
Q2 2026 Update: What Shipped in Ergo's Agent Economy Stack
Two months after the first release. Three SDK packages, ten working examples, four AI framework adapters, an MCP server, and the full Note lifecycle. Here's what changed — and what we'd like to ship next.
TL;DR
v0.2 — Full Note Lifecycle
Reserve creation, Note redemption, Tracker deployment, batch settlement. The full Reserve → Note → Tracker → Acceptance Predicate pipeline ships in one SDK.
Four AI Framework Adapters
LangChain, OpenAI function calling, CrewAI, AutoGen. Working examples in the repo. MCP server for Claude Desktop / Cursor / any MCP-compatible client.
Three Languages — One Stack
ergo-agent-pay (TypeScript), ergo-agent-py (Python), ergo-agent-mcp (MCP server). Same primitives, different runtimes.
Basis Layer Documented
Off-chain IOUs + on-chain settlement. Combines low-cost rapid payments with on-chain finality when trust isn't enough.
What Shipped Since Launch
The ergo-agent-pay SDK launched on 2026-03-18 with three examples and the basic pay() / issueNote() API. Three days later, v0.2 added the full Note lifecycle. Since then:
2026-03-18v0.1.0Initial release: ErgoAgentPay class, policy engine, LangChain + OpenAI adapters, acceptance predicate helpers, NetworkClient. Examples 01-03.
2026-03-21v0.2.0Full Note lifecycle: createReserve(), redeemNote(), settleBatch(), deployTracker(). Examples 04-06 (orchestrator, API server, Python agent). Register decode helpers. New error codes.
2026-04 — early MayEcosystem build-outergo-agent-py (Python SDK). ergo-agent-mcp (MCP server). Examples 07-10 covering streaming pay, treasury multisig, CrewAI, AutoGen. Basis off-chain credit layer documented.
Three Packages, One Repo
Same primitives — Reserve, Note, Tracker, Acceptance Predicate — exposed in TypeScript, Python, and over the Model Context Protocol.
ergo-agent-payPrimary SDK. ErgoAgentPay class, policy engine, LangChain + OpenAI adapters, full Note lifecycle methods. NPM-installable.
ergo-agent-pyPython equivalent for non-Node agents. Works with LangChain Python, CrewAI, AutoGen, and any Python-native AI agent framework.
ergo-agent-mcpModel Context Protocol server. Plug into Claude Desktop, Cursor, or any MCP client — your AI assistant can now pay for things on Ergo testnet.
10 Working Examples
Each example is self-contained and runs against Ergo testnet. No mainnet ERG required — the testnet faucet covers everything.
Basic Payment
Send ERG, agent → agent. The hello-world of on-chain agent payments.
Note Payment
Issue a programmable Note instead of raw ERG. Redeem it later.
Acceptance Predicate
Embed a task-completion condition in the Note. Receiver only redeems if condition holds.
Orchestrator Budget
Parent agent issues budgeted Notes to 3 sub-agents with per-agent acceptance predicates.
API Payment Server
Express server verifies Note on-chain before serving the response. Includes client demo.
Python Agent
Python/LangChain agent that pays for API calls using Ergo Notes.
Streaming Pay
Continuous micropayments for streaming services (per-token billing for LLM inference).
Treasury Multisig
Multi-agent treasury controlled by m-of-n signatures. Spending policy enforced on-chain.
CrewAI Agents
Multi-agent crew where each agent has its own wallet and pays peers for sub-tasks.
AutoGen Agent
Microsoft AutoGen agent with Ergo payment tool registered for autonomous transactions.
Agent Framework Adapters
Drop-in integrations for the most common AI agent frameworks. Each one is one import + one line of registration.
LangChain
Drop-in tool that exposes pay(), issueNote(), checkNote() to any LangChain agent. Works with both JS and Python LangChain.
const tool = agent.asLangChainTool()
// Add to LangChain agent's toolkitOpenAI Function Calling
Returns a function definition compatible with OpenAI's tool-use API. Your GPT-4-class model can call pay() directly.
const fn = agent.asOpenAIFunction()
// Register with chat.completions.create({ tools: [fn] })CrewAI
Each CrewAI agent gets its own wallet and policy. Agents pay each other for sub-task delivery.
# example 09-crewai-agents
from ergo_agent_py import ErgoAgent
agent = ErgoAgent(...)AutoGen
Microsoft AutoGen agent with Ergo payment registered as a callable tool. End-to-end agent-to-agent settlement.
# example 10-autogen-agent
autogen_agent.register_tool(ergo_pay_tool)MCP (Claude / Cursor / etc.)
Model Context Protocol server. Any MCP-compatible client (Claude Desktop, Cursor, Zed, etc.) can pay on Ergo.
# Add to MCP config:
{ "ergo": { "command": "npx ergo-agent-mcp" } }Full Note Lifecycle
v0.2 covers the complete Reserve → Note → Tracker → Acceptance Predicate pipeline. These are the methods you call to issue, redeem, and settle programmable bearer instruments.
createReserve()Deploy a Reserve collateral box (P2PK for dev, custom ErgoScript for production).
issueNote()Mint a programmable Note from an existing Reserve. Encodes value, expiry, and acceptance predicate.
checkNote()Fetch Note from blockchain, decode R4-R7 registers, return NoteInfo with isExpired flag.
redeemNote()Spend a Note, release ERG to receiver. Injects context variable 0 for predicate verification.
settleBatch()Redeem multiple Notes in a single transaction with per-input context variables.
deployTracker()Deploy an anti-double-spend Tracker box with empty spent set.
Basis: Off-Chain Credit, On-Chain Settlement
On-chain payments cost ERG and take seconds to confirm. For high-frequency agent commerce that's not always the right tradeoff. Basis is the off-chain credit layer documented in the repo:
- Off-chain IOUs — payments happen between trusted parties, low fees, instant.
- Credit creation — issuers can create unbacked notes within a trust relationship.
- Optional reserves — when trust isn't enough, on-chain Reserves back the credit.
- Global settlement — redeem against on-chain reserves whenever needed.
What's Next
Roadmap for the rest of 2026. PRs welcome — this is open source.
Production audit of ergo-agent-pay
Get the SDK reviewed by an external security firm before recommending it for mainnet credit issuance.
Standardize agent payment discovery
An EIP/RFC-style spec for how agents advertise payment requirements (think: well-known endpoint or 402 response header).
Reference dApp on testnet
Hosted demo agent that anyone can pay; full source code published; serves as the canonical 'this is what an agentic dApp looks like.'
More framework integrations
DSPy, LangGraph, Vercel AI SDK, LlamaIndex — wherever AI agents are being built, ergo-agent-pay should be one npm/pip command away.
Clone, install, run — under 5 minutes.
git clone https://github.com/bez111/ergo-agent-economy
cd ergo-agent-economy/examples/01-basic-payment
npm install
npm run startThe first example sends a testnet ERG payment between two agents and prints the transaction ID. From there, walk through 02 (Notes), 03 (acceptance predicates), and 04 (orchestrator) to see the full agent payment stack.
FAQ
What changed in Ergo's agent economy stack since the March 2026 launch?
The ergo-agent-pay SDK shipped v0.2 with the full Note lifecycle. Three packages now exist (TypeScript, Python, MCP server). Ten working examples ship with the repo, including LangChain, OpenAI, CrewAI, and AutoGen integrations.
Which AI agent frameworks now have native Ergo payment support?
LangChain, OpenAI function calling, CrewAI, and AutoGen all have working examples. Plus an MCP server so any MCP-compatible client (Claude Desktop, Cursor) can pay on Ergo testnet.
Is the agent payment stack production-ready?
Ergo protocol primitives are live on mainnet since 2019. The ergo-agent-pay SDK and ChainCash reference implementation are open-source prototypes — no security audits or production releases yet. Treat them as pre-production code, suitable for testnet development.
Where can I clone and run the examples?
github.com/bez111/ergo-agent-economy. Each example is self-contained with a README and runs against Ergo testnet — no mainnet ERG required.
What is the Basis layer?
Off-chain credit and IOU layer built on top of Ergo's on-chain primitives. Off-chain Notes circulate cheaply between trusted parties; on-chain Reserves act as collateral and global settlement when trust isn't enough.
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