dapplepot-sdk 0.1.0

DapplePot Python SDK for AI agent security observability — instruments LangChain/LangGraph, OpenAI, and Anthropic

DapplePot Python SDK

Python SDK for instrumenting LLM agents with DapplePot security observability. Drop-in integrations for LangChain/LangGraph, OpenAI, and Anthropic — sends structured events to the DapplePot ingest API and runs real-time threat detection in the hot path.

Installation

Ensure you have Python 3.10+ installed, then:

pip install dapplepot-sdk

# Framework-specific extras:
pip install "dapplepot-sdk[langchain]"    # LangChain / LangGraph
pip install "dapplepot-sdk[openai]"       # OpenAI
pip install "dapplepot-sdk[anthropic]"    # Anthropic
pip install "dapplepot-sdk[all]"          # Everything

Quick Start

Get your sdk_key, tenant_id, agent_id, and ingest_url from the DapplePot dashboard.

Anthropic

The standard anthropic package is patched in-place — upgrade it freely without coordinating with DapplePot releases.

import anthropic
from dapplepot_sdk import DapplePot

dp = DapplePot(
    sdk_key    = "dp_sk_...",
    tenant_id  = "your-tenant-id",
    agent_id   = "your-agent-id",
    ingest_url = "https://ingest.dapplepot.com",
)
dp.instrument_anthropic()

client = anthropic.Anthropic(api_key="...")

with dp.session(user_context_id="user_123", user_tenant_id="acme_corp"):
    response = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello!"}],
    )

All LLM, tool, and session events are captured automatically. See Tool Tracking for how multi-turn tool-use loops are traced with zero extra code.

OpenAI

import openai
from dapplepot_sdk import DapplePot

dp = DapplePot(
    sdk_key    = "dp_sk_...",
    tenant_id  = "...",
    agent_id   = "...",
    ingest_url = "https://ingest.dapplepot.com",
)
dp.instrument_openai()

with dp.session(user_context_id="user_123"):
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}],
    )

LangChain / LangGraph

LangChain fires its own callbacks during chain or graph execution — pass dp.callback_handler() and DapplePot listens.

from dapplepot_sdk import DapplePot

dp = DapplePot(
    sdk_key    = "dp_sk_...",
    tenant_id  = "...",
    agent_id   = "...",
    ingest_url = "https://ingest.dapplepot.com",
)

handler = dp.callback_handler(
    user_context_id="user_123",
    user_tenant_id="acme_corp",
)
result = graph.invoke(
    {"messages": [...]},
    config={"callbacks": [handler]},
)

For LangGraph: each named graph node automatically becomes a node_start / node_end event. Tool calls handled via ToolNode emit tool_start / tool_end automatically.

Core API

dp.session(*, user_context_id=None, user_tenant_id=None)

Context manager that opens a DapplePot session for a multi-call conversation. The session ID is generated automatically.

with dp.session(user_context_id="u_42", user_tenant_id="acme") as sess:
    # All LLM calls inside this block belong to the same session
    response = client.messages.create(...)

Parameter	Description
user_context_id	Identifies the end-user within the session (optional)
user_tenant_id	For multi-tenant agents, identifies the customer tenant (optional)

Outside dp.session(), every patched LLM call becomes its own one-event session.

dp.node(name, *, input=None)

Optional context manager that adds named structure to your agent code. Emits node_start on entry, node_end on success, or node_error if an exception escapes.

with dp.session():
    with dp.node("retrieve_context", input=query):
        docs = vector_store.search(query)

    with dp.node("generate_response"):
        response = client.messages.create(...)

Use this when you want named steps in the trace UI — it is entirely optional. LangChain and LangGraph emit node events automatically from their own callbacks.

dp.callback_handler(*, user_context_id=None, user_tenant_id=None)

Returns a fresh DapplePotCallbackHandler for one invocation of a LangChain chain or LangGraph run.

Tool Tracking

For the Anthropic and OpenAI integrations, tool calls are detected automatically — no client-side instrumentation is required.

Anthropic: when the model returns tool_use content blocks, tool_start is emitted. When the next messages.create() call carries matching tool_result blocks, tool_end is emitted with the real tool output and latency. Set is_error=True on the tool_result block to emit tool_error instead.

OpenAI: when the model returns tool_calls, tool_start is emitted. When the next chat.completions.create() carries the corresponding role="tool" message, tool_end is emitted. Add is_error=True to the tool message to emit tool_error instead (a DapplePot convention — OpenAI ignores extra fields).

LangChain / LangGraph: tool callbacks are wired automatically by the framework's own BaseTool and ToolNode.

Constructor

DapplePot(
    sdk_key,     # required — your SDK key from the DapplePot dashboard
    tenant_id,   # required — your tenant ID
    agent_id,    # required — the agent being instrumented
    ingest_url,  # required — your DapplePot ingest endpoint
    *,
    sample_rate        = 1.0,   # 0.0–1.0; fraction of sessions traced
    pii_scrubber       = None,  # custom scrubber object (must implement .scrub_value())
    redact_keys        = None,  # list[str] of payload keys to replace with [REDACTED]
    flush_interval_ms  = 500,   # background flush cadence
    flush_batch_size   = 100,   # max events per flush batch
)

All events are posted to {ingest_url}/v1/ingest/events with Authorization: Bearer {sdk_key}.

Call dp.shutdown() on exit to flush buffered events.

Online Security Checks

The SDK runs 12 sub-checks synchronously on every event. When a check fires it emits a finding immediately (without waiting for session end). Which checks are active and what action they take is configured per-agent in the DapplePot dashboard and fetched automatically at SDK startup.

Sub-check	Category	Phase	Severity
PI-01a	prompt_injection	input	high
PI-01b	prompt_injection	input	critical
PI-01c	prompt_injection	input	high
PI-02a	prompt_injection	tool_end	high
PI-05a	prompt_injection	input	high
PI-08a	prompt_injection	llm_start	high
SID-01a	data_disclosure	output	critical
SID-01c	data_disclosure	output	critical
SID-02a	data_disclosure	output	high
IOH-01a	output_handling	output	critical
EA-01a	excessive_agency	tool_start	high
EA-02b	excessive_agency	tool_start	high

Check configuration and the tool manifest are fetched from the DapplePot API at startup. Update checks in the dashboard and restart your agent — no code changes needed.

Actions

When a check fires, it takes one of four actions (configured per sub-check in the dashboard):

Action	Effect
alert	Logs a warning; execution continues
sanitize	Redacts matched content from the event payload; execution continues
block_call	Raises DapplePotBlockedError
terminate_session	Raises DapplePotSessionTerminatedError

from dapplepot_sdk import DapplePot, DapplePotBlockedError, DapplePotSessionTerminatedError

try:
    result = graph.invoke(...)
except DapplePotBlockedError as e:
    print(e.signal, e.reason, e.session_id)
except DapplePotSessionTerminatedError:
    print("Session terminated by security policy")

PII Scrubbing

Use the built-in RegexScrubber or implement your own:

from dapplepot_sdk import DapplePot
from dapplepot_sdk.scrubbers import RegexScrubber

scrubber = RegexScrubber(patterns=["email", "ssn", "aws_key", "jwt", "phone"])

dp = DapplePot(
    sdk_key      = "dp_sk_...",
    tenant_id    = "...",
    agent_id     = "...",
    ingest_url   = "https://ingest.dapplepot.com",
    pii_scrubber = scrubber,
)

Built-in pattern names: email, phone, ssn, credit_card, uk_nino, iban, ip_address, aws_key, jwt.

To implement a custom scrubber, subclass BaseScrubber and implement scrub(text: str) -> str:

from dapplepot_sdk.scrubbers import BaseScrubber

class MyScrubber(BaseScrubber):
    def scrub(self, text: str) -> str:
        return text.replace("sensitive", "[REDACTED]")

You can also redact known keys without touching values:

dp = DapplePot(..., redact_keys=["api_key", "password", "ssn"])

Impact on detection: Scrubbing runs after real-time online checks, so live session checks marked for online detection (e.g. SID-02a) still operate on the original content and fire correctly. However, events stored for post-session analysis contain the scrubbed values — PII is replaced with tokens like [EMAIL] or [SANITIZED] before storage, so any offline detection pass on those events will find nothing to flag.

Event Reference

All events flow through POST {ingest_url}/v1/ingest/events.

Event	When
session_start	dp.session() enter, or first patched call in standalone mode
session_end	dp.session() exit (normal close), or last patched call in standalone mode
session_error	Exception escapes dp.session() uncaught
node_start	dp.node() enter, or LangGraph/LangChain child chain start
node_end	dp.node() clean exit, or LangGraph/LangChain child chain end
node_error	dp.node() raises, or LangGraph/LangChain child chain fails
llm_start	Patched LLM call begins
llm_end	Patched LLM call returns (tokens, latency, model, finish reason)
llm_error	Patched LLM call raises
tool_start	Tool invoked by the model (auto-detected from response)
tool_end	Tool result fed back into the model (auto-detected from next call)
tool_error	Tool result marked is_error=True, or framework tool callback fails
security_finding	Online security check fired (out-of-band)

Errors propagate upward only as far as the exception actually travels. Catch an exception inside dp.node() and the session keeps going. Catch it inside dp.session() and you only see node_error and llm_error, never session_error.

License

Licensed under the Apache License, Version 2.0. See LICENSE and NOTICE for details.