preloop 0.9.2

Preloop - Control plane & safety layer for your agentic AI workforce

Preloop - The Open-Source AI Agent Control Plane

Preloop is the open-source AI agent control plane. It unifies an MCP firewall for tool access, an AI model gateway for cost, safety and attribution, policy-as-code with human approvals, runtime session observability, and audit trails - in a single self-hostable platform.

Use Preloop to onboard existing agents with one command, and to deploy event-driven agentic automations with governed tools and budgets.

Works with OpenClaw, Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenCode, Windsurf, and any MCP-compatible agent or managed runtime.

Run preloop agents discover and Preloop will find local agent configs, import representable MCP servers and model metadata, and transparently rewrite those agents to route tool calls through the Preloop MCP Firewall and model traffic through the Preloop Gateway. No SDK changes and no agent code changes required.

Build automations with templates like the Pull Request Reviewer, or write your own.

Official documentation: Full guides and tutorials at docs.preloop.ai.

# Install the standalone CLI
curl -fsSL https://preloop.ai/install/cli | sh

What is Preloop?

Preloop is a single open-source platform that covers the five jobs teams otherwise buy from four different vendors:

Capability	What it does	Alternatives
MCP Firewall	Govern every tool call an agent makes. Allow, deny, require approval, require justification. YAML + CEL policies.	MintMCP, Lunar.dev MCPX, TrueFoundry MCP Gateway
AI Model Gateway	OpenAI- and Anthropic-compatible gateway with per-account/flow budgets, allowed-model lists, token accounting, and runtime attribution.	Portkey, Helicone, LiteLLM, Kong AI
Human Approvals	Mobile, watch, Slack, Mattermost, email, or webhook notifications with one-tap decisions and full context. Async-safe.	Custom Slack bots, Peta Desk
Runtime Observability	Session-level timeline of tool calls, model calls, policy decisions, approvals, spend, and outcomes across agents.	AgentOps, Langfuse, LangSmith
Audit & AI Act Evidence	Durable logs with matched policy, approver, inputs, timestamps, and outcome. Ready for security review and EU AI Act work.	Credo AI, IBM watsonx.governance

All shipped as Apache 2.0 software that runs on your infrastructure.

Why Preloop?

AI agents like Claude Code, Cursor, and OpenClaw are transforming how we work. But agents now deploy code, touch production data, change infrastructure, and spend money — and traditional IAM, prompt rules, and manual review were never built for that.

• Accidental deletions. One wrong command and your production database is gone.
• Leaked secrets. API keys pushed to public repos before anyone notices.
• Runaway costs. Agents spinning up expensive cloud resources without limits.
• Breaking changes. Untested deployments to production at 3am.

Most teams face an impossible choice: give AI full access and move fast (but dangerously), or lock everything down and lose the productivity gains.

Preloop solves this. Govern what agents are allowed to do, route risky actions to the right human, attribute model spend to the right team, and keep a searchable record of every important decision — without rebuilding your stack or instrumenting SDKs.

AI Agent → Preloop → [Policy check] → Allow / Deny / Require Approval → Execute
                   → [Gateway]       → Budget + attribution             → Model

Core Capabilities

Managed Agent Onboarding (preloop agents discover)

One command discovers and enrolls existing local agents into your control plane.

preloop agents discover

Preloop inspects local configurations for Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenClaw, OpenCode, and other MCP-compatible runtimes, imports representable MCP servers and model metadata into your account, mints a durable credential, backs up the existing config, and rewrites the local agent to use Preloop-managed endpoints. Legacy and current config locations are supported, JSON5/YAML parsing included. No SDK. No agent code changes.

Access Policies & Approval Workflows

Define fine-grained access controls for any AI tool or operation. Tools support multiple ordered access rules that evaluate in priority order. When an AI attempts a protected operation, Preloop pauses and notifies you:

• Instant notifications via mobile app, email, Slack, Mattermost, or custom webhook.
• One-tap approvals from your phone, watch, or desktop.
• Async approval mode lets the agent poll for status instead of blocking network hooks.
• Per-tool justification — require (or optionally request) the agent to explain why a tool is being called.
• Full Audit Trail — every action is logged with full context: what was attempted, the matched policy, execution duration, and who approved it.

Policy-as-Code

Define policies in YAML and manage via CLI or API to version-control your safeguards alongside your infrastructure:

# Example: Require approval for production deployments
version: "1.0"
metadata:
  name: "Production Safeguards"
  description: "Require approval before deploying"

approval_workflows:
  - name: "deploy-approval"
    timeout_seconds: 600
    required_approvals: 1
    async_approval: true

tools:
  - name: "bash"
    source: mcp
    approval_workflow: "deploy-approval"
    justification: required
    conditions:
      - expression: "args.command.contains('deploy') && args.command.contains('production')"
        action: require_approval

AI Model Gateway

Preloop safely routes model traffic on behalf of managed runtimes instead of handing provider credentials to potentially vulnerable agent containers.

• OpenAI-compatible (/openai/v1/models, /openai/v1/chat/completions, /openai/v1/responses) and Anthropic-compatible (/anthropic/v1/messages) endpoints with SSE streaming.
• Budget enforcement at account, flow, and subject scopes using configurable cost tracking limits.
• Allowed-model lists per account, flow, API key, or managed agent.
• Usage accounting persisted as a canonical ApiUsage ledger — token usage, estimated cost, runtime-principal attribution, and provider-neutral conversation previews.
• Secret custody — provider API keys stay with Preloop; runtimes receive short-lived gateway tokens instead of raw credentials.

Runtime Session Observability

A durable RuntimeSession layer gives you one timeline per managed runtime — flow executions today, and any onboarded CLI/desktop agent session going forward. Operator-scoped endpoints expose recent sessions plus captured gateway interactions so the console can drill from aggregate usage into a single session timeline. Operators can end a session explicitly; doing so updates runtime state, emits audit events, and refreshes managed-agent summaries.

Getting Started

Install Preloop agents and components locally using our provisioning scripts.

# Install the standalone CLI
curl -fsSL https://preloop.ai/install/cli | sh

# Install the OSS platform stack
curl -fsSL https://preloop.ai/install/oss | sh

For extended details detailing comprehensive Docker builds, Kubernetes Helm topologies, GraphQL configuration, WebSocket streaming channels, and deep .env definitions, refer to the Preloop Documentation Hub.

The Open-Source Alternative to AWS Bedrock AgentCore

Preloop covers the same core jobs as AWS Bedrock AgentCore (runtime, gateway, identity, observability, policy) but is open source, self-hostable, MCP-native, and vendor-neutral. Many teams adopt Preloop specifically as an open-source alternative to AWS Bedrock AgentCore when they want to avoid hyperscaler lock-in or need to run governance inside their own VPC or on-prem.

Feature	Preloop	AWS Bedrock AgentCore
Open source (Apache 2.0)	✅	❌
Self-hostable (VPC / on-prem)	✅	❌
Policy-as-code (YAML + CEL)	✅	Limited
MCP-native tool governance	✅	Partial
Model gateway with budgets & attribution	✅	✅
Human-in-the-loop approval workflows	✅ (mobile, Slack, webhook)	Limited
Works with any agent runtime	✅	AWS-centric
Vendor lock-in	None	AWS
Onboard existing local agents with one command	✅ (preloop agents discover)	❌

How Preloop Compares to Other Categories

Category	Common tools	How Preloop differs
AI gateways / LLM proxies	Portkey, Helicone, LiteLLM, Kong AI	Preloop's gateway is bundled with an MCP firewall, approval workflows, and runtime observability — you do not need to stitch four products together.
MCP gateways	MintMCP, Lunar.dev MCPX, TrueFoundry	Preloop is open-source and includes a first-class AI model gateway, not just MCP tool routing.
AgentOps / observability	Langfuse, LangSmith, Braintrust, AgentOps.ai	Preloop adds runtime enforcement (policy, approvals, budgets), not just tracing.
AI runtime security	Lakera, Lasso, Zenity, Noma	Preloop is developer-facing, MCP-native, and self-hostable. Complementary to semantic content-safety firewalls.
AI governance suites	Credo AI, IBM watsonx, OneTrust	Preloop focuses on runtime controls agents actually hit, not just top-down inventory and risk artifacts.

Enterprise Features

Preloop Enterprise Edition extends the core open-source components with centralized RBAC capabilities:

Feature	Open Source	Enterprise
Basic approval workflows	✅	✅
Issue tracker integrations	✅	✅
Agentic flows & Vector search	✅	✅
Role-Based Access Control (RBAC)	❌	✅
Team management & Admin Dashboard	❌	✅
CEL conditional approval workflows	❌	✅
AI-driven approval logic	❌	✅
Team-based approvals with quorum	❌	✅
Approval escalation	❌	✅

Contact [e-mail] for Enterprise Edition licensing requests.

Contributing

Contributions are welcome! Please see our Contributing Guidelines for details on how to get started.

License

Preloop is open source software licensed under the Apache License 2.0. Copyright (c) 2026 Spacecode AI Inc.