Python SDK

python -c "import agenteye; print(agenteye.__version__)"

import agenteye

agenteye.configure(environment="production")

agenteye.event.agent_start(session_id="run-001", agent_id="planner", goal="answer user query")

agenteye.event.tool_use(
    session_id="run-002",
    agent_id="planner",
    tool_name="web_search",
    tool_call_id="toolu_01",
    input={"query": "latest AI research"},
)

agenteye.event.tool_result(
    session_id="run-001",
    agent_id="planner",
    tool_name="web_search",
    tool_call_id="toolu_01",
    output={"results": ["..."]},
)

agenteye.event.agent_end(session_id="run-001", agent_id="planner", outcome="success")

import anthropic
import agenteye

agenteye.configure(environment="production")
client = anthropic.Anthropic()

messages = [{"role": "user", "content": "Summarise today's incidents."}]

agenteye.event.model_request(
    session_id="run-001",
    agent_id="planner",
    model="claude-sonnet-4-6",
    messages=messages,
)

reply = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=512,
    messages=messages,
)

agenteye.event.model_response(
    session_id="run-001",
    agent_id="planner",
    model=reply.model,
    stop_reason=reply.stop_reason,
    input_tokens=reply.usage.input_tokens,
    output_tokens=reply.usage.output_tokens,
    content=[block.model_dump() for block in reply.content],
)

agenteye.configure(
    base_dir=None,           # Path | str | None. Default: $AGENTEYE_HOME or ~/.agenteye
    flush_interval=0.5,      # float, seconds between flush cycles
    environment=None,        # str | None. Deployment environment label
)

agenteye.configure(environment="production")

export AGENTEYE_ENVIRONMENT=production

agenteye.event.agent_start(
    session_id="run-001",
    agent_id="planner",
    goal="answer user query",   # str | None
    parent_id=None,             # str | None - nested agents के लिए parent agent_id
)

agenteye.event.agent_end(
    session_id="run-001",
    agent_id="planner",
    outcome="success",          # str | None
    summary="Answered query",   # str | None
)

agenteye.event.tool_use(
    session_id="run-001",
    agent_id="planner",
    tool_name="web_search",     # str, required
    tool_call_id="toolu_01",    # str, required - matching tool_result के लिए correlation key
    input={"query": "..."},     # dict | None
)

agenteye.event.tool_result(
    session_id="run-001",
    agent_id="planner",
    tool_name="web_search",
    tool_call_id="toolu_01",        # prior tool_use से मेल खाना चाहिए
    output={"results": ["..."]},    # Any | None
    error=None,                     # str | None - यदि टूल ने raise किया तो सेट करें
    # duration_ms स्वचालित रूप से गणना की जाती है - इसे पास न करें
)

agenteye.event.model_request(
    session_id="run-001",
    agent_id="planner",
    model="claude-sonnet-4-6", # str | None - कोई भी प्रोवाइडर/मॉडल स्ट्रिंग; सत्यापित नहीं
    messages=[                   # list[dict] | None - conversation turns
        {"role": "user", "content": "..."},
    ],
    system="You are helpful.",   # Any | None - str या content blocks की list
    tools=[                      # list[dict] | None - मॉडल को दिए गए tool schemas
        {"name": "search", "input_schema": {"type": "object"}},
    ],
)

agenteye.event.model_response(
    session_id="run-001",
    agent_id="planner",
    model="claude-sonnet-4-6", # str | None - कोई भी प्रोवाइडर/मॉडल स्ट्रिंग; सत्यापित नहीं
    stop_reason="end_turn",     # str | None
    input_tokens=1024,          # int | None
    output_tokens=256,          # int | None
    content=[                    # Any | None - str, या content blocks की list
        {"type": "text", "text": "..."},
    ],
    role="assistant",            # str | None
)

agenteye.event.hook_triggered(
    session_id="run-001",
    agent_id="planner",
    hook_name="pre_tool_use",   # str, required
    hook_id="hook-abc",         # str, required - correlation key
    trigger_event="tool_use",   # str | None
    input={"tool": "search"},   # Any | None
)

agenteye.event.hook_completed(
    session_id="run-001",
    agent_id="planner",
    hook_name="pre_tool_use",
    hook_id="hook-abc",         # prior hook_triggered से मेल खाना चाहिए
    outcome="allow",            # str | None
    output=None,                # Any | None
    error=None,                 # str | None
    # duration_ms स्वचालित रूप से गणना की जाती है - इसे पास न करें
)

agenteye.event.error(
    session_id="run-001",
    agent_id="planner",
    error_type="TimeoutError",  # str, required
    message="timed out",        # str, required
    traceback="Traceback...",   # str | None
)

agenteye.event.human_wait(
    session_id="run-001",
    agent_id="planner",
    input_id="inp-abc",                          # str, required - matching human_input के लिए correlation key
    prompt="Do you approve this action?",        # str | None - मानव को दिखाया गया सवाल
    options=["approve", "reject", "defer"],      # list[str] | None - मानव को दिए गए विकल्प
    reason="approval_required",                  # str | None - एजेंट क्यों प्रतीक्षा कर रहा है
)

agenteye.event.human_input(
    session_id="run-001",
    agent_id="planner",
    input_id="inp-abc",    # str, required - prior human_wait से मेल खाना चाहिए
    response="approve",    # str | None - मानव का उत्तर (मुक्त पाठ या चयनित विकल्प)
    # duration_ms स्वचालित रूप से गणना की जाती है - इसे पास न करें
)

agenteye.event.human_pause(
    session_id="run-001",
    agent_id="planner",
    reason="user_requested",  # str | None
    user_id="usr_42",         # str | None - किसने एजेंट को रोका
)

agenteye.event.human_interrupt(
    session_id="run-001",
    agent_id="planner",
    reason="output_incorrect",       # str | None
    user_id="usr_42",                # str | None - किसने एजेंट को बाधित किया
    at_step="tool_use:web_search",   # str | None - एजेंट क्या कर रहा था जब रोका गया
)

agenteye.event.tool_use(
    session_id="run-001",
    agent_id="planner",
    tool_name="db_query",
    tool_call_id="toolu_02",
    tenant_id="acme",           # custom field
    region="us-east-1",         # custom field
)

~/.agenteye/events/event-2026-04-01T12-00-00-000Z.jsonl