Swarm At Ledger
S

Swarm At Ledger

Swarm.at is an open ledger protocol based on hash chains, used to record and verify the action settlements of AI agents. It provides tamper - proof, auditable public records, supports multiple framework adapters, predefined workflow templates, and a reputation - based trust system, ensuring the transparency and trustworthiness of multi - agent collaboration.
2 points
6.3K

What is the Swarm.at Public Ledger?

The Swarm.at Public Ledger is a system specifically designed to provide trustworthy records for interactions and decisions between AI agents. You can think of it as an open, unalterable 'digital notebook' that records what tasks the AI agents have completed, when they were completed, and each new record is closely linked to the previous one through cryptographic technology. Anyone, including you, can check this notebook at any time to verify whether the records inside are true, complete, and have not been modified.

How to use the Swarm.at Public Ledger?

Using it is very simple. You don't need to run complex software. For ordinary users or developers, there are mainly three ways: 1) Query records and verify status through a simple web API; 2) Integrate it into your AI application using the officially provided SDK (Software Development Kit); 3) Let your existing AI assistant (such as Claude) directly use the ledger's functions through the MCP (Model Context Protocol) server. The system will automatically handle complex hash calculations and verifications.

Applicable Scenarios

This system is very suitable for AI collaboration scenarios that require transparency and auditability. For example: Multiple AI agents cooperate to complete a complex project (such as writing code, reviewing content), and the contributions and responsibilities of each agent need to be recorded; in sensitive fields such as finance, law, or medicine, the decision-making process of AI needs to be reliably recorded and traced; when developing an AI workflow platform, it is necessary to ensure that every operation step is undeniably recorded.

Main Features

Tamper-Proof Hash Chain
Each new record contains the 'fingerprint' (hash value) of the previous record. Once any old record is modified, the fingerprints of all subsequent records will not match, just like a broken chain, and it can be immediately detected.
Public Verification and Audit
The entire ledger is open to everyone. You can verify the integrity of the entire chain or query a single record through a simple API, command-line tool, or SDK without any account password.
Settlement Receipt System
After each action of an AI agent is recorded (settled), a unique receipt will be generated. With the hash value of this receipt, anyone can independently verify that the action actually occurred and confirm its position and time in the chain.
Agent Trust System
The system maintains a trust level (from untrusted to senior) for each AI agent based on its historical behavior. You can query the trust level of any agent and set a minimum trust requirement before entrusting important tasks.
Multi-Framework Adapter
Whether you use LangGraph, CrewAI, AutoGen, or other mainstream AI agent frameworks, there is a corresponding 'adapter' that can easily record the output of the agent to this ledger automatically without significantly modifying the original code.
Preset Workflow Blueprints
More than 50 verified general workflow templates (blueprints) are provided, covering scenarios such as software development, content review, and financial compliance. You can directly 'fork' these blueprints to quickly create executable workflows, and each step will be automatically settled.
Advantages
Extremely high transparency and auditability: All records are publicly available for query, establishing a strong foundation of trust.
Powerful tamper-proof guarantee: Based on hash chain technology, it ensures the non - tamperability of historical records.
Easy to integrate: Multiple ways of APIs, SDKs, and framework adapters are provided, reducing the usage threshold.
Rich ecosystem: A large number of workflow blueprints and tools are built - in, ready to use, accelerating development.
Permissionless verification: Third parties can independently verify the authenticity of records without relying on the platform.
Limitations
Performance overhead: Each settlement requires hash calculation and verification, which may introduce delays for extremely high - frequency micro - operations.
Data publicity: All settlement contents are publicly available by default, which is not suitable for recording highly sensitive or confidential information (although the content itself can be a hash or digest).
Initial learning cost: It is necessary to understand concepts such as settlement, blueprint, and trust score, which may seem complex for simple applications.
Dependence on the external network: Verification and settlement usually require access to Swarm.at's public services.

How to Use

Quick Verification (for all users)
Even if you are not a developer, you can easily verify the health status of the ledger. Just access its public API interface.
Query Specific Information
You can query specific settlement records (receipts) through the task ID or settlement hash value.
Integrate into a Python Project (for developers)
If you are developing an AI application, you can record the action results of the agent to the ledger by installing the SDK and calling a few lines of code.
Use through MCP (for AI assistant users)
If you use an AI assistant such as Claude that supports MCP, you can configure the MCP server to allow your assistant to directly obtain the capabilities of querying the ledger, verifying trust, and executing blueprints.

Usage Examples

Example 1: Verify Open - Source Code Contributions
An open - source project uses multiple AI agents for code review. Every time an agent completes the review of a file, its conclusion (such as 'Pass', 'Needs to modify line XX') will be settled to the public ledger. Project maintainers can publicly share the hashes of these settlements, and anyone in the community can verify whether these review conclusions are true and whether they have been tampered with afterwards.
Example 2: Multi - Agent Collaborative Content Creation
A team uses the 'Research - Write - Review' blueprint to create a technical article. The researcher agent settles the collected materials, the writer agent starts writing based on this settlement and settles again, and finally the reviewer agent settles its review comments. Every step of the entire creation process is traceable, forming a trustworthy creation traceability chain.
Example 3: Supplier Audit Automation Workflow
The company's procurement system integrates the Swarm.at blueprint. When a new supplier applies, the system automatically triggers the 'Supplier Verification' workflow: Agent A checks the business qualifications and settles, Agent B assesses the financial risks and settles, and Agent C makes a suggestion based on the results of the first two steps and settles. All decision - making bases and steps are auditable, meeting compliance requirements.

Frequently Asked Questions

Is there a fee for using the public ledger?
Is the data recorded in the ledger public? Can everyone see the content?
How is the 'trust level' calculated?
What if my AI agent framework is not in the adapter list?
What is the difference between a 'blueprint' and a 'workflow'?

Related Resources

Swarm.at Protocol Official Website
Understand the overall vision and core ideas of the project.
Public API Interface
Directly access the available public API endpoints for instant query and verification.
Blueprint Directory
Browse all available preset workflow templates and view their details.
Technical Specification Document
Detailed protocol technical specifications, suitable for developers to read in - depth.
Real - Time Data Dashboard
View the real - time statistical data of the ledger, such as the total number of settlements and active agents.
PyPI SDK Page
The release page of the Python SDK, where you can view the version history and installation commands.

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
6.6K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
6.7K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
5.8K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
5.7K
4 points
P
Paperbanana
Python
7.1K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
7.2K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
7.9K
5 points
A
Apify MCP Server
The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.
TypeScript
6.9K
5 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
25.3K
4.3 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
21.9K
4.5 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
73.7K
4.3 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
36.4K
5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
33.5K
5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
65.1K
4.5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
21.5K
4.5 points
C
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
98.6K
4.7 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
© 2026AIBase