Garudex-Labs

Garudex-Labs / ase

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Agent Settlement Extension (ASE) is an metadata layer that extends existing agent-to-agent (A2A) and Model Control Protocol (MCP) communication protocols with authority semantics.

pypi.orgprojectase-protocol economics interoperability metadata protocols settlement
21
6
100% credibility
Found Feb 01, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python toolkit implementing the Agent Settlement Extension protocol to add billing, auditing, disputes, and secure payments to AI agent conversations.

How It Works

1
👀 Discover ASE

You hear about ASE, a helpful toolkit that lets AI assistants track and bill for their work fairly and securely.

2
📥 Get the toolkit

You grab the ready-to-use pieces and add them to your AI project setup.

3
🔗 Connect to your AI helpers

You link ASE smoothly to your chatting AI tools so they can share billing details effortlessly.

4
💰 Set billing rules

You define simple rules for how much different tasks cost, like time or effort used.

5
🛡️ Add security seals

You put strong locks on messages to make sure bills can't be faked or changed.

6
✅ Test everything works

You run sample tasks and check that charges appear correctly with full records.

🎉 Agents bill automatically

Your AI assistants now handle payments between themselves securely, saving you time and worry.

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AI-Generated Review

What is ase?

ASE is a Python library that layers economic metadata onto agent-to-agent communications, extending protocols like A2A and MCP with charges, budgets, delegation tokens, and audits. It lets you wrap messages in LangChain or AutoGPT flows with settlement semantics—provisional charges, disputes, versioning—handling serialization, validation, and crypto signing out of the box. Ideal for agent GitHub repos needing built-in billing without custom agent settlement forms.

Why is it gaining traction?

Ready adapters for LangChain and AutoGPT make it drop-in for agent GitHub Copilot setups or OpenAI/Microsoft agent GitHub code, skipping boilerplate crypto and validation. Property-based tests and extension hooks appeal to devs building robust agent GitHub actions, while JSON schemas ensure interoperability in multi-agent systems.

Who should use this?

LangChain/AutoGPT builders tracking costs in production agents, like AI service providers charging per compute or API call. Suited for teams in agent GitHub Copilot IntelliJ/Reddit communities prototyping monetized workflows, or OpenAI agent GitHub repos with hierarchical delegations.

Verdict

Promising ASE Python GitHub reference at v1.1.0-beta with solid docs and tests, but 21 stars and 1.0% credibility signal early days—prototype only, not prod-ready. Worth forking if agent economies are your jam.

(178 words)

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