preloop

Preloop

The open-source AI agent control plane and software factory.

Preloop is one self-hostable service that sits between your AI agents and everything they reach, and runs agents for you when work arrives. Every tool call and every model call is governed, attributed and visible, whether a person started the agent or a ticket did. You see gateway spend by model, agent, session, API key and flow, and cap it with budgets. Imported Copilot usage is displayed separately as vendor-reported data.

It has two halves, and the same policies, budgets and session timeline apply to both:

• Control plane. An MCP firewall that allows, denies or holds any tool call for human approval; an AI model gateway with budgets and attribution; policy-as-code in YAML and CEL; one runtime timeline per session.
• Software factory. Flows run agents, in hosted containers or on your own runners, when GitHub, GitLab, Bitbucket Cloud, Jira, a webhook or a CI job fires. Presets review pull requests, implement issues, triage tickets, scan for vulnerabilities and collect evidence packs. A human can steer a running agent from the console, phone or watch, and the work lands as pull requests you can review.

Who it is for:

• Platform and security teams that need agents governed before they reach production systems.
• Engineering teams running coding agents that want to see the cost and keep an audit trail.
• Teams that want tickets and events turned into bounded agent work they can review.

MCP clients connect to /mcp; existing /mcp/v1 configurations remain compatible. See the tested MCP protocol matrix for modern discovery, legacy initialization, and SDK migration details.

Works with OpenClaw, Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenCode, Devin, Copilot CLI, and any MCP-compatible agent.

Your first five minutes

# 1. Install the CLI (macOS / Linux / WSL)
curl -fsSL https://preloop.ai/install/cli | sh

# Windows, native (PowerShell): irm https://preloop.ai/install/cli.ps1 | iex
# Details: docs/windows-cli.md

# 2. Connect it to a control plane
preloop signup                                # Preloop Cloud (fastest), or
preloop login --url http://localhost:3000    # your self-hosted instance

# 3. Bring local agents under governance
preloop agents discover

preloop agents discover finds local agent configs and reports what each adapter supports. preloop agents onboard configures them: calls routed through the managed MCP entry reach the MCP Firewall, supported model routes use the Gateway. To talk to a running agent, install the runtime plugin with preloop agents install-plugin (or preloop claude for Claude Code). Full walkthrough: onboard local agents.

Watch it work

Onboarding, the MCP firewall, human approvals, and cutting session cost. Recorded against a real stack, no slideware.

What you get

Jobs teams otherwise buy from several vendors, in one Apache 2.0 stack:

Capability	What it does	Alternatives
MCP Firewall	Govern every tool call. Allow, deny, require approval, require justification. YAML + CEL. Connect OAuth MCP servers with automatic token refresh.	MintMCP, Lunar.dev MCPX, TrueFoundry
AI Model Gateway	OpenAI-, Anthropic- and Gemini-compatible. Budgets, allowed-model lists, token accounting, attribution.	Portkey, Helicone, LiteLLM, Kong AI
Flows	Start an agent when a tracker, webhook, or CI job fires, with the same firewall, approvals, and cost. preloop flow trigger.	Custom CI glue, AgentCore Runtime
Private runners	Run flow agents on your own machines over an outbound WebSocket, no inbound ports.	Self-managed CI runners
Agent Control	Talk to long-running agents from the console, phone, or watch. Steer runs with operator notes.	Terminal access
Cost & Budgets	Spend by model, agent, session, API key, flow, and user, including imported Copilot usage.	FinOps dashboards, vendor billing exports
Human Approvals	Mobile, watch, Slack, Mattermost, email, webhook, or preloop approvals. Native Bash/Edit. Agents can ask_user.	Custom Slack bots, Peta Desk
Runtime Observability	One session timeline: tool calls, model calls, policy, approvals, spend, outcomes, and synchronized recordings.	AgentOps, Langfuse, LangSmith
Fleet Map	People, devices, agents, models and observed work in one map.	Spreadsheets
Evidence packs	Apache flow presets write result.json plus an evidence directory for CRA / AI Act-style work. Not a certification.	Custom GRC folders

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

The software factory

One loop, end to end: an issue event arrives from GitHub, GitLab, Bitbucket Cloud or Jira. A flow starts an agent in a hosted container or on your own runner. Every tool call it makes passes the same firewall and approvals as your own agents, and every model call is charged to the flow's budget. The agent opens a pull request; the Pull Request Reviewer reviews it; review or CI feedback resumes the implementation on the same branch, and the result is a pull request you can review. You can steer the run with an operator note at any point, and the whole run is one timeline.

21 presets ship in this repository, including:

• Issue Triage Assistant and issue refinement
• Pull Request Reviewer
• Delivery Report: a weekly report with proposals for human decisions and project reruns. See what landed, what the available evidence supports against stated criteria, and which gaps deserve investigation
• Automated issue implementation
• Security audit presets: SBOM verify, exploit check, release audit. They collect machine evidence for CRA- and EU AI Act-style reviews. That is not a conformity assessment, certification, or legal advice.
• Observe / Eval
• Component due diligence, architecture and strategy review, repo code health review
• Standards compliance walk, release backport, weekly model price review

Or write your own. Trigger from CI with the run-flow GitHub Action; run agents on private runners; let a flow start child flows with flow delegation. Tracker setup: Bitbucket and Jira quickstart; Bitbucket Data Center is available as an opt-in provider.

Policy-as-code

version: "1.0"
metadata:
  name: "Production Safeguards"

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

Check a sample call before saving with Test this rule or Simulate in the console. Ship it with preloop policy apply <file> (validate / diff / export also exist).

More capabilities

• Model providers: OpenAI, Anthropic, Google, Amazon Bedrock, Azure OpenAI, Alibaba Cloud Model Studio, DeepSeek, Mistral, Moonshot, Z.ai, OpenRouter, any OpenAI-compatible endpoint, and runner-proxied local models. Reviewed model pricing; unpriced models stay visibly unpriced.
• Restricted CI identity: a machine identity bound to one project and one flow for CI jobs. Guide.
• Account kill switch: blocks gateway and tool traffic and stops managed flow runs, with audited recovery. Guide.
• Durable audit delivery: opt-in transactional intents for approvals, policy decisions and tool outcomes, with restart replay and backlog alerts. Operations guide.
• Session search: keyword search by default, semantic as an opt-in, every search audited. Saved searches.
• DORA exports: the AI-agent slice of the ICT inventory and incident record. Guide.
• OTLP export: off by default; emits OpenTelemetry GenAI spans. Guide.
• Chat: Slack, Mattermost, and Discord for questions, approval votes and operator notes. Chat connections.
• Event-driven employees: managed agents doing bounded tasks from tracker and error events. Guide.
• Remote sessions and deployment: remote sessions, SSH and GCP agent deployment, environments and recovery.
• Runtime plugins: Pi and DeepSeek Harness, Nanobot, OpenClaw, Hermes, runtime adapters.

Everything else: docs.preloop.ai.

Getting started

The CLI is a client. It talks to a control plane: Preloop Cloud or a stack you run.

Cloud (fastest)

curl -fsSL https://preloop.ai/install/cli | sh
preloop signup
preloop agents discover

Self-host (Docker Compose, data stays on your machine)

curl -fsSL https://preloop.ai/install/oss | sh
curl -fsSL https://preloop.ai/install/cli | sh
preloop login --url http://localhost:3000
preloop agents discover

Console: http://localhost:3000. CLI login commands: CLI authentication. TLS, SMTP, upgrades and Kubernetes: Install the OSS stack, Helm chart helm/preloop, compatibility policy. Docker Compose and Helm are the supported install surfaces; this repository does not ship Terraform modules. The local upgrade acceptance harness verifies release-to-candidate migrations, data and credentials in disposable Compose and kind stacks.

Credential storage upgrades keep the existing encryption key and run as a normal rolling upgrade; read the credential storage upgrade guide before upgrading an existing installation.

Production self-host: SECRET_KEY is required or the app refuses to start, and the Helm chart refuses to install while environment.jwtSecret is empty or a placeholder (see helm/preloop/README.md). The development docker-compose.yml ships development-only credential defaults; override them with a .env file (start from .env.example). Telemetry is a daily pseudonymous version check-in; set PRELOOP_DISABLE_TELEMETRY=true to disable. Event list: SECURITY.md.

Open-source alternative to AWS Bedrock AgentCore

Same core jobs (runtime, gateway, identity, observability, policy), vendor-neutral and self-hostable. Full comparison: preloop.ai/vs/aws-agentcore.

	Preloop	AWS Bedrock AgentCore
Open source (Apache 2.0)	Yes	No
Self-hostable (VPC / on-prem)	Yes	No
Policy-as-code (YAML + CEL)	Yes	Limited
MCP-native tool governance	Yes	Partial
Human approvals (mobile, Slack, webhook)	Yes	Limited
Onboard existing local agents (preloop agents discover)	Yes	No

Also compare: LiteLLM, Portkey, Helicone, MintMCP, Lunar, Runlayer, Zenity.

Editions

Unqualified Preloop is this repository (Apache 2.0, self-hosted). Preloop Cloud is the hosted service at preloop.ai. Preloop Enterprise is the commercial self-hosted edition. Cloud and Enterprise include support plans. Cloud billing owners can compare usage from the previous three completed months in Account settings before requesting a plan-change quote.

Your data: self-hosted, nothing leaves your network except the optional daily version check. On Preloop Cloud, sessions are stored only to give you search, audit trails, cost reports and policy enforcement; they are not used to train models and are not sold or shared. Details: Security & Privacy.

Governance itself is open source: every model provider, flows, private runners, session search, operator notes, the audit trail, single-approver workflows and budgets scoped to the account, a flow, an API key or a managed agent. What is commercial is the organizational layer on top:

Feature	Open Source	Cloud / Enterprise
Audit collection, query API, and console	Yes	Yes
Users, teams, and RBAC on one account	No	Yes
Budgets scoped to a user or a team, budget notification routing	No	Yes
Multi-approver routing, quorum, escalations	No	Yes

A self-hosted OSS instance is one operator per account. Public signup, if left on, creates a separate account, not a teammate. Enterprise licensing: sales@preloop.ai.

Working in this repository

If you need	Read
How the system fits together	ARCHITECTURE.md, then one chapter under docs/architecture/
Commands, DB/CRUD rules, Lit frontend	AGENTS.md
PR process	CONTRIBUTING.md
Operator and client guides	docs.preloop.ai
Policy examples	backend/presets/

Community

Discord for help, feedback, and the founder's build log.

Contributing

See CONTRIBUTING.md. Maintainers: follow the protected-branch release process.

License

Apache License 2.0. Copyright (c) 2026 Spacecode AI Inc.

Release assets carry signed Sigstore build provenance and checksums (verification). The release SBOM audit gate checks both architectures of every release image and the tag's security workflows. Windows CLI notes: windows-cli.md, code signing policy. Free code signing provided by SignPath.io, certificate by SignPath Foundation.