Python SDK

The Python SDK instruments your agent from application code. Install it with pip install honeai and configure it once at startup.

initCopy Anchor Link

Configure the SDK with your API key. Call it once, early, before any other Tensile call.

import honeai

honeai.init("sk_your_key_here")

Pass endpoint to target a non-default base URL, such as a local stack:

honeai.init("sk_your_key_here", endpoint="http://localhost:8080")

Deployment stage (dev / staging / prod)Copy Anchor Link

Tag every event from a process with its deployment stage so the dashboard can separate real production traffic from dev and staging. The Stage filter on the reliability scorecard, cost views, and triage all read it, and the scorecard defaults to production so dev noise never skews your reliability numbers.

Set it once at init, or per-deploy from the HONE_ENV environment variable:

honeai.init("sk_your_key_here", environment="production")
# or, per-deploy, from the environment:
#   HONE_ENV=staging

The value is written to metadata["environment"] on every event and canonicalized case-insensitively at write time: prod / PROD / prd → production, stg → staging, local / development → dev. Any other value is kept as-is (lowercased), so a custom stage like qa is never folded into the wrong bucket. The explicit init argument wins over HONE_ENV.

begin and endCopy Anchor Link

begin() opens an interaction for a single agent turn and returns an object you close with end(). This is the most explicit way to record a turn, and it captures latency automatically from the time between the two calls.

interaction = honeai.begin(
    user_id="user_8f21",
    agent_name="support-bot",
    input="How do I reset my password?",
)

reply = run_agent("How do I reset my password?")

interaction.end(reply, success=True)

begin parameters

end parameters

Mark a failed turn so it surfaces in error analytics:

try:
    reply = run_agent(user_input)
    interaction.end(reply, success=True)
except Exception as err:
    interaction.end(str(err), success=False)

trackCopy Anchor Link

track() records a completed turn in a single call. Reach for it when you already have both the input and the output in hand and do not need to hold an interaction open.

honeai.track(
    user_id="user_8f21",
    input="How do I reset my password?",
    output="Head to Settings, then Security, and choose Reset password.",
    agent_name="support-bot",
    conversation_id="conv_5c19",
)

Parameters

Multi-tenant (customer_id)Copy Anchor Link

When one agent serves several of your own customers, pass customer_id so each session and event is scoped to the downstream tenant it belongs to. The end-user's identity becomes the pair (customer_id, user_id), and the dashboard can filter and roll up per customer.

# On an explicit turn:
turn = honeai.begin(
    user_id="user-123",
    agent_name="support-bot",
    input="How do I reset my password?",
    customer_id="acme-corp",
)
turn.end("Head to Settings → Security.")

# Or in one shot:
honeai.track(
    user_id="user-123",
    input="How do I reset my password?",
    output="Head to Settings → Security.",
    agent_name="support-bot",
    customer_id="acme-corp",
)

Conversation groupingCopy Anchor Link

A conversation is a series of turns between one user and your agent. Pass the same conversation_id across track() calls to thread them together, so the dashboard shows them as one exchange under User Stories rather than as isolated turns.

conversation_id = "conv_5c19"

honeai.track(
    user_id="user_8f21",
    input="How do I reset my password?",
    output="Head to Settings, then Security, and choose Reset password.",
    conversation_id=conversation_id,
)

honeai.track(
    user_id="user_8f21",
    input="I don't see a Security tab.",
    output="It's under your profile menu on the top right.",
    conversation_id=conversation_id,
)

Generate a fresh id when a new conversation starts, and reuse it for every follow-up turn in that exchange.

identifyCopy Anchor Link

identify() attaches traits to a user id. Traits enrich the user profile in the dashboard and become dimensions you can filter and segment on. Keep the id pseudonymous and avoid putting raw personal data in the traits.

honeai.identify("user_8f21", {
    "plan": "enterprise",
    "signup_cohort": "2026-Q2",
    "region": "eu-west",
})

set_property and set_propertiesCopy Anchor Link

Within an open interaction, attach custom properties such as the model used, token counts, or cost. These ride along on the event and power metadata-driven charts and filters.

interaction = honeai.begin(
    user_id="user_8f21",
    agent_name="support-bot",
    input="Summarize this thread.",
)

interaction.set_property("model", "claude-sonnet-4")

interaction.set_properties({
    "input_tokens": 1840,
    "output_tokens": 220,
    "cost_usd": 0.0142,
})

interaction.end(reply, success=True)

Use set_property for a single key and set_properties to attach several at once. Define the corresponding keys under Settings → Metadata Keys so they become selectable dimensions in the dashboard.

Full exampleCopy Anchor Link

import os
import honeai

honeai.init(os.environ["HONE_API_KEY"])

honeai.identify("user_8f21", {"plan": "enterprise", "region": "eu-west"})

interaction = honeai.begin(
    user_id="user_8f21",
    agent_name="support-bot",
    input="How do I export my data?",
)

interaction.set_property("model", "claude-sonnet-4")

reply = "Open Settings, choose Export, and pick a format. We'll email a link."
interaction.set_properties({"output_tokens": 96, "cost_usd": 0.0031})

interaction.end(reply, success=True)