AI Skill Hub 推荐使用:AegisFlow代理治理系统 是一款优质的MCP工具。AI 综合评分 7.8 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。
AegisFlow代理治理系统 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
AegisFlow代理治理系统 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/saivedant169/AegisFlow
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"aegisflow------": {
"command": "npx",
"args": ["-y", "aegisflow"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 AegisFlow代理治理系统 执行以下任务... Claude: [自动调用 AegisFlow代理治理系统 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"aegisflow______": {
"command": "npx",
"args": ["-y", "aegisflow"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p align="center"> <h1 align="center">AegisFlow</h1> <p align="center"> <strong>Local-first runtime governance for coding agents and tool-using agents</strong> </p> <p align="center"> Let agents read, test, edit, and open PRs safely. Block destructive actions,<br/> review risky writes, mint short-lived credentials, and verify tamper-evident evidence. </p> <p align="center"> <a href="#start-here-governed-pr-writer">Start Here</a> | <a href="#what-aegisflow-controls">What It Controls</a> | <a href="#how-it-works">How It Works</a> | <a href="#design-principles">Design Principles</a> | <a href="#legacy--supporting-capabilities">Gateway Mode</a> | <a href="#contributing">Contributing</a> </p> </p>
---
Start here: See the governed PR-writer walkthrough with blocked shell actions, reviewed GitHub writes, scoped credentials, and verifiable evidence. → docs/PR_WRITER.md
The sections below cover gateway mode — AegisFlow's earlier identity as an OpenAI-compatible AI gateway with policy, observability, and mock provider. Gateway mode is still fully supported and is what powers the governance plane internally, but the primary product story is governed agent execution above. If you arrived here looking for an AI gateway, this is the right place.
Grab a prebuilt binary (no Go toolchain needed):
curl -fsSL https://raw.githubusercontent.com/saivedant169/AegisFlow/main/scripts/install.sh | sh
Or run the published container:
docker run --rm -p 8080:8080 -p 8081:8081 \
-v "$PWD/configs/aegisflow.yaml:/app/configs/aegisflow.yaml" \
saivedant169/aegisflow:v0.8.0
Building from source still works (make build, Go 1.26.4+) if you prefer.
---
git clone https://github.com/saivedant169/AegisFlow.git
cd AegisFlow
docker compose -f deployments/docker-compose.yaml up
Putting nginx or Caddy in front for TLS, SSE buffering, and admin-port isolation: see docs/deploy/reverse-proxy.md. Hit a snag? docs/troubleshooting.md covers port conflicts, Docker daemon issues, GitHub App config, invalid policy files, and evidence-verify failures.
brew install go
git clone https://github.com/saivedant169/AegisFlow.git cd AegisFlow make build
The examples directory includes local-only configs and requests:
examples/configs/single-tenant.yamlexamples/configs/multi-tenant.yamlexamples/configs/policy-blocking.yamlexamples/requests/openai-compatible-curl.shRun an example config:
make build
./bin/aegisflow --config examples/configs/single-tenant.yaml
./examples/requests/openai-compatible-curl.sh
git clone https://github.com/saivedant169/AegisFlow.git
cd AegisFlow
make demo-local
```bash
./scripts/demo.sh
The demo walks through the full agent governance flow: allowed reads, blocked
destructive operations, human-in-the-loop approval for writes, and evidence
chain verification. See [`configs/demo.yaml`](configs/demo.yaml) for the
policy configuration and [`scripts/demo.sh`](scripts/demo.sh) for the script.
To run with Docker instead:
bash docker compose -f deployments/docker-compose.demo.yaml up --build ```
./scripts/realworld_demo.sh ```
The demo sends MCP tool calls through AegisFlow and demonstrates: - Allowed reads: github.list_repos, github.list_pull_requests pass through - Review required: github.create_pull_request enters the approval queue - Blocked destructive ops: github.delete_repo is rejected - Evidence chain: all decisions are recorded and verifiable
See configs/realworld.yaml for the policy configuration, scripts/mock-mcp-server.js for the mock server, and scripts/realworld_demo.sh for the full test script.
---
Every agent action is normalized into an ActionEnvelope:
type ActionEnvelope struct {
ID string // unique action ID
Actor ActorInfo // who: user, agent, session
Task string // declared task or ticket
Protocol string // MCP, HTTP, shell, SQL, Git
Tool string // github.create_pull_request, shell.exec
Target string // repo, host, table, service
Parameters map[string]any // normalized arguments
RequestedCapability string // read, write, delete, deploy, approve
CredentialRef string // to-be-issued or attached
PolicyDecision string // allow, review, block
EvidenceHash string // chain pointer
Justification string // model explanation, approval, policy match
}
---
```bash
make run ```
make run CONFIG=configs/demo.yaml
AegisFlow is configured via a single YAML file. See configs/aegisflow.example.yaml for the full annotated reference.
server:
port: 8080
admin_port: 8081
providers:
- name: "mock"
type: "mock"
enabled: true
default: true
tenants:
- id: "default"
api_keys: ["my-api-key"]
rate_limit:
requests_per_minute: 60
tokens_per_minute: 100000
routes:
- match:
model: "*"
providers: ["mock"]
strategy: "priority"
policies:
input:
- name: "block-jailbreak"
type: "keyword"
action: "block"
keywords:
- "ignore previous instructions"
- "ignore all instructions"
- "DAN mode"
- name: "pii-detection"
type: "pii"
action: "warn"
patterns: ["ssn", "email", "credit_card"]
output:
- name: "content-filter"
type: "keyword"
action: "log"
keywords: ["harmful-keyword"]
providers:
- name: "openai"
type: "openai"
enabled: true
base_url: "https://api.openai.com/v1"
api_key_env: "OPENAI_API_KEY"
models: ["openai-chat", "openai-fast"]
- name: "anthropic"
type: "anthropic"
enabled: true
base_url: "https://api.anthropic.com/v1"
api_key_env: "ANTHROPIC_API_KEY"
models: ["claude-sonnet-4-20250514"]
routes:
- match:
model: "openai-*"
providers: ["openai", "mock"]
strategy: "priority"
- match:
model: "claude-*"
providers: ["anthropic", "mock"]
strategy: "priority"
---
---
| Method | Endpoint | Description |
|---|---|---|
GET | /health | Health check |
POST | /v1/chat/completions | Chat completion (streaming and non-streaming) |
GET | /v1/models | List available models |
WS | /v1/ws | WebSocket endpoint for persistent connections |
| Method | Endpoint | Description |
|---|---|---|
GET | /health | Admin health check |
GET | /metrics | Prometheus metrics |
GET | /admin/v1/usage | Usage statistics per tenant |
GET | /admin/v1/providers | Provider status and health |
GET | /admin/v1/tenants | Tenant configuration summary |
GET | /admin/v1/policies | Active policy rules |
GET | /admin/v1/whoami | Current API key role and tenant |
GET | /admin/v1/analytics | Real-time analytics summary |
GET | /admin/v1/alerts | Recent anomaly alerts |
POST | /admin/v1/alerts/{id}/acknowledge | Acknowledge alert |
GET | /admin/v1/budgets | Budget statuses and forecasts |
GET | /admin/v1/cost-recommendations | Cost optimization recommendations |
GET | /admin/v1/audit | Query audit log (filter by actor, action, tenant) |
POST | /admin/v1/audit/verify | Verify audit chain integrity |
POST | /admin/v1/graphql | GraphQL admin API |
GET | /admin/v1/approvals | List pending approvals |
POST | /admin/v1/approvals/{id}/approve | Approve action |
POST | /admin/v1/approvals/{id}/deny | Deny action |
GET | /admin/v1/evidence/sessions | List evidence sessions |
GET | /admin/v1/evidence/sessions/{id}/export | Export session evidence (JSON) |
GET | /admin/v1/evidence/sessions/{id}/report | Human-readable Markdown report |
GET | /admin/v1/evidence/sessions/{id}/report.html | HTML evidence report |
POST | /admin/v1/evidence/sessions/{id}/verify | Verify session chain integrity |
GET | /admin/v1/credentials | List active credentials |
POST | /admin/v1/credentials/{id}/revoke | Revoke a credential |
GET | /admin/v1/manifests | List active task manifests |
POST | /admin/v1/manifests | Create task manifest |
GET | /admin/v1/manifests/{id}/drift | Get drift events for manifest |
GET | /admin/v1/tickets | List capability tickets |
GET | /admin/v1/sessions/{id}/risk | Session behavioral risk score |
POST | /admin/v1/test-action | Test policy decision without executing |
POST | /admin/v1/simulate | Simulate policy with full trace |
GET | /admin/v1/rollouts | List canary rollouts |
GET | /admin/v1/health/detailed | Detailed health with provider status |
GET | /admin/v1/supply-chain | Supply chain asset trust status |
---
warn/enforce mode blocks out-of-scope actions创新的代理治理方案,填补AI安全管理空白。Go实现性能优异,MCP标准兼容性强,但生态成熟度有限,适合前沿探索者。
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建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
总体来看,AegisFlow代理治理系统 是一款质量良好的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | AegisFlow |
| 原始描述 | 开源MCP工具:Runtime governance for coding agents and tool-using agents. Allow/review/block M。⭐20 · Go |
| Topics | 代理治理MCP工具编码代理安全控制GitHub集成 |
| GitHub | https://github.com/saivedant169/AegisFlow |
| License | Apache-2.0 |
| 语言 | Go |
收录时间:2026-06-10 · 更新时间:2026-06-11 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端