local, no account needed)
```bash
pip install "aegis-governance[mcp]"
```
**Claude Code**
```bash
claude mcp add aegis -- aegis-mcp-server
```
**Cursor** (`.cursor/mcp.json`) / **Windsurf** / any stdio MCP client:
```json
{
"mcpServers": {
"aegis": { "command": "aegis-mcp-server" }
}
}
```
**VS Code** (`.vscode/mcp.json`):
```json
{
"servers": {
"aegis": { "type": "stdio", "command": "aegis-mcp-server" }
}
}
```
Runs in sandbox mode out of the box. Set `AEGIS_API_KEY` in the server's
environment ([free key](https://portal.undercurrentholdings.com/signup?product=aegis&utm_source=github&utm_medium=aegis-mcp))
to unlock decision history, usage reports, and risk checks. Requires Python >= 3.10.
## Hosted server (streamable-http, full 10-tool surface)
Get a free API key at [portal.undercurrentholdings.com](https://portal.undercurrentholdings.com/signup?product=aegis&utm_source=github&utm_medium=aegis-mcp) (GitHub/Google sign-in, key provisioned automatically), then:
**Claude Code**
```bash
claude mcp add --transport streamable-http aegis https://mcp.aegis.undercurrentholdings.com/mcp \
--header "Authorization: Bearer YOUR_API_KEY"
```
**Cursor** (`.cursor/mcp.json`) / **Windsurf** / any streamable-http MCP client:
```json
{
"mcpServers": {
"aegis": {
"type": "streamable-http",
"url": "https://mcp.aegis.undercurrentholdings.com/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
```
**VS Code** (`.vscode/mcp.json`):
```json
{
"servers": {
"aegis": {
"type": "http",
"url": "https://mcp.aegis.undercurrentholdings.com/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
```
## Prefer a local SDK instead of MCP?
The Python SDK has a sandbox mode that works with no account at all (10 evaluations/day):
```bash
pip install aegis-governance
```
```python
from aegis import Aegis
decision = Aegis().evaluate(
proposal_summary="Add Redis caching layer to reduce API latency",
risk_baseline=0.02, risk_proposed=0.05,
novelty_score=0.75, complexity_score=0.8, quality_score=0.9,
)
print(decision.status) # "proceed"
```
> The local stdio MCP server above ships in `aegis-governance` >= 1.3.0 via the `[mcp]` extra.
## Tools
| Tool | What it does |
|------|--------------|
| `aegis_evaluate_proposal` | Full six-gate evaluation of a proposal; returns PROCEED/PAUSE/HALT/ESCALATE with per-gate scores and rationale |
| `aegis_quick_risk_check` | Fast risk screen for a proposed change |
| `aegis_check_thresholds` | Current gate threshold configuration |
| `aegis_get_scoring_guide` | Domain-specific guidance for deriving gate parameters (e.g. `cicd`) |
| `aegis_record_proposal` | Record a proposal for later verification |
| `aegis_list_proposals` | List recorded proposals |
| `aegis_verify_proposals` | Verify recorded proposals against outcomes |
| `aegis_list_decisions` | List past governance decisions |
| `aegis_get_decision` | Fetch a specific decision with full audit detail |
| `aegis_crypto_status` | Hash-chain audit integrity status |
## Why a governance gate?
AI agents make thousands of decisions with no record of why. AEGIS gives every consequential action a quantitative evaluation and a tamper-evident audit entry — so "the agent decided to deploy" becomes a signed, replayable record with gate scores and rationale.
- **Six gates**: Risk, Profit, Novelty, Complexity, Quality, Utility — calibrated thresholds, KL-divergence drift detection
- **Audit-ready**: hash-chained decision log; NIST AI RMF and EU AI Act Annex IV artifact generation
- **Five integration surfaces**: MCP (this repo), Python SDK, REST API, CLI, GitHub Action
## Links
- **Docs**: [aegis.undercurrentholdings.com/docs](https://aegis.undercurrentholdings.com/docs) · [MCP tools reference](https://aegis.undercurrentholdings.com/docs/api/mcp-tools)
- **Try it in the browser** (no install): [AEGIS Advisor](https://aegis.undercurrentholdings.com/advisor)
- **Pricing**: [portal.undercurrentholdings.com/pricing](https://portal.undercurrentholdings.com/pricing) — free Community tier; paid tiers for teams and regulated environments
- **Source distribution**: [PyPI `aegis-governance`](https://pypi.org/project/aegis-governance/) (BSL-1.1)
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