The trust layer for AI agents. Runtime compliance certificates across EU AI Act, Singapore MGF, and more.
One-line SDK integration captures every agent action as a tamper-proof, Ed25519-signed audit trail and maps it to specific regulatory requirements. Learn more about agent attestation and how it differs from periodic certification.
pip install agentapproved
from agentapproved import AgentApprovedHandler, assess_compliance, generate_evidence_packet
from agentapproved.hasher import get_public_key_hex
# 1. Attach to your LangChain agent
handler = AgentApprovedHandler(agent_id="my-agent", data_dir="./evidence")
agent = create_agent(..., callbacks=[handler])
agent.invoke({"input": "What is the return policy?"})
# 2. Record human oversight (EU AI Act Art 12(2)(d))
handler.record_oversight(reviewer_id="jane", decision="approved", reason="Accurate")
handler.end_session()
# 3. Check compliance score
report = assess_compliance(handler.get_events())
print(f"EU AI Act Article 12 compliance: {report.overall_score}%")
for gap in report.gaps:
print(f" {gap.article}: {gap.remediation}")
# 4. Export evidence packet for auditors
generate_evidence_packet(
handler.get_events(),
"./audit-packet",
organisation="Acme Ltd",
public_key_hex=get_public_key_hex(handler._public_key),
)Send events to the AgentApproved server for centralised compliance monitoring, dashboard, and evidence export.
from agentapproved import AgentApprovedHandler
# Connect to your AgentApproved server
handler = AgentApprovedHandler(
agent_id="my-agent",
api_key="ap_your_key_here",
endpoint="https://your-server.example.com",
)
# Use with any LangChain agent — events are batched and sent automatically
agent = create_agent(..., callbacks=[handler])
agent.invoke({"input": "What is the return policy?"})
# Record human oversight
handler.record_oversight(reviewer_id="jane", decision="approved")
handler.end_session()
# Flush remaining events and stop the background sender
handler.shutdown()Events are buffered and sent in batches (every 50 events or 5 seconds). The background thread never blocks your agent. Failed sends are retried with exponential backoff.
You can also use both modes together — events are sent to the server AND saved locally:
handler = AgentApprovedHandler(
agent_id="my-agent",
api_key="ap_your_key_here",
endpoint="https://your-server.example.com",
data_dir="./local-backup",
)EU AI Act Article 12 compliance: 100%
✓ Article 12(1) — Automatic logging capability
✓ Article 12(2)(a) — Period of each use
✓ Article 12(2)(b) — Reference database
✓ Article 12(2)(c) — Input data leading to match
✓ Article 12(2)(d) — Human oversight verification
✓ Article 12(3) — Post-market monitoring traceability
The evidence packet contains:
- report.html — auditor-readable compliance report with article-by-article assessment
- evidence.json — machine-readable event data with compliance mapping
- integrity.json — hash chain and Ed25519 signature verification proof
Every event is SHA-256 hash-chained and Ed25519 signed. Tampering with any event breaks the chain. An auditor can independently verify the entire audit trail.
- One-line integration —
callbacks=[handler]on any LangChain agent - All LangChain events — LLM calls, tool use, RAG retrieval, agent decisions, chat models
- EU AI Act Article 12 mapping — automatic compliance scoring with remediation guidance
- Tamper-proof — SHA-256 hash chain + Ed25519 signatures on every event
- Human oversight capture —
record_oversight()satisfies Article 12(2)(d) - Self-contained evidence packets — HTML report + JSON data + integrity proof
- Local-first — events persist to local JSON files, no cloud required
- Server mode — send events to AgentApproved server for centralised monitoring
- Background batching — HTTP transport batches events, never blocks your agent
- Never crashes your agent — all errors swallowed, logging only
- Python 3.10+
- LangChain (
langchain-core >= 0.3.0)
- EU AI Act Article 12 — 6 logging requirements, automated compliance scoring
- Singapore MGF — 8 requirements across 4 governance dimensions
- Integrity Oath — voluntary ethical commitment, 6 principles
- Full composite — multi-framework attestation with Bronze/Silver/Gold certification
The SDK provides dedicated helper methods for each compliance scope. Use these to emit the exact evidence events that each framework requires.
LangChain callbacks automatically capture LLM calls, tool use, and retrieval events. Add record_oversight() for human review evidence:
handler = AgentApprovedHandler(agent_id="my-agent")
agent = create_agent(..., callbacks=[handler])
agent.invoke({"input": "Summarise this contract"})
# Human reviews the output — satisfies Art 12(2)(d)
handler.record_oversight(reviewer_id="jane", decision="approved", reason="Accurate summary")
handler.end_session()11 helpers covering all 4 governance dimensions (Accountability, Human Oversight, Monitoring, Transparency):
handler = AgentApprovedHandler(agent_id="invoice-bot")
# Dimension 1 — Accountability
handler.record_config(config_name="model", details="claude-3.5-sonnet, temperature=0")
handler.record_permission(permission="read_invoices", scope="finance", details="Read-only")
handler.record_documentation(doc_type="capability_statement", content="Extracts invoice line items", author_id="eng-lead")
# Dimension 2 — Human Oversight
handler.record_approval(reviewer_id="tech-lead", decision="approved", reason="Tests pass")
handler.record_review(reviewer_id="qa-lead", subject="output_quality", outcome="pass")
# Dimension 3 — Monitoring
handler.record_test(test_name="accuracy_benchmark", result="pass", details="97.3%")
handler.record_evaluation(eval_name="hallucination_rate", score=0.02)
handler.record_monitor(metric_name="latency_p99", value="230ms")
handler.record_health_check(service="llm_api", status="healthy")
handler.record_heartbeat()
# Dimension 4 — Transparency
handler.record_disclosure(disclosure_type="ai_identity", content="This system uses AI to process invoices")
handler.end_session()3 helpers for honest error reporting and human escalation:
handler = AgentApprovedHandler(agent_id="support-bot")
# P1/P6 — Report errors honestly
handler.record_error(error_type="parse_failure", message="Could not extract table from PDF")
handler.record_tool_error(tool_name="pdf_parser", error="No table found", details="Tried camelot + tabula")
# P2 — Escalate when uncertain
handler.record_escalation(reviewer_id="human-ops", reason="Confidence below threshold", severity="high")
handler.end_session()Combine helpers from any scope in a single session for composite attestation:
handler = AgentApprovedHandler(agent_id="finance-agent")
agent = create_agent(..., callbacks=[handler])
# Pre-deployment evidence (SGP MGF)
handler.record_config(config_name="model", details="gpt-4o")
handler.record_permission(permission="read", scope="transactions")
handler.record_test(test_name="accuracy", result="pass", details="98.1%")
handler.record_approval(reviewer_id="cto", decision="approved")
handler.record_disclosure(disclosure_type="ai_identity", content="AI-powered financial analysis")
# Run the agent (EU AI Act — automatic LLM/tool/retrieval capture)
agent.invoke({"input": "Analyse Q4 expenses"})
# Runtime monitoring (SGP MGF)
handler.record_heartbeat()
handler.record_monitor(metric_name="requests_processed", value="50")
# Error handling (Integrity Oath)
handler.record_error(error_type="api_timeout", message="Retry succeeded on attempt 2")
# Human review (EU AI Act + SGP MGF)
handler.record_oversight(reviewer_id="finance-director", decision="approved", reason="Analysis verified")
handler.end_session()- agentapproved.ai — homepage, getting started, pricing
- EU AI Act compliance guide
- Singapore MGF compliance guide
- The Integrity Oath
- PyPI package
MIT — see LICENSE