0.6.0

AEP Python SDK

Python client library for the Agent Event Protocol — an observability framework for agent workflows.

Version: 0.6.0 · Python: ≥ 3.10 · Schema: AEP v0.2.0

📍 Project direction (2026-06): AEP is converging on OpenTelemetry rather than continuing as a standalone protocol. This SDK remains published and usable, and is the carry-forward / reference SDK — its framework auto-instrumentation, OTEL bridge, and signing feed contributions to the OTel GenAI semantic conventions.

---

Installation

From PyPI:

pip install agent-event-protocol

The PyPI distribution name is agent-event-protocol (the bare aep name was already taken on PyPI), but the import name is unchanged — you still write import aep.

From the repo root (development):

pip install -e "sdks/python[dev]"

---

Quick start

from aep import create_event, AEPClient

# Build a spec-compliant event
event = create_event(
    source="agent://my-agent",
    type="task.created",
    session_id="ses_001",
    trace_id="trc_001",
    payload={"task": "summarise document"},
)

# Emit to the AEP ingest server
with AEPClient(server_url="http://localhost:8787", api_key="aep_...") as client:
    result = client.emit(event)
    # {"accepted": True, "duplicate": False, "id": "evt_..."}

Environment variables are read automatically:

---

Async client

import asyncio
from aep import create_event, AsyncAEPClient

async def main():
    event = create_event(
        source="agent://my-agent",
        type="task.completed",
        session_id="ses_001",
        trace_id="trc_001",
        payload={"result": "done"},
    )
    async with AsyncAEPClient() as client:
        result = await client.emit(event)

asyncio.run(main())

---

Event types

from aep import CORE_EVENT_TYPES, EventType

# String constants
print(CORE_EVENT_TYPES)
# ['task.created', 'task.updated', ..., 'error.raised']

# Enum access
EventType.TOOL_CALLED.value  # 'tool.called'

The 12 core types: task.created, task.updated, task.completed, task.failed, tool.called, tool.result, memory.read, memory.write, handoff.started, handoff.completed, policy.blocked, error.raised.

---

Multi-agent / sub-agent workflows

from aep import create_event, AEPClient

trace_id = "trc_workflow_001"
orch_session = "ses_orchestrator_001"
sub_session = "ses_subagent_001"

orch_event = create_event(
    source="agent://orchestrator",
    type="task.created",
    session_id=orch_session,
    trace_id=trace_id,
    payload={"goal": "research AI observability"},
    agent_role="orchestrator",
)

sub_event = create_event(
    source="agent://subagent",
    type="task.created",
    session_id=sub_session,
    trace_id=trace_id,       # same trace_id ties sessions together
    parent_session_id=orch_session,  # links to parent
    payload={"subtask": "web search"},
    agent_role="subagent",
)

with AEPClient() as client:
    client.emit_batch([orch_event, sub_event])
    tree = client.get_session_tree(orch_session)
    workflow = client.get_workflow(trace_id)

---

Auto-instrumentation (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK & Claude Agent SDK)

Emit the full multi-agent DAG from a LangGraph, CrewAI, AutoGen AgentChat, OpenAI Agents SDK, or Anthropic Claude Agent SDK workflow with no changes to your code — one aep.instrument() call wires AEP events to the run, every sub-agent, each tool call, and the handoffs between them. Only the frameworks you actually use need be installed; instrumenting CrewAI, AutoGen, the OpenAI Agents SDK, or the Claude Agent SDK does not require LangChain.

pip install -e "sdks/python[langgraph]"   # adds langgraph + langchain-core

import aep
aep.instrument()          # reads AEP_INGEST_URL / AEP_API_KEY (or pass them in)

# ... build and run your StateGraph exactly as usual ...
app = graph.compile()
app.invoke({"topic": "AI agent observability"})

aep.flush()               # block until buffered telemetry is sent (see below)
# aep.uninstrument()      # optional: restore original behavior (also flushes)

What gets emitted, with causation preserved (trace_id, session_id, parent_session_id, causation_id):

Notes:

• Tested against langgraph>=0.1. Internals vary across versions; if the expected hook isn't found, instrument() logs a warning and is a no-op — it never crashes your app, and it won't falsely report success.
• Configuration: aep.instrument(server_url=..., api_key=...), or AEP_INGEST_URL / AEP_API_KEY env vars.
• Implemented as a LangChain BaseCallbackHandler injected via RunnableConfig (the supported extension point), so it survives parallel node fan-out.
• Emission is non-blocking — events are sent on a background worker so they never add network latency to your graph. Call aep.flush() before a short-lived process exits (or rely on the atexit flush / uninstrument()) to be sure they were delivered. The buffer is bounded and drops with a warning under sustained overload rather than blocking your workflow.
• See demos/langgraph_multiagent.py for a runnable 10-node example.

CrewAI

pip install -e "sdks/python[crewai]"   # adds crewai (no LangChain needed)

import aep
aep.instrument()          # or aep.instrument(frameworks=["crewai"])

# ... build and kick off your Crew exactly as usual ...
crew.kickoff()

aep.flush()

Notes:

• Tested against crewai>=1.0. Implemented by subscribing to CrewAI's own event bus (crewai.events), the supported extension point — not by wrapping Crew/Agent internals. If the event API has drifted, instrument() warns and is a no-op (never crashes your app).
• CrewAI runs each task through its assigned agent, so a task is the sub-agent session (named for that agent's role); an agent that runs outside any task (e.g. a hierarchical manager) gets its own sub-agent session.
• Tool-call attribution is exact for sequential crews; with concurrent agents running tools at once, pairing a tool.result to its tool.called is best-effort (the events don't always carry a per-call id).
• Guardrail failures map to policy.blocked (v0.6.0): a guardrail validation that fails (LLMGuardrailCompletedEvent with success=False) emits policy.blocked on the guarded task's session with the guardrail's name and error text plus retry_count (CrewAI retries failed validations — each failed attempt is one event). Passed validations emit nothing. On a CrewAI without guardrail events, everything else still instruments.
• See demos/crewai_multiagent.py for a runnable 3-agent example that works offline with no LLM API key.

AutoGen AgentChat

pip install -e "sdks/python[autogen]"   # adds autogen-agentchat + autogen-ext (no LangChain needed)

import aep
aep.instrument()          # or aep.instrument(frameworks=["autogen"])

# ... build and run your team exactly as usual ...
await team.run(task="research and write a report")   # or team.run_stream(...)

aep.flush()

Notes:

• Tested against autogen-agentchat>=0.4 (developed on 0.7.x). AutoGen AgentChat has no callback registry or event bus, so the tracer taps the async event stream BaseGroupChat.run_stream yields (which team.run() consumes internally — so both entry points are covered). If the team base class has drifted, instrument() warns and is a no-op (never crashes your app).
• Teams are the instrumented surface. A team is the orchestrator; each agent that speaks becomes a sub-agent session. In-team agents run through the AgentChat runtime, so they're captured once with no double-counting. A standalone single AssistantAgent run with no team is not instrumented — wrap it in a team.
• Tool pairing is exact, even for parallel tool calls returned out of order: AutoGen tags each result with the call_id of its request, so no LIFO guessing is needed (unlike CrewAI).
• Agent boundaries are inferred from message source (AutoGen emits no per-agent start/stop event), so a run-level failure marks only the orchestrator task.failed; observed sub-agents close task.completed.
• See demos/autogen_multiagent.py for a runnable 2-agent team example that works offline with no LLM API key (via autogen-ext's ReplayChatCompletionClient).

OpenAI Agents SDK

pip install -e "sdks/python[openai-agents]"   # adds openai-agents (no LangChain needed)

import aep
aep.instrument()          # or aep.instrument(frameworks=["openai-agents"])

# ... build and run your agents exactly as usual ...
from agents import Runner
await Runner.run(triage_agent, "help me in Spanish")   # or Runner.run_sync(...)

aep.flush()

Notes:

• Tested against openai-agents>=0.1 (developed on 0.17.x). Implemented by registering a tracing processor via agents.tracing.add_trace_processor — the SDK's supported, global, zero-code observation surface — alongside (not replacing) the SDK's own exporter. If the tracing API has drifted, instrument() warns and is a no-op (never crashes your app).
• The run's trace is the orchestrator, and every agent is a sub-agent of it — matching how the SDK itself trees agents as siblings under the workflow. The real from_agent of a handoff is recorded on the handed-to agent's task.created payload as handoff_from, so the actual flow is preserved even though the parent edge is the workflow root.
• Tool pairing is exact: a tool is a single function span carrying both its start and end, so tool.called → tool.result pair by span_id — no LIFO guessing. A tool nests on its owning agent's session (resolved by walking the span tree to the nearest enclosing agent).
• Agents-as-tools (agent.as_tool(...)) produce both a tool.called / tool.result pair (for the as_tool function span) and a nested sub-agent task.* for the inner agent (parented to the calling agent) — a faithful double-representation of "the outer agent called a tool that was itself an agent", not a duplicate. The DAG stays a single trace with no dangling links.
• Caveat — uncaught run errors aren't marked failed. The tracing surface only reports failures the SDK records on a span (e.g. a tool error). An uncaught exception from Runner.run is not delivered to processors — the spans and trace still close cleanly and the exception propagates to your caller — so such a run is recorded completed here. The exception itself remains your source of truth; AEP deliberately doesn't add a separate failure path that would race the SDK's own span/trace close.
• Guardrail tripwires map to policy.blocked (v0.6.0): a guardrail span that ends with its tripwire triggered emits policy.blocked on the owning agent's session (policy = the guardrail's name, action_blocked = agent/<agent name> or workflow/<workflow name> — the gated run's identity), chained to that run via causation_id. Untriggered guardrails emit nothing (blocked-only semantics).
• See demos/openai_agents_multiagent.py for a runnable handoff + tool example that works offline with no LLM API key (via a scripted Model).

Anthropic Claude Agent SDK

pip install -e "sdks/python[claude-agent]"   # adds claude-agent-sdk (no LangChain needed)

import aep
aep.instrument()          # or aep.instrument(frameworks=["claude-agent"])

# ... run your agent exactly as usual ...
from claude_agent_sdk import query
async for message in query(prompt="review the repo"):   # or ClaudeSDKClient(...)
    ...

aep.flush()

Notes:

• Permission denials map to policy.blocked (v0.6.0): when your app supplies a can_use_tool handler, AEP wraps it observationally — the handler's result passes through unchanged, and a deny additionally emits policy.blocked (policy: "can_use_tool", the deny message as reason, action_blocked: "tool.called/<tool_name>"). Attribution: the denial's context carries no session identifier, so the event lands on the denied tool's run (when PreToolUse fired first), else the single open session's root; with several sessions open and no correlation it is dropped rather than mislabeled. Runs without a can_use_tool handler have no in-process denial surface — interactive prompts and permission-rule denials happen inside the CLI and are not observable here.
• Tested against claude-agent-sdk>=0.2 (developed on 0.2.x). Implemented by injecting observer hooks into ClaudeAgentOptions.hooks — the SDK's supported observation surface — at the two methods both entry points consume (InternalClient.process_query for query(), ClaudeSDKClient.connect for the streaming client). If the hooks API has drifted, instrument() warns and is a no-op (never crashes your app). Call aep.instrument() once at startup.
• The top-level agent is the orchestrator (one per session_id); each Task sub-agent is a sub-agent of it. Every tool/sub-agent hook carries an agent_id and tool_use_id, so attribution and pairing are exact — a tool nests on its owning agent's session (the sub-agent named by agent_id if one is open, else the root), and tool.called/tool.result pair by tool_use_id. No inference, no LIFO guessing.
• The injected hooks are pure observers — each returns {} (proceed, no decision) and swallows its own errors, so AEP can never alter or break your agent run. They coexist with any hooks you configure yourself.
• Caveat — the top-level run is closed by the Stop hook (fired at the end of each turn). A multi-turn ClaudeSDKClient session therefore records one trace per turn (the root reopens on the next turn's first hook). Sub-agents still open/close within their turn.
• See demos/claude_agent_multiagent.py for a runnable orchestrator + sub-agent + tools example that works offline with no API key and no claude binary (it replays scripted hooks through a real query() via a control-protocol fake transport).

---

Client API

AEPClient (sync) / AsyncAEPClient (async)

Retries (v0.5.0+)

Both clients automatically retry transient failures — connection/timeout errors, HTTP 429 and HTTP 5xx (honouring Retry-After on any retryable status, per RFC 7231) — with full-jitter exponential backoff. Other 4xx responses, and deterministic local errors (e.g. an unsupported URL scheme), are never retried. Retrying POST /events is safe because events carry a client-generated id and the server deduplicates on it (a replay returns "duplicate": true).

AEPClient(
    max_retries=3,            # retries after the initial attempt; 0 disables
    retry_backoff_base=0.5,   # seconds; delay ceiling is base * 2**attempt
    retry_backoff_max=30.0,   # hard cap per delay, also caps Retry-After
)

Since v0.5.0 every transport-level failure (including timeouts, previously a raw httpx.TimeoutException) is raised as AEPConnectionError after retries are exhausted.

---

HMAC signing

from aep import create_event, sign_event

event = create_event(source="agent://test", type="task.created",
                     session_id="ses_1", trace_id="trc_1", payload={})
sign_event(event, secret="my-hmac-secret")  # v2 (deep) by default
# event["signature"] == {"alg": "hmac-sha256", "value": "<base64>", "canon": "v2"}

# Or let the client sign automatically:
from aep import AEPClient
with AEPClient(hmac_secret="my-hmac-secret") as client:
    client.emit(event)  # signs (v2) before sending

Canonicalization versions (canon) — issue #59

The default canonical form is now v2 (deep): it recursively key-sorts the whole event including nested payloads, so the signature covers payload contents and carries a signature.canon: "v2" marker — payload tamper-evidence is on without opt-in. canonicalize_v2(event) exposes the deep form directly. It is byte-identical to the server and the Node and Go SDKs for JSON values shared across runtimes (locked by a server-derived known-answer test).

v1 is now legacy but still supported — pass canon="v1" for the envelope-only form, which sorts only top-level keys and drops nested object contents (covers the envelope but not nested payloads):

sign_event(event, secret="my-hmac-secret", canon="v1")  # legacy envelope-only

verify_signature is version-aware (honours canon; an absent marker accepts either form).

Compatibility: the v2 default requires a v2-aware server (one that includes server PR #60+). The current AEP server requires v2 and rejects legacy v1 with 401 — the v1 retirement is complete (issue #65, the successor to the #59 unification). canon="v1" is retained only for talking to an older self-hosted server that predates signature.canon support; a current server rejects it.

---

Offline audit-bundle verification

Verify a tamper-evident audit bundle (from GET /sessions/:id/audit-bundle, GET /workflows/:traceId/audit-bundle, or aep audit export) entirely offline — no server, no database — with just the bundle JSON and the audit signing secret:

import json
from aep import verify_audit_bundle

with open("bundle.json", encoding="utf-8") as fh:
    bundle = json.load(fh)

result = verify_audit_bundle(bundle, secret="my-audit-signing-secret")
# {"valid": True, "content_digest_match": True, "manifest_signature_valid": True,
#  "errors": [], "per_event": [...]}

if not result["valid"]:
    raise SystemExit(f"bundle failed verification: {result['errors']}")

It recomputes the content digest over the bundle's events and the HMAC signature over its manifest — both byte-identical to the server (and the Go/Node SDKs; locked by a shared known-answer fixture). Any post-hoc change — a mutated payload field, reordered/added/dropped events, an edited manifest, or the wrong secret — makes valid false. (Building/signing bundles stays server-side, where the signing secret lives.)

---

Validation

from aep import create_event, validate_event

event = create_event(...)
result = validate_event(event)
# {"valid": True, "errors": []}

# Errors include warnings prefixed with [warn] which don't affect validity

---

Exceptions

---

Demo

A complete multi-agent research demo is in demos/subagent_research.py. It mirrors the JS demo and exercises the session tree and workflow APIs.

# Start the server first
npm run ingest   # from repo root

# Run the demo
cd sdks/python
python demos/subagent_research.py

---

Tests

cd sdks/python
pip install -e ".[dev]"

# Unit tests (no server needed)
pytest tests/unit/

# Integration tests (requires running server)
AEP_INGEST_URL=http://localhost:8787 pytest tests/integration/

---

Publishing / Releases

The SDK is published to PyPI as agent-event-protocol (import name stays aep) by the Release Python SDK workflow, which is triggered only by pushing a python-sdk-v* tag — never on a branch push or PR. Publishing uses PyPI Trusted Publishing (OIDC), so there is no API token to store or leak.

Like tags in general, python-sdk-v* tags are not branch-protected, so the release is gated twice and independently:

1. verify — fails fast unless the tagged commit is an ancestor of origin/main (i.e. it landed via the PR-protected merge path), then builds the sdist + wheel and runs pytest. The approver sees a green pre-flight.
2. publish — runs in the pypi-publish GitHub Environment, which has required reviewers. The upload to PyPI only happens after a human approves the deployment, and it ships the exact artifacts verify built and tested.

Maintainer setup (one-time)

Before the first release, configure the publisher side (cannot be done from code):

• PyPI Trusted Publisher — on PyPI, add a GitHub Actions trusted publisher (use the pending publisher flow for the first-ever release, since the project doesn't exist on PyPI yet) with exactly:
  • Project name: agent-event-protocol
  • Owner: surpradhan · Repository: agent-event-protocol
  • Workflow: release-python-sdk.yml · Environment: pypi-publish
• GitHub Environment — create an Environment named pypi-publish under Settings → Environments and add the release owners as Required reviewers. No secrets are stored (OIDC replaces the API token).

Cutting a release

# 1. Bump sdks/python/pyproject.toml `version` AND aep/__init__.py `__version__`
#    (keep them in sync) on a PR; squash-merge to main.
# 2. From main, tag the release commit and push the tag:
git tag python-sdk-v0.6.0
git push origin python-sdk-v0.6.0
# 3. Approve the `pypi-publish` deployment in the Actions UI once `verify` is green.