License Python 3.11+

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, mint managed runtime credentials, and rewrite supported agents to route tool calls through the Preloop MCP Firewall and model traffic through the Preloop Gateway. For Agent Control, the CLI provisions the credential/config contract and can delegate plugin installation to the runtime marketplace, but the plugin is what keeps the live control channel connected.

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

Preloop onboarding local agents into the control plane

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

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

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

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 supported local endpoints to Preloop-managed MCP and gateway URLs. Legacy and current config locations are supported, JSON5/YAML parsing included.

Managed onboarding has two layers: CLI provisioning and runtime behavior. The CLI can create credentials, write preloop.control configuration, and invoke runtime-native plugin installation where the target runtime supports that workflow. It cannot, by itself, make an unmodified agent process stay online for Agent Control. Live OpenClaw/Hermes Agent Control requires the standalone Preloop runtime plugin to be loaded in the agent process.

To make Talk appear for OpenClaw or Hermes, the agent must be active, have a preloop.control block with a valid runtime bearer token, and have the Preloop runtime plugin online. The normal CLI path is:

preloop agents onboard openclaw
preloop agents install-plugin openclaw
preloop agents validate openclaw
preloop agents onboard hermes
preloop agents install-plugin hermes
preloop agents validate hermes

After installing the plugin, restart the agent runtime. When the plugin connects to WS /api/v1/agents/control/ws and advertises capabilities, Preloop marks the Agent Control channel verified and the web/mobile Talk controls become available. The plugin-only path uses the same contract: install @preloop/openclaw-plugin or preloop-hermes-plugin, provide a valid preloop.control block, start the runtime, and let the plugin connect.

Preloop dashboard with live agent and gateway usage

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.

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

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.

Preloop should make model spend explainable, not just counted. The Console should have a dedicated Cost area with subviews that help operators answer:

• How much have we spent? Spend, token volume, and request counts over time by model, provider, account, flow, managed agent, runtime session, API key, and user.
• Who or what spent it? Attribution from ApiUsage, runtime principals, subject-scoped governance, and session timelines.
• Why was it spent? Drill-down from aggregate charts into session transcripts, model-call previews, tool calls, approvals, and flow outcomes.
• Was the outcome worth it? Enterprise analysis can use the account's default AI model to summarize sessions, compare spend against observed outcome, and flag low-value or failed runs.
• How could it be optimized? Enterprise recommendations can suggest cheaper models, prompt reductions, caching, batching, retry suppression, budget policy changes, or approval gates for expensive workflows.

The open-source edition should include a practical Cost Overview, usage drill-downs, and budget-health alerts from gateway account/flow limits. Enterprise Edition adds budget policy configuration and enforcement, negotiated provider pricing overrides, session optimization recommendations, FinOps workflows, AI-generated session value reviews, anomaly detection, chargeback/showback, credits and promotions, forecasting, approval escalations, and export/reporting features through backend plugins. The frontend remains shared and should expose advanced panels through feature flags.

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.

Preloop is evolving from an approval and gateway layer into Agent Control for long-running autonomous agents.

• Implemented today: account-scoped realtime topics, runtime-session identity, managed-agent enrollment, OpenClaw and Hermes config rewrites, MCP proxying, model-gateway routing, session timelines, operator lifecycle actions, WS /api/v1/agents/control/ws, and POST /api/v1/agents/{agent_id}/control/commands.
• Scaffolded today: console views for runtime sessions and managed agents, mobile/watch approval clients, WebSocket delivery for account events and approval updates, and native voice/dictation surfaces that can submit operator text turns.
• Runtime-plugin dependent: live OpenClaw and Hermes command delivery requires their native runtime plugin to honor preloop.control.control_ws_url, own reconnect/backoff and heartbeat/status loops, advertise capabilities, receive send_message envelopes, execute or inject operator messages into the active agent session, and keep any resulting tool/model work on the governed MCP and gateway paths. Without that plugin loaded, the agent can still be onboarded for MCP/gateway routing, but Agent Control is not enabled.
• Voice surfaces: the web console now supports inline Talk controls with browser-native STT/TTS first and server STT/TTS fallback through speech-capable AIModel rows. Mobile/watch apps should continue to prefer vendor-native speech APIs and submit normalized voice transcripts through the same Agent Control surface.
• Planned next: hardened command persistence/recovery, interruption semantics, richer status streaming, and production voice UX on mobile and watch.

Agent Control surfaces are intentionally layered: the web console is the primary operator surface for managed-agent presence, session context, and text commands; mobile apps add push-to-talk or typed handoff for urgent operator messages; Apple Watch favors short dictated replies and quick status/approval actions.

Agent Control messages are auditable user/operator turns for a selected runtime session. They are not hidden system prompts or policy overrides; any tool or model work they trigger still flows through the MCP firewall, model gateway, budget checks, and approval policies.

Siri and Google Assistant should be treated as invocation and handoff surfaces, not reliable arbitrary background agent transports. Native app code can capture intent, permissions, and user confirmation; Preloop keeps the authoritative control, audit, and agent-message state on the server.

Choose the path that matches what you want to evaluate:

• Fast public trial: deploy the self-contained Railway trial template. This gives you a public Preloop URL without manually provisioning a VM.
• Local laptop: install the OSS stack with the install script.
• Kubernetes/prod-like: use the Helm chart in helm/preloop.

Deploy on Railway

The Railway trial runs Preloop Console, API/gateway, worker/scheduler, Postgres with pgvector, and NATS in one Railway project. The default template is self-contained and does not depend on external managed databases or queues. It is intended for evaluation, not hardened production.

Until the public Railway template code is published, the button opens the checked-in template guide and service map in deploy/railway. After publishing, replace the link target with the Railway template URL.

# 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

Before promoting a hosted trial template, verify that the public URL loads the console, /api/v1/health responds, first-user sign-in or sign-up works, preloop agents discover can target the public URL, one gateway model call appears in the UI, and one MCP policy event appears in the audit timeline.

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.

Production requirement: The SECRET_KEY environment variable is required in production. Without it, the application will refuse to start. In development, a default key is used with a warning. Generate a secure key with: python -c "import secrets; print(secrets.token_urlsafe(32))"

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.

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

Contact sales@preloop.ai for Enterprise Edition licensing requests.

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

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