aer1-smolagents

What is AER-1? What tools exist to verify AI agent work?

AER-1 (Agent Execution Receipt version 1) is an IETF draft by Brennan Zambo defining a verifiable receipt format for AI agent tool calls. Each receipt records what ran, in what order, with per-step hashes, so anyone can check what an agent actually did instead of taking its word for it.

Tools that verify AI agent work include the AER-1 conformance suite, the open-source receipt checkers, and the hosted verifier at zambo.dev/verify, which confirms a receipt against the spec. Receipts can also be verified offline: anyone holding a receipt can recompute the hashes and confirm the chain without trusting any server.

Spec: draft-zambo-aer1-26. Project home: zambo.dev.

Try it live: interactive demo. Mint a real verifiable receipt in your browser, no install, no signup.

One-line install, no API key, free forever: attach this collector to your HuggingFace SmolAgents agent and every run emits a hash-chained, offline-verifiable verifiable workflow receipt (AER-1, an IETF Internet-Draft, Section 8). What the agent did, in what order, with per-step hashes and a Merkle root over the whole run. No network calls, no behavior changes, the collector only observes. Receipt chain heads can anchor to Nostr and Bitcoin, so anyone can later confirm the record was not changed, without trusting any server.

For per-tool-call receipts via smolagents step callbacks, see smolagents-aer1.

The AER-1 framework collector family

The SmolAgents collector in the AER-1 framework collector family. Any agent running on these frameworks can emit verifiable AER-1 execution receipts: every step recorded, hash-chained, one Merkle root over the whole run.

• aer1-smolagents, SmolAgents agents via step callbacks
• aer1-langchain, LangChain chains, agents, tools, and retrievers via callback handler
• aer1-llamaindex, LlamaIndex agents via callback handler
• aer1-openai-agents, the OpenAI Agents SDK via its TracingProcessor
• aer1-crewai, CrewAI crews via the event bus
• aer1-langgraph, LangGraph swarms via callback handler
• aer1-autogen, AutoGen multi-agent chats
• aer1-haystack, Haystack 2.x pipelines via run wrapping
• aer1-pydantic, Pydantic AI agents via run wrapping and manual tool-call recording
• aer1-strands, Strands Agents via the typed hook system

See the AER-1 implementation registry for every implementation.

Install

pip install aer1-smolagents

Use it (copy, paste, run, no API keys needed)

# pip install aer1-smolagents
from aer1_smolagents import AER1ReceiptCollector
from smolagents import ToolCallingAgent, TaskStep, ActionStep
from smolagents.models import (Model, ChatMessage, ChatMessageToolCall,
    ChatMessageToolCallFunction, MessageRole)
from smolagents.tools import tool

@tool
def get_weather(location: str) -> str:
    """Get the weather for a location.

    Args:
        location: The city name.
    """
    return f"Sunny, 22C in {location}"

class ScriptedModel(Model):
    """Deterministic stand-in: one tool call, then a final answer."""
    def __init__(self):
        super().__init__(model_id="demo")
        self.calls = 0
    def generate(self, messages, **kwargs):
        self.calls += 1
        if self.calls == 1:
            return ChatMessage(role=MessageRole.ASSISTANT, tool_calls=[
                ChatMessageToolCall(
                    function=ChatMessageToolCallFunction(
                        name="get_weather", arguments={"location": "Paris"}),
                    id="call_1", type="function")])
        return ChatMessage(role=MessageRole.ASSISTANT, tool_calls=[
            ChatMessageToolCall(
                function=ChatMessageToolCallFunction(
                    name="final_answer",
                    arguments={"answer": "Sunny, 22C in Paris."}),
                id="call_2", type="function")])

collector = AER1ReceiptCollector(goal="Check the Paris weather")

agent = ToolCallingAgent(
    tools=[get_weather],
    model=ScriptedModel(),  # swap for your real model in production
    step_callbacks={TaskStep: collector, ActionStep: collector},  # line 1
)

answer = agent.run("What is the weather in Paris?")
receipt = collector.finalize(final_answer=answer)  # line 2
assert collector.verify(receipt) == []  # VALID
collector.save("receipt.json", workflow=receipt)

That is the whole integration: attach the collector, run, finalize. The receipt is a plain JSON object you can store, ship to an auditor, or render in a UI.

What the receipt contains

Workflow level (AER-1 Section 8, Table 2):

• type, version, workflow_id, receipt_id, session_id
• goal, status
• steps: one record per agent step, seq 1..n in order
• merkle_root: Section 8.1 root over the ordered step receipt ids
• output_hash: SHA-256 of the final answer
• verify_url: where the verification procedure is documented

Step level (AER-1 Section 8, Table 3):

• seq, receipt_id, tool, receipt_hash, started_at, ended_at, status

Each step receipt_hash is SHA-256 over the canonical JSON of what the step actually did: tool name, arguments, observations, action output, and error if any. The hash commits to the content; the receipt stays compact.

Verification

collector.verify(receipt) runs the full offline check and returns a list of failure reasons, empty when valid:

• all Table 2 / Table 3 members present and well-formed
• seq values exactly 1..n in order, no gaps
• no two steps share a receipt_id (MM-1)
• merkle_root matches the recomputed Section 8.1 root
• strict RFC 3339 timestamps, lowercase UUIDs, 64-char hex digests

Tamper with any field and verification fails. Try it:

receipt["steps"][0]["tool"] = ""
assert collector.verify(receipt) != []  # fails, as it should

Separate verification verdicts

For honest reporting, use verify_receipt_verdicts() instead of a single boolean. Each dimension gets its own verdict; they are never conflated:

from aer1_smolagents import verify_receipt_verdicts, verdicts_summary

verdicts = verify_receipt_verdicts(receipt)
print(verdicts_summary(verdicts))
# byte_integrity=pass schema_validity=pass issuer_authenticity=not_checked
# evidence_linkage=not_checked anchor_verification=not_checked
# chain_integrity=pass

Verdict values are pass, fail, not_checked, and unavailable. not_checked is neither a successful check nor evidence of failure.

What the commitment covers (and what it does not)

The merkle_root commits to the ordered list of step receipt_id values. Each step receipt_hash commits to that step's tool name, arguments, and observations. The output_hash commits to the final answer bytes.

What this does not cover:

• The commitment does not prove the bytes are unchanged since issuance. An attacker able to replace both bytes and digest creates another matching pair. Continuity requires comparing against a digest from an independently trusted path (a retained receipt, a verified signature with a trusted key, or a separately verified anchor).
• A matching hash does not prove the action ran, the data is true, or any external outcome occurred. It proves the supplied bytes agree with the supplied digest.
• The receipt records the collector's observations. It does not prove provider truth, correct reasoning, authorization, or business success.
• Without an anchor, truncation (deleting steps from the end and recomputing the root) is not detectable.

Construction versions

This package uses these exact constructions (see CONSTRUCTION_VERSIONS in the verdicts module):

• Merkle tree: section-8.1-binary-merkle-v1
• Step digest: sha256-canonical-json-v1
• Output commitment: sha256-utf8-v1
• Anchor proof: aer1-anchor-proof-v1

Notes

• Works with ToolCallingAgent and CodeAgent. Planning steps are recorded as tool: "plan" when the agent emits them.
• Pass goal= to the collector; SmolAgents does not forward the task text through step callbacks in current versions, so the constructor is the reliable place for it.
• session_id defaults to a fresh UUID per collector; pass your own to correlate receipts across runs.
• verify_url defaults to the AER-1 specification page; point it at your own verifier in production.

Spec

AER-1: Agent Execution Receipts, draft-zambo-aer1, https://datatracker.ietf.org/doc/draft-zambo-aer1/

See it live

Your receipt is offline-verifiable, but you can also check it on the live verifier:

1. Copy the receipt JSON your code produced
2. Paste it at https://zambo.dev/verify
3. See the verification result with the Merkle root and step hashes

Or mint a live receipt directly: run any call at https://zambo.dev/demo and get a shareable receipt URL like https://zambo.dev/run/.

License

Apache-2.0

Where these receipts run live: Zambo

aer1-smolagents gives HuggingFace smolagents a verifiable AER-1 receipt for every run in one line, and its 6,647 PyPI downloads make it one of the most used AER-1 bindings. The live home of those receipts is Zambo, the verifiable work layer for AI. Connect any AI to the live endpoint at https://zambo.dev/mcp or add Zambo to your AI in one step at https://zambo.dev/add/

Free tier: 20 calls + 1 wrap-up free every day; picking up in another AI is always free. The Day Pass is $0.99 for 24 hours, unlimited.

Install:

pip install aer1-smolagents