Metadata-Version: 2.4
Name: agentid-sdk
Version: 0.1.20
Summary: Enterprise Python SDK for AI guardrails, PII protection, and telemetry logging.
Project-URL: Homepage, https://agentid.ai
Project-URL: Repository, https://github.com/ondrejsukac-rgb/agentid/tree/main/python-sdk
Project-URL: Issues, https://github.com/ondrejsukac-rgb/agentid/issues
Author: AgentID
License: MIT
Requires-Python: >=3.9
Requires-Dist: httpx>=0.27.0
Provides-Extra: pii
Requires-Dist: presidio-analyzer>=2.2.0; extra == 'pii'
Requires-Dist: presidio-anonymizer>=2.2.0; extra == 'pii'
Requires-Dist: pyahocorasick>=2.0.0; extra == 'pii'
Requires-Dist: spacy>=3.0.0; extra == 'pii'
Provides-Extra: security
Requires-Dist: google-re2>=1.1.20240702; extra == 'security'
Requires-Dist: numpy; extra == 'security'
Requires-Dist: torch>=2.0.0; extra == 'security'
Requires-Dist: transformers>=4.0.0; extra == 'security'
Provides-Extra: test
Requires-Dist: pytest-asyncio>=0.24.0; extra == 'test'
Requires-Dist: pytest>=8.0.0; extra == 'test'
Description-Content-Type: text/markdown

# agentid-sdk (Python)

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[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

## 1. Introduction

`agentid-sdk` is the official Python SDK for AgentID, an AI security and compliance System of Record. It lets you enforce guardrails before model execution, capture immutable telemetry for auditability, and integrate security checks into OpenAI and LangChain workflows with minimal code.

### The Mental Model

AgentID sits between your application and the LLM runtime:

```text
User Input -> guard() -> [AgentID Policy] -> verdict
                              | allowed
                              v
                         LLM Provider
                              v
                           log() -> [Immutable Ledger]
```

- `guard()`: evaluates prompt and context before model execution.
- Model call: executes only if guard verdict is allowed.
- `log()`: persists immutable telemetry (prompt, output, latency) for audit and compliance.

## 2. Installation

```bash
pip install agentid-sdk
```

Optional extras:

```bash
pip install "agentid-sdk[pii]"
pip install "agentid-sdk[security]"
```

If you enable Presidio/spaCy-backed PII detection, install the spaCy language model:

```bash
pip install "agentid-sdk[pii]"
python -m spacy download en_core_web_lg
```

## 3. Prerequisites

1. Create an AgentID account at `https://app.getagentid.com`.
2. Create an AI system and copy:
   - `AGENTID_API_KEY` (for example `sk_live_...`)
   - `AGENTID_SYSTEM_ID` (UUID)
3. If using OpenAI/LangChain, set:
   - `OPENAI_API_KEY`

```bash
export AGENTID_API_KEY="sk_live_..."
export AGENTID_SYSTEM_ID="00000000-0000-0000-0000-000000000000"
export OPENAI_API_KEY="sk-proj-..."
```

### Compatibility

- **Node.js:** v18+ / **Python:** 3.9+ (cross-SDK matrix)
- **Thread Safety:** AgentID clients are thread-safe and intended to be instantiated once and reused across concurrent requests.
- **Latency:** async `log()` is non-blocking for model execution paths; sync `guard()` typically adds network latency (commonly ~50-100ms, environment-dependent).

## 4. Quickstart

```python
import os
from agentid import AgentID

agent = AgentID()  # auto-loads AGENTID_API_KEY
system_id = os.environ["AGENTID_SYSTEM_ID"]

verdict = agent.guard(
    input="Summarize this support ticket.",
    system_id=system_id,
    model="gpt-4o-mini",
    user_id="quickstart-user",
)
if not verdict.get("allowed", False):
    raise RuntimeError(f"Blocked: {verdict.get('reason')}")

agent.log(
    system_id=system_id,
    input="Summarize this support ticket.",
    output="Summary generated.",
    model="gpt-4o-mini",
    event_id=verdict.get("client_event_id"),
    metadata={"agent_role": "support-assistant"},
)
print("Guard allowed + telemetry logged")
```

## 5. Core Integrations

### OpenAI Wrapper

```python
import os
from openai import OpenAI
from agentid import AgentID, SecurityBlockError

agent = AgentID(pii_masking=True)
openai = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
secured = agent.wrap_openai(
    openai,
    system_id=os.environ["AGENTID_SYSTEM_ID"],
    user_id="customer-123",
)

try:
    response = secured.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "What is the capital of the Czech Republic?"}],
    )
    print(response.choices[0].message.content)
except SecurityBlockError as exc:
    print("Blocked by AgentID:", exc.reason)
```

Wrapped OpenAI calls persist telemetry for both regular and streamed completions. For `stream=True`, logging happens when the stream finishes.

> Scope note: AgentID compliance/risk controls apply to the specific SDK-wrapped LLM calls (`guard()`, `wrap_openai()`, LangChain callback-wrapped flows). They do not automatically classify unrelated code paths in your whole monolithic application.

### LangChain Integration

```bash
pip install agentid-sdk openai langchain langchain-openai
```

```python
import os
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from agentid import AgentID, AgentIDCallbackHandler

agent = AgentID()
handler = AgentIDCallbackHandler(agent, system_id=os.environ["AGENTID_SYSTEM_ID"])

prompt = PromptTemplate.from_template("Answer in one sentence: {question}")
model = ChatOpenAI(model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"])
chain = prompt | model | StrOutputParser()

result = chain.invoke(
    {"question": "What is the capital of the Czech Republic?"},
    config={"callbacks": [handler]},
)
print(result)
```

LangChain callbacks log on run completion. Token/cost telemetry for streamed chains depends on the provider exposing usage in the final LangChain result.

### Raw Ingest API (Telemetry Only)

```python
import os
from agentid import AgentID

agent = AgentID()
agent.log(
    system_id=os.environ["AGENTID_SYSTEM_ID"],
    event_type="complete",
    severity="info",
    model="gpt-4o-mini",
    input="Raw telemetry prompt",
    output='{"ok": true}',
    metadata={"agent_role": "batch-worker", "channel": "manual_ingest"},
)
```

## 6. Advanced Configuration

### Custom identity / role metadata

Use `user_id` for actor identity and `metadata` for additional context (for example `agent_role`, environment, trace IDs).

```python
verdict = agent.guard(
    input="Process user request",
    system_id=system_id,
    user_id="service:billing-agent",
)
agent.log(
    system_id=system_id,
    input="Process user request",
    output="Done",
    model="gpt-4o-mini",
    metadata={"agent_role": "billing-agent", "environment": "prod"},
)
```

### Timeouts

```python
agent = AgentID(
    guard_timeout_s=10.0,
    ingest_timeout_s=10.0,
    strict_mode=True,  # fail-closed on connectivity/timeouts
)
```

### Optional client-side fast fail

```python
agent = AgentID(
    failure_mode="fail_close",
    client_fast_fail=True,  # opt-in local preflight before /guard
)
```

### Error Handling & Strict Mode

By default, AgentID is designed to keep your application running if the AgentID API has a timeout or is temporarily unreachable.

| Mode | Connectivity Failure | LLM Execution | Best For |
| :--- | :--- | :--- | :--- |
| **Default** (Strict Off) | API Timeout / Unreachable | **Fail-Open** (continues) | Standard SaaS, chatbots |
| **Strict Mode** (`strict_mode=True`) | API Timeout / Unreachable | Direct `guard()` denies; wrapped flows can apply local fallback first | Healthcare, FinTech, high-risk |

- `guard()` returns a verdict (`allowed`, `reason`); handle deny paths explicitly.
- Wrapped OpenAI/LangChain flows raise `SecurityBlockError` when a prompt is blocked.
- Backend `/guard` is the default authority for prompt injection, DB access, code execution, and PII leakage in SDK-wrapped flows.
- `client_fast_fail` is optional and disabled by default. Enable it only when you explicitly want local preflight before the backend call.
- If backend guard is unreachable and the effective failure mode is `fail_close`, wrapped OpenAI/LangChain flows can run local fallback enforcement. Local hits still block; otherwise the request can continue with fallback telemetry attached.
- If `strict_mode` is not explicitly set in SDK code, runtime behavior follows the system configuration from AgentID (`strict_security_mode` / `failure_mode`).
- Ingest retries transient failures (5xx/429) and logs warnings if persistence fails.

### Event Identity Model

For consistent lifecycle correlation in Activity/Prompts, use this model:

- `client_event_id`: external correlation ID for one end-to-end action.
- `guard_event_id`: ID of the preflight guard event returned by `guard()`.
- `event_id` on `log()`: idempotency key for ingest. In the Python SDK it is canonicalized to `client_event_id` for stable one-row lifecycle updates.

SDK behavior:

- `guard()` sends `client_event_id` and returns canonical `client_event_id` + `guard_event_id`.
- `log()` sends:
  - `event_id = canonical client_event_id`
  - `metadata.client_event_id`
  - `metadata.guard_event_id` (when available from wrappers/callbacks)
  - `x-correlation-id = client_event_id`
- after a successful primary ingest, SDK wrappers can call `/ingest/finalize` with the same `client_event_id` to attach `sdk_ingest_ms`
- SDK requests include `x-agentid-sdk-version` for telemetry/version diagnostics.

This keeps Guard + Complete linked under one correlation key while preserving internal event linkage in the dashboard.

### SDK Timing Telemetry

SDK-managed metadata can include:

- `sdk_config_fetch_ms`: capability/config fetch time before dispatch.
- `sdk_local_scan_ms`: optional local enforcement time (`client_fast_fail` or fail-close fallback path).
- `sdk_guard_ms`: backend `/guard` round-trip time observed by the SDK wrapper.
- `sdk_ingest_ms`: post-ingest transport timing finalized by the SDK through `/ingest/finalize` after a successful primary `/ingest`.

### Policy-Pack Runtime Telemetry

When the backend uses compiled policy packs, runtime metadata includes:

- `policy_pack_version`: active compiled artifact version.
- `policy_pack_fallback`: `true` means fallback detector path was used.
- `policy_pack_details`: optional diagnostic detail for fallback/decision trace.

Latency interpretation:

- Activity `Latency (ms)` maps to synchronous processing (`processing_time_ms`).
- Async AI audit time is separate (`ai_audit_duration_ms`) and can be higher.
- First request after warm-up boundaries can be slower than steady-state requests.

### Monorepo QA Commands (Maintainers)

If you are validating runtime in the AgentID monorepo:

```bash
npm run qa:policy-pack-bootstrap -- --base-url=http://127.0.0.1:3000/api/v1 --system-id=<SYSTEM_UUID>
npm run bench:policy-pack-hotpath
```

PowerShell diagnostics:

```powershell
powershell -ExecutionPolicy Bypass -File .\scripts\qa\run-guard-diagnostic.ps1 -BaseUrl http://127.0.0.1:3000/api/v1 -ApiKey $env:AGENTID_API_KEY -SystemId $env:AGENTID_SYSTEM_ID -SkipBenchmark
powershell -ExecutionPolicy Bypass -File .\scripts\qa\run-ai-label-audit-check.ps1 -BaseUrl http://127.0.0.1:3000/api/v1 -ApiKey $env:AGENTID_API_KEY -SystemId $env:AGENTID_SYSTEM_ID -Model gpt-4o-mini
```

## 7. Security & Compliance

- Optional local-first reversible PII masking via `PIIManager` and `pii_masking=True`.
- Backend `/guard` remains the primary enforcement authority by default.
- `client_fast_fail` is opt-in; local enforcement is otherwise reserved for fail-close outage fallback.
- Telemetry logging is async/fire-and-forget to minimize app latency, with SDK timing breakdowns finalized on the lifecycle row.
- Designed for server, serverless, and background-worker runtimes.
- Supports compliance workflows requiring complete prompt/output traceability.

## 8. Support

- Dashboard: `https://app.getagentid.com`
- Repository: `https://github.com/ondrejsukac-rgb/agentid/tree/main/python-sdk`
- Issues: `https://github.com/ondrejsukac-rgb/agentid/issues`

## 9. Publishing Notes (PyPI)

PyPI renders this `README.md` as package long description.

### `setup.py` projects

```python
from setuptools import setup

with open("README.md", "r", encoding="utf-8") as fh:
    long_description = fh.read()

setup(
    name="agentid-sdk",
    long_description=long_description,
    long_description_content_type="text/markdown",
)
```

### `pyproject.toml` projects

```toml
readme = { file = "README.md", content-type = "text/markdown" }
```
