Overview

Alephant is the open-source Agentic Finance Gateway for AI agents and workflows.

It provides the policy, tracing, and payment layer for production agent systems: govern every run, route model calls, control budgets and tool access, track usage and cost, and publish agent capabilities as paid endpoints with x402 and MPP payment rails.

As AI agents move from simple chat interactions to long-running workflows, they start making decisions across models, tools, APIs, and payment rails. Alephant sits between agents and the systems they call, giving teams a single gateway to control what agents can access, what they can spend, how they are audited, and how their capabilities can be monetized.

Core Capabilities

Agent Gateway: Route LLM calls across providers and models through a unified gateway.
Policy Control: Govern agent and workflow runs with budgets, rate limits, model allowlists, tool permissions, endpoint access rules, and approval policies.
Run Tracing: Track each agent run across model calls, tool calls, workflow steps, latency, errors, usage, and audit logs.
Cost & Revenue Logs: Attribute token cost, external API/tool spend, x402 outbound spend, endpoint revenue, and net margin to each agent or workflow.
x402 Monetization: Publish agent capabilities as paid endpoints and accept machine-to-machine payments through x402 and MPP rails.
Workflow Governance: Apply the same policy, tracing, and finance controls to agents, members, and automated workflows.

Positioning

Alephant is not just an AI Gateway or an LLM cost dashboard.

It is built for the Agent Era, where agents and workflows do not only call models. They access tools, trigger APIs, spend budget, create audit trails, and expose capabilities that other agents can pay to use.

Alephant gives these systems a control plane:

rules for what they can do, receipts for what happened, and revenue rails for what they can sell.

One-Line Summary

Alephant is the open-source Agentic Finance Gateway for governing AI agents and workflows, tracing every run, and monetizing agent capabilities with x402 payments.

Why Alephant

AI agents are moving from chat to work.

They do not just answer questions anymore. They call models, use tools, trigger workflows, access APIs, spend budget, and expose capabilities that other agents or applications can call.

That creates a new infrastructure problem.

Teams need to know:

What did the agent run?
Which model did it use?
Which tools did it access?
How much did it spend?
Which policy allowed or blocked the action?
What logs prove what happened?
Can this capability be turned into a paid endpoint?

Traditional AI gateways focus on model routing and token usage. That is useful, but too narrow for production agents.

Alephant is built for the full agent run.

It gives AI agents and workflows a policy, tracing, and payment layer:

Policy control for budgets, rate limits, model access, tool permissions, and workflow guardrails
Run tracing across model calls, tool calls, workflow steps, latency, errors, and audit logs
Cost logs for token spend, tool/API spend, x402 outbound spend, and per-agent economics
x402 monetization for publishing agent capabilities as paid endpoints
Workflow governance across agents, members, and automated workflows

Alephant exists because agents are becoming economic actors.

They need rules for what they can do.
They need receipts for what happened.
They need revenue rails for what they can sell.

Alephant is the open-source Agentic Finance Gateway for production AI agents and workflows.