Metadata-Version: 2.4
Name: amplitude-ai
Version: 1.4.3
Summary: Agent analytics for Amplitude
Project-URL: Homepage, https://amplitude.com
Project-URL: Documentation, https://docs.amplitude.com
Author-email: Amplitude <sdk@amplitude.com>
License-Expression: MIT
License-File: LICENSE
Keywords: agents,ai,amplitude,analytics,anthropic,llm,observability,openai
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: amplitude-analytics>=1.0.0
Requires-Dist: genai-prices>=0.0.53
Provides-Extra: all
Requires-Dist: anthropic<1.0.0,>=0.75.0; extra == 'all'
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Description-Content-Type: text/markdown

# amplitude-ai

[![PyPI](https://img.shields.io/pypi/v/amplitude-ai)](https://pypi.org/project/amplitude-ai/)
[![Python](https://img.shields.io/pypi/pyversions/amplitude-ai)](https://pypi.org/project/amplitude-ai/)
[![License](https://img.shields.io/pypi/l/amplitude-ai)](https://pypi.org/project/amplitude-ai/)

Agent analytics for [Amplitude](https://amplitude.com). Track every LLM call, user message, tool call, and quality signal as events in your Amplitude project — then build funnels, cohorts, and retention charts across AI and product behavior.

```bash
pip install amplitude-ai
```

```python
from amplitude import Amplitude
from amplitude_ai import AmplitudeAI, OpenAI  # or AsyncOpenAI for async frameworks

ai = AmplitudeAI(amplitude=Amplitude("YOUR_API_KEY"))
openai = OpenAI(amplitude=ai, api_key="sk-...")
agent = ai.agent("my-agent")

@app.post("/chat")
async def chat(request):
    data = await request.json()
    with agent.session(user_id=data["user_id"], session_id=data["session_id"]) as session:
        session.track_user_message(data["message"])
        response = openai.chat.completions.create(
            model="gpt-4o", messages=data["messages"],
        )
        return {"content": response.choices[0].message.content}
    # Events: [Agent] User Message, [Agent] AI Response (with model, tokens, cost, latency),
    #         [Agent] Session End — all tied to user_id and session_id
```


## How to Get Started

### Instrument with a coding agent (recommended)

```bash
pip install amplitude-ai
amplitude-ai
```

The CLI prints a prompt to paste into any AI coding agent (Cursor, Claude Code, Windsurf, Copilot, Codex, etc.):

> Instrument this app with amplitude-ai. Follow \<path-to-site-packages\>/amplitude-ai.md

The agent reads the guide, scans your project, discovers your agents and LLM call sites, and instruments everything — provider wrappers, session lifecycle, multi-agent delegation, tool tracking, scoring, and a verification test. You review and approve each step.

### Manual setup

Whether you use a coding agent or set up manually, the goal is the same: **full instrumentation** — agents + sessions + provider wrappers. This gives you every event type, per-user analytics, and server-side enrichment.

Follow the [code example above](#amplitude-ai) to get started. The pattern is:

1. **Swap your LLM import** — `from amplitude_ai import OpenAI` (or `AsyncOpenAI`, `Anthropic`, etc.)
2. **Create an agent** — `ai.agent("my-agent")` to name and track your AI component
3. **Wrap in a session** — `agent.session(user_id=..., session_id=...)` for per-user analytics, funnels, cohorts, and server-side enrichment
4. **Track user messages** — `session.track_user_message(...)` for conversation context
5. **Score responses** — `session.score(...)` for quality measurement

> `patch()` exists for quick verification or legacy codebases where you can't modify call sites, but it only captures `[Agent] AI Response` without user identity — no funnels, no cohorts, no retention. Start with full instrumentation; fall back to `patch()` only if you can't modify call sites.

## Table of Contents

- [How to Get Started](#how-to-get-started)
- [Quickstart (5 minutes)](#quickstart-5-minutes)
- [Current Limitations](#current-limitations)
- [Is this for me?](#is-this-for-me)
- [Why this SDK?](#why-this-sdk)
- [What you can build](#what-you-can-build)
- [Integration Approaches](#integration-approaches)
- [Multi-service / Distributed Tracing](#multi-service-distributed-tracing)
- [Developer Experience](#developer-experience)
- [Model Tier Auto-Inference](#model-tier-auto-inference)
- [Semantic Cache Tracking](#semantic-cache-tracking)
- [FastAPI / Starlette Middleware](#fastapi-starlette-middleware)
- [Get Started](#get-started)
  - [Path A: You use Amplitude for product analytics](#path-a-you-use-amplitude-for-product-analytics)
  - [Path B: You already use an OTEL LLM tool](#path-b-you-already-use-an-otel-llm-tool)
  - [User Identity](#user-identity)
  - [Initialization Options](#initialization-options)
- [Core Concepts](#core-concepts)
- [Integration Patterns](#integration-patterns)
- [Going Deeper](#going-deeper)
  - [Privacy & Content Control](#privacy-content-control)
  - [Bound Agents & Sessions](#bound-agents-sessions)
  - [Context Dict Conventions](#context-dict-conventions)
  - [Multi-Agent Patterns](#multi-agent-patterns)
  - [Event Linking](#event-linking)
  - [Scoring](#scoring)
  - [Labeling and Tagging Messages](#labeling-and-tagging-messages)
  - [Enrichments](#enrichments)
  - [Provider Wrappers](#provider-wrappers)
- [Event Schema](#event-schema)
- [Event Property Reference](#event-property-reference)
- [Sending Events Without the SDK](#sending-events-without-the-sdk)
- [Register Event Schema in Your Data Catalog](#register-event-schema-in-your-data-catalog)
- [Content Storage (`$llm_message`)](#content-storage-llm_message)
- [Troubleshooting](#troubleshooting)
- [For AI Coding Agents](#for-ai-coding-agents)
- [Reporting Issues](#reporting-issues)

### Quickstart (5 minutes)

1. **Install:** `pip install amplitude-ai`
2. **Get your API key:** In Amplitude, go to **Settings > Projects** and copy the API key.
3. **Instrument:** Run `amplitude-ai` and paste the printed prompt into your AI coding agent. Or follow the [manual setup](#manual-setup) steps — the goal is the same: agents + sessions + provider wrappers.
4. **Set your API key** in the generated `.env` file and replace the placeholder `user_id`/`session_id`.
5. **Run your app.** You should see `[Agent] User Message`, `[Agent] AI Response`, and `[Agent] Session End` within 30 seconds.

To verify locally before checking Amplitude, add `debug=True`:
```python
ai = AmplitudeAI(amplitude=Amplitude("YOUR_API_KEY"), config=AIConfig(debug=True))
# Prints: [amplitude-ai] [Agent] AI Response | model=gpt-4o | tokens=847 | cost=$0.0042 | latency=1,203ms
```


### What full instrumentation gives you

Full instrumentation means agents + sessions + provider wrappers. This is the recommended setup for both coding agent and manual workflows.

| What you set | What you unlock |
|---|---|
| Provider wrapper (`from amplitude_ai import OpenAI`) | Model, tokens, cost, latency, TTFB — auto-captured per call |
| + `user_id` | Per-user funnels, cohorts, retention |
| + `session_id` (via `agent.session(...)`) | Session grouping, server-side enrichment, quality scoring, behavioral patterns |
| + `score()` calls | Explicit quality signals alongside automated evals |

Adding `user_id` is one parameter per call. Adding session context is two lines. See [User Identity](#user-identity) and [Integration Approaches](#integration-approaches).

> **Tip:** Call `enable_live_price_updates()` at startup so cost tracking stays accurate when new models are released. See [Cache-Aware Cost Calculation](#cache-aware-cost-calculation).

### Current Limitations

| Area | Status |
|---|---|
| Language support | Python only. JS/TS SDK is the next major investment (no public ETA yet). |
| Zero-code patching | OpenAI, Anthropic, Azure OpenAI, Gemini, Mistral. Bedrock: use `wrap()` or swap import. CLI wrapper available for env-var-only setup. |
| Proxy/gateway instrumentation | Use the [OTEL bridge](#otel-genai-bridge-reference) for proxy setups (LiteLLM, Portkey, custom gateways). See [Path B](#path-b-you-already-use-an-otel-llm-tool). |
| Streaming cost tracking | Automatic for OpenAI and Anthropic. Manual token counts for other providers' streamed responses. |

### Is this for me?

**Yes, if** you're building an AI-powered feature (chatbot, copilot, agent, RAG pipeline) and you want to measure how it impacts real user behavior. AI events land in the same Amplitude project as your product events, so you can build funnels from "user asks a question" to "user converts," create cohorts of users with low AI quality scores, and measure retention without stitching data across tools.

**Already using an LLM observability tool?** Keep it. The [OTEL bridge](#otel-genai-bridge-reference) adds Amplitude as a second destination in one line. Your existing traces stay, and you get product analytics on top.

This SDK is for teams who want AI session review, automated enrichment, and business impact measurement in the same place they measure product behavior. The [quickstart](#quickstart-5-minutes) takes under 5 minutes.

### Why this SDK?

Most AI observability tools give you traces. This SDK gives you **per-turn events that live in your product analytics** so you can build funnels from "user opens chat" through "AI responds" to "user converts," create cohorts of users with low AI quality scores and measure their 7-day retention, and answer "is this AI feature helping or hurting?" without moving data between tools.

The structural difference is the event model. Trace-centric tools typically produce spans per LLM call. This SDK produces **one event per conversation turn** with 40+ properties: model, tokens, cost, latency, reasoning, implicit feedback signals (regeneration, copy, abandonment), cache breakdowns, agent hierarchy, and experiment context. Each event is independently queryable in Amplitude's charts, cohorts, funnels, and retention analysis.

**Every AI event carries your product `user_id`.** No separate identity system, no data joining required. Build a funnel from "user opens chat" to "AI responds" to "user upgrades" directly in Amplitude, using the same user properties and cohort definitions you already have.

**Server-side enrichment does the evals for you.** When content is available (`content_mode="full"`), Amplitude's enrichment pipeline runs automatically on every session after it closes. You get topic classifications, quality rubrics, behavioral flags, and session outcomes without writing or maintaining any eval code. Define your own topics and scoring rubrics; the pipeline applies them to every session automatically. Results appear as `[Agent] Score` events with rubric scores, `[Agent] Topic Classification` events with category labels, and `[Agent] Session Evaluation` summaries, all queryable in charts, cohorts, and funnels alongside your product events.

**Quality signals from every source in one event type.** User thumbs up/down (`source="user"`), automated rubric scores from the enrichment pipeline (`source="ai"`), and reviewer assessments (`source="reviewer"`) all produce `[Agent] Score` events differentiated by `[Agent] Evaluation Source`. One chart shows all three side by side. Filter by source or view them together. Filter by `[Agent] Agent ID` for per-agent quality attribution.

**Three content-control tiers.** `full` sends content and Amplitude runs enrichments for you. `metadata_only` sends zero content (you still get cost, latency, tokens, session grouping, and everything that doesn't require text). `customer_enriched` sends zero content but lets you provide your own structured labels via `track_session_enrichment()` for the same analytics value with full data control. See [Privacy & Content Control](#privacy-content-control) for what each tier enables.

**Cache-aware cost tracking.** Pass `cache_read_tokens` and `cache_creation_tokens` for accurate blended costs. With Anthropic's prompt caching, naive `tokens x price` overestimates by 2-5x on multi-turn sessions. The SDK uses cache-aware pricing automatically via [genai-prices](https://pypi.org/project/genai-prices/) when you provide the token breakdown. Supported for OpenAI, Anthropic, Gemini, Azure OpenAI, and AWS Bedrock.

**Works alongside your existing LLM tools.** Add the OTEL GenAI exporter to your pipeline to send spans to Amplitude alongside Langfuse, OpenLIT, or other destinations with no changes to your existing instrumentation code. Use LangChain, LlamaIndex, OpenAI Agents SDK, Anthropic tool_use loop, or CrewAI integrations for framework-level tracking. Or swap in provider wrappers (OpenAI, Anthropic, Gemini, Azure, Bedrock, Mistral) for the richest field coverage.

**Multi-agent and multi-tenant from day one.** `ai.agent()` creates a bound handle that carries `agent_id`, `description`, `agent_version`, `env`, and optional multi-tenant fields so you never repeat them. `ai.tenant()` pre-fills `customer_org_id` and `groups` for platforms serving multiple customers. `agent.child()` auto-sets `parent_agent_id` and inherits `agent_version`. `agent.session()` manages lifecycle automatically and propagates context to provider wrappers and the OTEL bridge via Python's `contextvars`.

### What you can build

Once AI events are in Amplitude alongside your product events:

**Cohorts.** "Users who had 3+ task failures in the last 30 days." "Users with low task completion scores." Target them with Guides, measure churn impact.

**Funnels.** "AI session about charts -> Chart Created." "Sign Up -> First AI Session -> Conversion." Measure whether AI drives feature adoption and onboarding.

**Retention.** Do users with successful AI sessions retain better than those with failures? Segment retention curves by `[Agent] Overall Outcome` or task completion score.

**Agent analytics.** Compare quality, cost, and failure rate across agents in one chart. Identify which agent in a multi-agent chain introduced a failure.

### How quality measurement works

The SDK captures quality signals at three layers, from most direct to most comprehensive:

**1. Explicit user feedback** — Instrument thumbs up/down, star ratings, or CSAT scores via [`score()`](#scoring). Each call produces an `[Agent] Score` event with `source="user"`:

```python
ai.score(user_id="u1", name="user-feedback", value=1,
         target_id=ai_msg_id, target_type="message", source="user")
```

**2. Implicit behavioral signals** — The SDK auto-tracks behavioral proxies for quality on every turn, with zero additional instrumentation:

| Signal | Property | Event | Interpretation |
|--------|----------|-------|----------------|
| Copy | `[Agent] Was Copied` | `[Agent] AI Response` | User copied the output — positive |
| Regeneration | `[Agent] Is Regeneration` | `[Agent] User Message` | User asked for a redo — negative |
| Edit | `[Agent] Is Edit` | `[Agent] User Message` | User refined their prompt — friction |
| Abandonment | `[Agent] Abandonment Turn` | `[Agent] Session End` | User left after N turns — potential failure |

**3. Automated server-side evaluation** — When `content_mode="full"`, Amplitude's enrichment pipeline runs LLM-as-judge evaluators on every session after it closes. No eval code to write or maintain:

| Rubric | What it measures | Scale |
|--------|-----------------|-------|
| `task_completion` | Did the agent accomplish what the user asked? | 0–2 |
| `response_quality` | Was the response clear, accurate, and helpful? | 0–2 |
| `user_satisfaction` | Did the user seem satisfied based on conversation signals? | 0–2 |
| `agent_confusion` | Did the agent misunderstand or go off track? | 0–2 |

Plus boolean detectors: `negative_feedback` (frustration phrases), `task_failure` (agent failed to deliver), `data_quality_issues`, and `behavioral_patterns` (clarification loops, topic drift). All results are emitted as `[Agent] Score` events with `source="ai"`.

**All three layers use the same `[Agent] Score` event type**, differentiated by `[Agent] Evaluation Source` (`"user"`, `"ai"`, or `"reviewer"`). One chart shows user feedback alongside automated evals. No joins, no separate tables.

### What You Set vs What You Get

| You set | Where it comes from | What you unlock |
|---|---|---|
| API key | Amplitude project settings | Events reach Amplitude |
| user_id | Your auth layer (JWT, session cookie, API token) | Per-user analytics, cohorts, retention |
| agent_id | Your choice (e.g. `'chat-handler'`) | Per-agent cost, latency, quality dashboards |
| session_id | Your conversation/thread/ticket ID | Multi-turn analysis, session enrichment, quality scores |
| *description* | *Your choice (e.g. `'Handles support queries via GPT-4o'`)* | *Human-readable agent registry from event streams* |
| *content_mode + redact_pii* | *Config (defaults work)* | *Server enrichment (automatic), PII scrubbing* |
| *model, tokens, cost* | *Auto-captured by provider wrappers* | *Cost analytics, latency monitoring* |
| parent_agent_id | Auto via `child()`/`run_as()` | Multi-agent hierarchy |
| env, agent_version, context | Your deploy pipeline | Segmentation, regression detection |

*Italicized rows require zero developer effort — they're automatic or have sensible defaults.*

**The minimum viable setup is 4 fields**: API key, user_id, agent_id, session_id.
Everything else is either automatic or a progressive enhancement.

### Integration Approaches

**Start with full instrumentation.** Use agents + sessions + provider wrappers. This is the recommended approach for both coding agent and manual workflows — it gives you every event type, per-user analytics, and server-side enrichment.

| Approach | When to use | What you get |
|---|---|---|
| **Full control** (recommended) | Any project, new or existing | `BoundAgent` + `Session` + provider wrappers — all event types, per-user funnels, cohorts, retention, quality scoring, enrichments |
| **FastAPI middleware** | Web app, auto-session per request | Same as full control with automatic session lifecycle via `AmplitudeAIMiddleware` |
| **Swap import** | Existing codebase, incremental adoption | `from amplitude_ai import OpenAI` — auto-tracking per call, add sessions when ready |
| **Wrap** | You've already created a client | `wrap(client, amplitude=amp)` — instruments an existing client instance |
| **Managed / hosted agents** | Anthropic Managed Agents, OpenAI Assistants, agent-as-a-service | Manual `track_user_message` + `track_ai_message` + `track_tool_call` with tokens/cost from the API response, or `ManagedAgentTracker` adapter |
| **Claude Agent SDK hooks** | Claude Agent SDK (`claude-code`, `claude-agent`) | `ClaudeAgentSDKTracker` — PreToolUse/PostToolUse hooks for real tool latency + message stream processing |
| **Zero-code / `patch()`** | Verification or legacy codebases only | `amplitude_ai.patch(amplitude=amp)` — `[Agent] AI Response` + auto-extracted `[Agent] Tool Call`, no user identity, no funnels |

> The first four approaches all support the full event model. Choose based on how you want to integrate — the analytics capabilities are the same. **`patch()` is the exception**: it only captures aggregate `[Agent] AI Response` events without user identity, useful only for verifying the SDK works or for codebases where you can't modify call sites.

**New in v1.4.0:** `patch()` now automatically extracts `[Agent] Tool Call` events from LLM message arrays — no manual `track_tool_call()` needed for basic tool tracking. It scans conversation messages for tool calls and their results across OpenAI Chat Completions (`tool_calls` arrays), OpenAI Responses API (`function_call` / `function_call_output`), and Anthropic Messages (`tool_use` / `tool_result` blocks). Extracted tool calls have `latency_ms=0` since execution timing isn't available through message inspection; use the `@tool` decorator or `ClaudeAgentSDKTracker` hooks for real latency.

**Zero-code** patches provider modules so existing calls are tracked without code changes:

```python
import amplitude_ai

amplitude_ai.patch(amplitude=amp)
# All subsequent openai, anthropic, gemini, mistral calls are instrumented

amplitude_ai.unpatch()  # Restore all originals -- critical for test isolation
```

`patch()` auto-detects installed providers and returns a list of what it patched (e.g. `["openai", "anthropic", "gemini"]`). If you only want to patch a specific provider, use the per-provider functions:

```python
amplitude_ai.patch_openai(amplitude=amp)
amplitude_ai.patch_async_openai(amplitude=amp)
amplitude_ai.patch_anthropic(amplitude=amp)
amplitude_ai.patch_gemini(amplitude=amp)
amplitude_ai.patch_mistral(amplitude=amp)
amplitude_ai.patch_azure_openai(amplitude=amp)
```

**Declaring expected providers (optional).** You can declare which providers your application expects to use and have the SDK log a one-time warning if the set it actually patches differs:

```python
amplitude_ai.patch(
    amplitude=amp,
    expected_providers=["openai"],  # declared set
    app_key="support-bot",            # optional, used to dedupe warnings
)
# If the code ends up importing Anthropic too, a single warning is logged:
#   amplitude-ai: application 'support-bot'; declared providers ['openai']
#     do not match providers patched at runtime ['anthropic', 'openai'];
#     unexpected: ['anthropic']. Events will still be emitted; ...
```

This is warn-only — it never throws, never blocks `patch()`, and never
interferes with event emission. Useful for catching drift between your
declared configuration and what your code actually imports.

Zero-code patching is available for OpenAI (sync and async), Anthropic, Azure OpenAI, Gemini, and Mistral. For Bedrock, use the **Swap import** provider class directly (`from amplitude_ai.providers.bedrock import Bedrock`) because the `boto3.client()` factory pattern doesn't support clean monkey-patching.

**`amplitude-ai-instrument`** — run any Python application with automatic LLM instrumentation, using only environment variables and no source code changes:

```bash
pip install amplitude-ai
AMPLITUDE_AI_API_KEY=xxx AMPLITUDE_AI_AUTO_PATCH=true amplitude-ai-instrument python app.py
```

`amplitude-ai-instrument` initializes the Amplitude SDK, auto-detects installed LLM providers (OpenAI, Anthropic, Gemini, Mistral, Azure OpenAI), patches them via `patch()`, then executes your command. All LLM calls in your app are instrumented automatically. This is equivalent to adding `amplitude_ai.patch(amplitude=amp)` at the top of your code, but without modifying any source files.

> **Limitation:** Because `amplitude-ai-instrument` uses `patch()` under the hood, it only captures `[Agent] AI Response` events without user identity or session context. Use it for quick verification, demos, or legacy codebases. For per-user analytics, funnels, and enrichments, use [full instrumentation](#integration-approaches) instead.

| Variable | Description |
|---|---|
| `AMPLITUDE_AI_API_KEY` | **(required)** Amplitude API key |
| `AMPLITUDE_AI_AUTO_PATCH` | Must be `"true"` to enable patching |
| `AMPLITUDE_AI_CONTENT_MODE` | `"full"` (default), `"metadata_only"`, or `"customer_enriched"` |
| `AMPLITUDE_AI_DEBUG` | `"true"` for colored event summaries on stderr |

**Doctor CLI**:

Validate setup (env, provider deps, mock event capture, mock flush path):

```bash
amplitude-ai-doctor
```

Useful flags:

- `amplitude-ai-doctor --no-mock-check`

**Status CLI**:

Print the SDK's current state (version, patched providers, active sessions):

```bash
amplitude-ai-status
```

**MCP server**:

Run the SDK-local MCP server over stdio:

```bash
amplitude-ai-mcp
```

MCP surface:

| Tool | Description |
| --- | --- |
| `get_event_schema` | Return the full event schema and property definitions |
| `get_integration_pattern` | Return canonical instrumentation code patterns |
| `validate_setup` | Check env vars and dependency presence |
| `suggest_instrumentation` | Context-aware next steps based on your framework and provider |
| `validate_file` | Analyze source code to detect uninstrumented LLM call sites |

Resources: `amplitude-ai://event-schema`, `amplitude-ai://integration-patterns`

Prompt: `instrument_app` — guided walkthrough for instrumenting an application

**Examples and AI coding agent guide**:

- Mock-based examples demonstrating the event model (also used as CI smoke tests):
  - `examples/zero_code_example.py`
  - `examples/wrap_openai_example.py`
  - `examples/multi_agent_example.py`
  - `examples/framework_integration_example.py`
- Real provider examples (require API keys):
  - `examples/real_openai_example.py` — end-to-end OpenAI integration with session tracking and flush
  - `examples/real_anthropic_example.py` — end-to-end Anthropic integration with session tracking and flush
- AI coding agent guide:
  - `amplitude-ai.md` — self-contained 4-phase instrumentation guide for any AI coding agent

**Wrap** instruments a client you've already created (OpenAI, Anthropic, Azure OpenAI):

```python
from openai import OpenAI
client = OpenAI(api_key="sk-...")
wrapped = amplitude_ai.wrap(client, amplitude=amp, user_id="u1")
# wrapped is a real amplitude_ai.OpenAI instance
```

Move to **Full control** when you need multi-agent hierarchy, custom scoring, or session lifecycle management.

### Multi-service / Distributed Tracing

If your LLM pipeline spans multiple services (e.g., an orchestrator calling a retrieval service that calls an LLM), enable context propagation so sessions link across service boundaries:

```python
from amplitude_ai import AmplitudeAI
from amplitude_ai.config import AIConfig

ai = AmplitudeAI(
    amplitude=amplitude,
    config=AIConfig(propagate_context=True),
)
```

When enabled, provider wrappers inject W3C `traceparent` and `x-amplitude-session-id` headers on outgoing LLM calls. Downstream services running the SDK (or the `AmplitudeAIMiddleware`) automatically pick up this context, linking the sessions into a single distributed trace.

You can also inject/extract context manually for non-LLM HTTP calls:

```python
from amplitude_ai.propagation import inject_context, extract_context

# Sender: inject context into outgoing headers
headers = inject_context(existing_headers)
requests.post("https://downstream-service/api", headers=headers)

# Receiver: extract context from incoming headers
ctx = extract_context(request.headers)
# ctx = {"trace_id": "...", "session_id": "...", "agent_id": "..."}
```

Context propagation is opt-in (default `False`) because injecting extra headers into LLM API calls is harmless for most providers (they ignore unknown headers), but some proxies or custom endpoints may reject them.

### Developer Experience

Enable **debug mode** to see every tracked event in your terminal. Set it on `AIConfig` (or pass `debug=True` to `patch()` for the zero-code fallback):

```python
ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(debug=True))
# [amplitude-ai] [Agent] AI Response | user=u1 | session=a3f8... | model=gpt-4o | tokens=1,247 | cost=$0.0089 | latency=1,203ms
```

Use **dry-run mode** in CI to validate events without sending them:

```python
ai = AmplitudeAI(api_key="unused", config=AIConfig(dry_run=True))
```

Enable **strict validation** to catch bad inputs early (empty `user_id`, negative `latency_ms`, non-numeric scores):

```python
ai = AmplitudeAI(api_key="...", config=AIConfig(validate=True))
# Raises ValidationError on bad inputs instead of silently continuing
```

Combine all three for the strictest CI configuration:

```python
ai = AmplitudeAI(api_key="unused", config=AIConfig(debug=True, dry_run=True, validate=True))
```

Inspect current configuration at any time:

```python
ai.status()
# {"content_mode": "full", "debug": False, "dry_run": False,
#  "redact_pii": True, "providers_available": ["openai", "anthropic"],
#  "patched_providers": ["openai"]}
```


### Model Tier Auto-Inference

Every `[Agent] AI Response` event automatically includes a `[Agent] Model Tier` property (`"fast"`, `"standard"`, or `"reasoning"`) inferred from the model name. This enables cost optimization insights like "70% of simple sessions use your most expensive model."

Override when the auto-inference is wrong:

```python
ai.track_ai_message(..., model_tier="reasoning")
```

Coverage at launch: GPT-4o-mini/Haiku/Flash = `fast`, GPT-4o/Sonnet/Pro = `standard`, o1/o3/DeepSeek-R1 = `reasoning`.

### Semantic Cache Tracking

Track full-response semantic cache hits (distinct from token-level prompt caching):

```python
ai.track_ai_message(..., was_cached=True)  # Served from Redis/semantic cache
```

Maps to `[Agent] Was Cached`. Enables "cache hit rate" charts and cost optimization analysis.

### FastAPI / Starlette Middleware

Auto-create sessions per HTTP request with context propagation to all SDK calls within the handler:

```python
from amplitude_ai.middleware import AmplitudeAIMiddleware

app.add_middleware(
    AmplitudeAIMiddleware,
    amplitude_ai=ai,
    user_id_resolver=lambda request: request.state.user.id,
)
```

Provider wrappers and `@tool` calls within the request handler automatically inherit the session context. No manual `session_id` passing needed.

## Get Started

Both paths below lead to the same outcome: **full instrumentation** — agents + sessions + provider wrappers — giving you every event type, per-user funnels, cohorts, retention, and server-side enrichment.

### Path A: You use Amplitude for product analytics

You already have `amplitude-analytics` sending product events. Now you're adding AI features and want those events in the same project.

**Step 1: Install and create your agent**

```bash
pip install "amplitude-ai[openai]"   # or [anthropic], [gemini], [bedrock], [mistral]
```

```python
from amplitude import Amplitude
from amplitude_ai import AmplitudeAI, OpenAI  # drop-in replacement

# Share your existing Amplitude instance -- same pipeline, no duplicate queues
amplitude = Amplitude("YOUR_API_KEY")
ai = AmplitudeAI(amplitude=amplitude)
agent = ai.agent("my-agent", env="production")

# Use the wrapped client exactly like the original
client = OpenAI(amplitude=ai, api_key="sk-...")
```

**Step 2: Wrap in a session and track**

The provider wrapper call automatically inherits `session_id`, `trace_id`, `agent_id`, and `turn_id` from the active session via Python's `contextvars`. No extra parameters on the LLM call.

```python
with agent.session(user_id="user-1", session_id="conv-123") as s:
    s.track_user_message(content="What is retention?")
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "What is retention?"}],
    )
    # [Agent] User Message + [Agent] AI Response with session context
    s.score(name="helpful", value=1.0, target_id="...")
# [Agent] Session End auto-emitted here, triggering enrichment immediately.
# Without the `with` block, Amplitude auto-closes after 30 min of inactivity.
```

You're now getting the full event model: `[Agent] User Message`, `[Agent] AI Response` (with model, tokens, cost, latency), `[Agent] Score`, and `[Agent] Session End` — all tied to user_id and session_id, all appearing alongside your existing product events.

**Step 3: Progressive enhancement** *(add as needed)*

For sessions where gaps between messages may exceed 30 minutes (e.g., coding assistants, support agents waiting on customer replies), pass `idle_timeout_minutes` so Amplitude knows the session is still active:

```python
with agent.session(idle_timeout_minutes=240) as s:  # expect up to 4-hour gaps
    ...
```

Without this, sessions with long idle periods may be closed and evaluated prematurely. The default is 30 minutes.

**Link to Session Replay** *(optional)*

If your frontend uses Amplitude's [Session Replay](https://www.docs.developers.amplitude.com/session-replay/), you can link browser recordings to AI sessions. Pass the browser's `device_id` and `session_id` to `agent.session()` and every `[Agent]` event will automatically include the `[Amplitude] Session Replay ID` property (`device_id/session_id`), enabling one-click navigation from an AI session to the corresponding replay.

```python
# The frontend sends device_id and session_id to your backend
# (e.g., via request headers, query params, or the request body).
with agent.session(
    user_id="user-1",
    device_id=request.headers["X-Amp-Device-Id"],
    browser_session_id=request.headers["X-Amp-Session-Id"],
) as s:
    s.new_trace()
    s.track_user_message(content="What is retention?")
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "What is retention?"}],
    )
    # All events now carry [Amplitude] Session Replay ID
```

Provider wrappers, `@tool` calls, and manual `track_*` calls all inherit the replay ID automatically when inside the session block.

**What unlocks with full instrumentation:**

| Capability | Events | What it enables |
|---|---|---|
| **Provider wrapper** (auto) | `[Agent] User Message`, `[Agent] AI Response` | Model, tokens, cost, latency, TTFB, reasoning, system prompt, implicit feedback signals |
| **+ agent + session** (Steps 1-2 above) | + `[Agent] Session End`, `[Agent] Score` | Per-user funnels, cohorts, retention, session grouping, abandonment analysis, server-side enrichments |
| **+ manual track_\* calls** (optional) | + `[Agent] Tool Call`, `[Agent] Embedding`, `[Agent] Span`, `[Agent] Session Enrichment` | Full event graph, customer-provided enrichments, multi-agent hierarchies |

**Next:** [Scoring](#scoring) | [Enrichments](#enrichments) | [All providers](#provider-wrappers) | [Privacy](#privacy-content-control)

---

### Path B: You already use an OTEL LLM tool

Already using Langfuse, OpenLIT, or Datadog for tracing? Keep them. Add Amplitude as a second destination in one line. You get product analytics for AI (funnels, cohorts, retention across AI and product events) without ripping out your existing setup. The OTEL GenAI exporter consumes any [OTEL GenAI semantic convention](https://opentelemetry.io/docs/specs/semconv/gen-ai/) spans and maps them to Amplitude `[Agent]` events.

**Step 1: Add the bridge**

```bash
pip install "amplitude-ai[otel]"
```

```python
from amplitude import Amplitude
from amplitude_ai import AmplitudeAI
from opentelemetry import trace
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from amplitude_ai.integrations.opentelemetry import AmplitudeAgentExporter

amplitude = Amplitude("YOUR_API_KEY")
ai = AmplitudeAI(amplitude=amplitude)

# Add alongside your existing TracerProvider (e.g. Langfuse, OpenLIT, etc.)
trace.get_tracer_provider().add_span_processor(
    SimpleSpanProcessor(AmplitudeAgentExporter(amplitude=amplitude, user_id="user-123"))
)
# All GenAI spans now flow to Amplitude as [Agent] events -- zero changes to your
# existing instrumentation. Your OTEL tool keeps working exactly as before.
```

**Step 2: Add session context**

Wrap your code in `agent.session()` and the OTEL bridge automatically inherits session/agent context via the same `ContextVar` mechanism:

```python
agent = ai.agent("support-bot", env="production")

with agent.session(user_id="user-123") as s:
    s.new_trace()
    s.track_user_message(content="What is retention?")

    # Any OTEL-instrumented GenAI calls inside this block automatically get
    # session_id, trace_id, turn_id, and agent_id in Amplitude
    result = my_instrumented_function(...)  # Langfuse @observe, OpenLIT, etc.

    s.score(name="helpful", value=1.0, target_id="...")
# Session auto-ends, server-side enrichment kicks in
```

**What unlocks at each step:**

| Step | Events | Key fields | Not available from OTEL |
|---|---|---|---|
| **Add bridge** | `[Agent] User Message`, `[Agent] AI Response`, `[Agent] Embedding`, `[Agent] Tool Call` | model, provider, tokens (input/output/total), cache tokens (read/creation), cost (cache-aware), latency, system prompt, temperature, top_p, max_output_tokens, content (if opted-in), errors | Reasoning content/tokens, TTFB, streaming detection, implicit feedback, file attachments, event graph linking (parent_message_id) |
| **+ session context** | + `[Agent] Score` | + session_id, trace_id, turn_id, agent_id, description, agent_version, env, abandonment_turn | Same field gaps, but now: session grouping, scoring, abandonment analysis, server-side enrichments. Compare agent versions. Build funnels from product events through AI sessions. |
| **+ selective native wrappers** | Same events, richer fields on wrapped providers | + reasoning content, TTFB, streaming, implicit feedback (is_regeneration, is_edit, was_copied), file attachments for those providers | Gaps closed progressively per provider you wrap. See [Provider Wrappers](#provider-wrappers). |

The third row is the natural upgrade path: start with the OTEL bridge for everything, then selectively wrap your most important provider calls for full field coverage. The bridge and native wrappers coexist; you don't have to choose one or the other.

**Next:** [OTEL Bridge details](#otel-genai-bridge-reference) | [Scoring](#scoring) | [Privacy](#privacy-content-control) | [Provider Wrappers](#provider-wrappers)

### User Identity

User identity flows through the **session** or **per-call**, not at agent creation or patch time. This keeps the agent reusable across users.

**Via sessions** (recommended): pass `user_id` when opening a session:
```python
agent = ai.agent("support-bot", env="production")
with agent.session(user_id="user-42") as s:
    s.new_trace()
    s.track_user_message(content="Hello")
    response = client.chat.completions.create(model="gpt-4o", messages=[...])
```

**Per-call**: pass `amplitude_user_id` on each LLM call (useful with `patch()` or when not using sessions):
```python
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[...],
    amplitude_user_id="user-42",
)
```

**Via middleware**: `AmplitudeAIMiddleware` extracts user identity from the request (see [FastAPI / Starlette Middleware](#fastapi-starlette-middleware)).

### Initialization Options

```python
from amplitude import Amplitude

# Recommended -- share your existing Amplitude pipeline
amplitude = Amplitude("YOUR_API_KEY")
ai = AmplitudeAI(amplitude=amplitude)

# Standalone (creates an Amplitude instance internally)
ai = AmplitudeAI(api_key="YOUR_API_KEY")

# EU Data Residency
amplitude = Amplitude("YOUR_API_KEY")
amplitude.configuration.server_zone = "EU"
ai = AmplitudeAI(amplitude=amplitude)
```

---

## Core Concepts

The SDK organizes AI interactions into **sessions**, **traces**, **turns**, and **spans**:

```
What you instrument:
SESSION
  Trace                            ← one per user message (new_trace())
    [Agent] User Message             automatic from provider wrapper
    [Agent] Tool Call                automatic or track_tool_call()
    [Agent] AI Response              automatic from provider wrapper
    [Agent] Score                    s.score() — rate this response
  ...repeat per conversation turn...
  [Agent] Session Enrichment         track_session_enrichment()
  [Agent] Score                      s.score() — rate the session
  [Agent] Session End                track_session_end()

What Amplitude adds automatically (content_mode="full" only):
  [Agent] Session Evaluation         outcome, flags, behavioral patterns
  [Agent] Topic Classification       one per topic model you define
  [Agent] Score (ai)                 one per rubric you define
```

Scores can attach at **message level** (rate a specific response) or **session level** (rate the whole conversation). Enrichments attach at **session level** only.

| Concept | Property | Description |
|---------|----------|-------------|
| **Session** | `session_id` | A conversation between a user and the AI. All events in one conversation share the same `session_id`. |
| **Trace** | `trace_id` | One user-message-to-AI-response cycle. Generate a new `trace_id` (UUID) each time the user sends a message. All events in that cycle (the user message, any tool calls, and the AI response) share the same `trace_id`. Use `new_trace()` or pass a UUID directly. |
| **Turn** | `turn_id` | Monotonically increasing counter for event ordering. The SDK auto-increments per session when omitted. For custom ordering (e.g., per-trace numbering), pass explicit values. |
| **Span** | `span_id` | A tracked operation: tool call, embedding, vector search, or custom step. |
| **Agent** | `agent_id` | Which agent handled the interaction (for multi-agent systems). |

#### What is a BoundAgent?

A **`BoundAgent`** is a pre-configured handle that carries context (`agent_id`, `user_id`, `session_id`, `env`, `description`, `agent_version`, etc.) so you don't repeat these fields on every tracking call. You create one via `ai.agent(...)`:

```python
agent = ai.agent("support-bot", env="production", description="Handles support queries")
```

Use `agent.session(user_id=..., session_id=...)` for request-scoped sessions (the `with` block auto-manages lifecycle), or call `agent.track_*()` methods directly for long-lived conversations that span multiple requests.

Key capabilities:
- **`agent.session(...)`** — creates a `Session` context manager for request-scoped conversations
- **`agent.child("sub-agent")`** — creates a child agent that inherits context and auto-sets `parent_agent_id`
- **`agent.track_user_message(...)`, `agent.track_ai_message(...)`, etc.** — tracking methods with all context pre-filled
- **`agent.track_session_end()`** — explicit session close for long-lived conversations

### Events at a Glance

The SDK produces 8 event types. When `content_mode="full"`, Amplitude's server adds 3 more per session automatically.

| Event | What it captures |
|-------|-----------------|
| `[Agent] User Message` | User's input, attachments (file uploads), regeneration/edit signals |
| `[Agent] AI Response` | Model output, tokens, cost, latency, reasoning, system prompt, model config, copy signal |
| `[Agent] Tool Call` | Function/tool invocation by the AI |
| `[Agent] Embedding` | Vector embedding operation |
| `[Agent] Span` | Any pipeline step (search, rerank, guardrails) |
| `[Agent] Session End` | Explicit session close, abandonment tracking |
| `[Agent] Session Enrichment` | Your structured labels (topics, rubrics, outcomes) |
| `[Agent] Score` | Quality signal on a message or session (user, automated, or annotator) |
| *Server-side (automatic when content_mode="full"):* | |
| `[Agent] Session Evaluation` | Session summary with outcome and behavioral flags |
| `[Agent] Topic Classification` | Category label per configured topic model |
| `[Agent] Score` (automated) | Rubric score per configured rubric |

See [Event Schema](#event-schema) for the full property reference. Most tracking methods return a unique ID (`message_id`, `invocation_id`, or `span_id`) that you can use to link related events into a graph. Session lifecycle methods (`track_session_end`, `track_session_enrichment`) and `score` return `None`. See [Event Linking](#event-linking) for the full table and code examples.

### What You Actually Get

Every SDK tracking call produces a standard Amplitude event. Here's what they look like in practice. These are the events you query in charts, cohorts, and funnels. Notice that `user_id` is the same one your product events use.

**A `[Agent] AI Response` event** (SDK-emitted, immediate):

```json
{
  "event_type": "[Agent] AI Response",
  "user_id": "user-42",
  "event_properties": {
    "[Agent] Session ID": "sess-abc-123",
    "[Agent] Trace ID": "trace-7f3a",
    "[Agent] Turn ID": 2,
    "[Agent] Message ID": "msg-9e2f-4a1b",
    "[Agent] Model Name": "claude-sonnet-4-20250514",
    "[Agent] Provider": "anthropic",
    "[Agent] Latency Ms": 1240.5,
    "[Agent] TTFB Ms": 89.3,
    "[Agent] Input Tokens": 4850,
    "[Agent] Output Tokens": 312,
    "[Agent] Cache Read Tokens": 4200,
    "[Agent] Cost USD": 0.0019,
    "[Agent] Finish Reason": "end_turn",
    "[Agent] Is Streaming": true,
    "[Agent] Temperature": 0.7,
    "[Agent] Agent ID": "support-bot",
    "[Agent] Agent Version": "v4.2",
    "[Agent] Env": "production",
    "[Agent] Context": "{\"experiment_variant\": \"prompt-v2\", \"surface\": \"chat\"}",
    "[Agent] Was Copied": true,
    "[Agent] Is Error": false,
    "[Agent] Component Type": "llm",
    "[Agent] SDK Version": "1.0.2",
    "[Agent] Runtime": "python"
  }
}
```

**A `[Agent] Session Evaluation` event** (server-generated after session closes, when `content_mode="full"`):

```json
{
  "event_type": "[Agent] Session Evaluation",
  "user_id": "user-42",
  "event_properties": {
    "[Agent] Session ID": "sess-abc-123",
    "[Agent] Overall Outcome": "response_provided",
    "[Agent] Turn Count": 4,
    "[Agent] Has Task Failure": false,
    "[Agent] Has Negative Feedback": false,
    "[Agent] Has Technical Failure": false,
    "[Agent] Behavioral Patterns": ["multi_turn_refinement"],
    "[Agent] Agent Chain Depth": 1,
    "[Agent] Models Used": ["claude-sonnet-4-20250514"],
    "[Agent] Session Cost USD": 0.0087,
    "[Agent] Evaluation Source": "ai",
    "[Agent] Taxonomy Version": "2.0"
  }
}
```

The first event is queryable immediately. The second appears within minutes of session close. Both carry the same `user_id` and `session_id`. Build a cohort from Session Evaluation properties (e.g., `Has Task Failure = true`) and measure that cohort's 7-day retention using your existing product events.

### Data Flow

The SDK uses **composition**: it wraps an `Amplitude` instance rather than subclassing it. When `amplitude` is passed in, the SDK shares your existing event pipeline with no duplicate queues. It never opens its own network connections:

```
Your Code                        amplitude-ai SDK                  Amplitude
─────────────────────────────────────────────────────────────────────────────
                                 ┌─────────────────┐
ai.track_ai_message(...)  ────→  │   AmplitudeAI   │
                                 │  - Apply privacy │
                                 │  - Build event   │
                                 └────────┬─────────┘
                                          ▼
                                 ┌─────────────────┐
                                 │    Amplitude     │  ────→  Amplitude API
                                 │  (your instance) │         (US or EU)
                                 └─────────────────┘
```

Privacy controls (content mode, PII redaction) are applied _before_ events leave your process. Content is never sent unfiltered and then redacted server-side.

---

## Integration Patterns

The SDK supports three integration patterns. Pick the one that matches your architecture.

### Pattern A: Single-Request Handler

**Use when:** Your session starts and ends in a single code path (Lambda functions, synchronous API endpoints, CLI tools).

```python
agent = ai.agent("support-bot", env="production")

with agent.session(user_id="user-1") as s:
    s.new_trace()
    s.track_user_message(content="What is retention?")
    ai_msg = s.track_ai_message(
        content="Retention measures...",
        model="gpt-4o",
        provider="openai",
        latency_ms=350.0,
        input_tokens=50,
        output_tokens=200,
    )
    s.score(name="helpful", value=1.0, target_id=ai_msg)
# Session auto-ends here -- track_session_end() called on __exit__
# Server-side enrichment kicks in (when content_mode="full")
```

The `Session` context manager auto-generates a `session_id`, handles `track_session_end()` on exit (even on exception), and publishes session context into Python's `contextvars` so provider wrappers and the OTEL bridge inherit `session_id`, `trace_id`, `agent_id`, and `turn_id` automatically.

### Pattern B: Long-Lived Conversation

**Use when:** Your session spans multiple HTTP requests, WebSocket messages, or Slack interactions. This is the most common real-world pattern for chatbots and conversational agents.

Use `BoundAgent` directly, NOT the `Session` context manager, because the session outlives any single code path.

```python
agent = ai.agent(
    "support-bot",
    user_id="user-1",
    env="production",
    session_id="thread-abc",
)

# --- Request 1: user sends a message ---
agent.track_user_message(content="What is retention?", trace_id="req-1", turn_id=1)
ai_msg = agent.track_ai_message(
    content="Retention measures...",
    model="gpt-4o",
    provider="openai",
    latency_ms=350.0,
    trace_id="req-1",
    turn_id=2,
)

# --- Request 2: user follows up ---
agent.track_user_message(content="Show me an example", trace_id="req-2", turn_id=1)
agent.track_ai_message(
    content="Here's a retention chart...",
    model="gpt-4o",
    provider="openai",
    latency_ms=200.0,
    trace_id="req-2",
    turn_id=2,
)

# --- Optional: trigger enrichment immediately when conversation ends ---
agent.track_session_end()
# Without this call, Amplitude auto-closes the session after 30 min of
# inactivity and runs enrichment at that point.
```

Key differences from Pattern A:
- `BoundAgent` carries all context (`user_id`, `agent_id`, `session_id`, `env`, etc.) without a `with` block
- Generate a new `trace_id` for each user-message-to-AI-response cycle
- Pass explicit `turn_id` values (or omit to auto-increment per session)
- `track_session_end()` is **optional** — it triggers enrichment immediately. Without it, sessions auto-close after 30 minutes of inactivity and enrichment runs then

### Pattern C: Multi-Agent Orchestrator

**Use when:** Multiple agents collaborate on a task. `agent.child()` creates a sub-agent that inherits session context and auto-sets `parent_agent_id`. Use `session.run_as()` to automatically propagate the child agent's identity to **both** manual tracking calls and provider wrappers:

```python
orchestrator = ai.agent("orchestrator", env="production")
researcher = orchestrator.child("researcher")
writer = orchestrator.child("writer")
# researcher.parent_agent_id == "orchestrator" (automatic)

with orchestrator.session(user_id="u1") as s:
    s.track_user_message(content="Compare our pricing to competitors")

    # Research phase — provider calls automatically tagged with agent_id='researcher'
    with s.run_as(researcher) as rs:
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": "Research competitor pricing"}],
        )
        rs.track_tool_call(tool_name="web_search", latency_ms=500, success=True)

    # Writing phase — provider calls automatically tagged with agent_id='writer'
    with s.run_as(writer) as ws:
        draft = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": f"Write summary: {response}"}],
        )

    # Fan in: orchestrator synthesizes results
    s.track_ai_message(
        content="Based on research...",
        model="gpt-4o",
        provider="openai",
        latency_ms=500,
    )

# Events emitted:
#   [Agent] User Message  → agent_id='orchestrator'
#   [Agent] AI Response    → agent_id='researcher',  parent_agent_id='orchestrator'
#   [Agent] Tool Call      → agent_id='researcher',  parent_agent_id='orchestrator'
#   [Agent] AI Response    → agent_id='writer',      parent_agent_id='orchestrator'
#   [Agent] AI Response    → agent_id='orchestrator'
#   [Agent] Session End    → agent_id='orchestrator'  (one session end, not per-child)
```

**How `run_as` works:**

- Shares the parent session's `session_id`, `trace_id`, and turn counter
- Overrides `agent_id` and `parent_agent_id` in `contextvars` for the block's duration
- Provider wrappers automatically read the child's identity — no explicit overrides needed
- Does **not** emit `[Agent] Session End` (the child operates within the parent session)
- Restores the parent context when the block exits, even on exceptions
- Supports nesting: `with s.run_as(child) as cs: with cs.run_as(grandchild) as gs: ...`
- Async variant: `async with s.arun_as(child) as cs: ...`

See [Multi-Agent Patterns](#multi-agent-patterns) for more examples (linear chains, fan-out/fan-in, dynamic routing).

### Which API Should I Use?

```
Want automatic LLM call tracking?
  YES --> Provider Wrappers (OpenAI, Anthropic, etc.)
          + BoundAgent for session/agent context
  NO  --> Manual track_*() calls

Want to track a Python function as a tool call automatically?
  YES --> @tool decorator (zero boilerplate)
  NO  --> track_tool_call() manually

Want to track a function as a span (pipeline step, retriever, etc.)?
  YES --> @observe decorator (auto session lifecycle)
  NO  --> track_span() manually

Session contained in one code path (Lambda, sync handler)?
  YES --> agent.session() context manager  (Pattern A)
  NO  --> BoundAgent directly + explicit track_session_end()  (Pattern B)

Multiple agents collaborating?
  YES --> agent.child() for sub-agents  (Pattern C)
  NO  --> Single BoundAgent is sufficient
```

---

## Going Deeper

### Privacy & Content Control

Three tiers control who does the enrichment and what data leaves your environment:

| Mode | What you send | Who enriches | Best for |
|------|--------------|-------------|----------|
| `full` | Content + metrics | **Amplitude** automatically classifies every session: topic models, quality rubrics, behavioral flags, outcomes | Maximum insight, zero eval code, works out of the box |
| `metadata_only` | Metrics only (no content) | Nobody | Strict environments where no conversation text can leave your infrastructure |
| `customer_enriched` | Your labels + metrics | **You** run your own classifiers, send structured labels via `track_session_enrichment()` | Teams in regulated industries who want full analytics value and full data control |

> **This is a control gradient, not a quality gradient.** `customer_enriched` gives the same analytics output as `full`. The difference is who runs the enrichment. In `full` mode, Amplitude does it for you. In `customer_enriched` mode, you do it yourself and send structured labels. The result in your charts, cohorts, and funnels is the same.

For teams in regulated industries or with strict data residency requirements, `customer_enriched` is the recommended path: you get full analytics value without sending any conversation content to Amplitude.

The table below shows what analytics patterns each tier enables:

| Analytics pattern | `full` | `metadata_only` | `customer_enriched` |
|---|---|---|---|
| Cohort by topic | Yes | No | Yes (your labels) |
| Cohort by task failure | Yes | No | No |
| Cohort by quality score | Yes | No | Yes (your scores) |
| Retention by AI engagement | Yes | Yes | Yes |
| Behavioral pattern detection (retry_storm, etc.) | Yes | No | No |
| Cost analytics | Yes | Yes | Yes |

In `full` mode, message content is stored at full length with no truncation or size limits. See [Content Storage](#content-storage-llm_message) for details.

```python
from amplitude_ai import AmplitudeAI, AIConfig, ContentMode

# Full (default) -- raw content, server enrichments enabled
ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(content_mode=ContentMode.FULL))

# Metadata only -- no content at all
ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(content_mode=ContentMode.METADATA_ONLY))

# Customer enriched -- you provide your own classifications
ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(content_mode=ContentMode.CUSTOMER_ENRICHED))

# PII redaction (works with any mode -- strips emails, phone numbers, credit cards, SSNs)
ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(redact_pii=True))
```

**`AIConfig` options (complete surface):**

| Name | Type | Default | Description |
|------|------|---------|-------------|
| `content_mode` | `ContentMode` | `FULL` | Privacy tier. See above. |
| `redact_pii` | `bool` | `True` | Scrub emails, phone numbers, credit cards, SSNs before sending. Set to `False` if you explicitly want raw content in tracked events. |
| `custom_redaction_patterns` | `list[str]` | `[]` | Additional regex patterns to redact when `redact_pii=True`. |
| `debug` | `bool` | `False` | Print colored one-line event summaries to stderr. See [Developer Experience](#developer-experience). |
| `dry_run` | `bool` | `False` | Validate and print events without sending to Amplitude. |
| `validate` | `bool` | `False` | Raise `ValidationError` on bad inputs instead of silently continuing. |
| `on_event_callback` | `Callable` | `None` | Per-event delivery callback: `(event, status_code, message) -> None`. |

**OTEL bridge privacy (two-gate model):** If you're using the [OTEL GenAI Bridge](#otel-genai-bridge-reference), the OTEL GenAI spec marks message content as Opt-In. The SDK's `content_mode` setting (via `AIConfig`) acts as a second gate, so you control exactly what reaches Amplitude regardless of what your OTEL source captures:

| Customer intent | OTEL tool setting | Amplitude `content_mode` | Result |
|---|---|---|---|
| Maximum insight | Content capture ON | `full` | Content + server enrichments in Amplitude |
| No conversation text in Amplitude | Content capture ON | `metadata_only` | Amplitude receives model, tokens, cost, latency (no message text) |
| No content anywhere | Content capture OFF | any | No content in the span to begin with |
| Own classifications | Content capture OFF | `customer_enriched` + `track_session_enrichment()` | Your structured labels in Amplitude, no raw content |

---

### Bound Agents & Sessions

**Bound Agents.** `ai.agent()` creates a pre-configured handle that carries context fields so you never repeat them:

```python
agent = ai.agent(
    "support-bot",
    description="Handles customer support queries via OpenAI GPT-4o",
    user_id="user-1",
    agent_version="v4.2",
    env="production",
    context={
        "experiment_variant": "prompt-v2-treatment",
        "prompt_revision": "abc123",
    },
)

# Every call inherits user_id, agent_id, description, agent_version, env, context
msg = agent.track_user_message(content="How do I set up a funnel?", session_id="s1")
ai_msg = agent.track_ai_message(
    content="To create a funnel...",
    session_id="s1",
    model="gpt-4o",
    provider="openai",
    latency_ms=450.0,
    input_tokens=120,
    output_tokens=340,
)
agent.score(name="user-feedback", value=1.0, target_id=ai_msg)
```

Explicit kwargs always override bound defaults:

```python
# Uses "override-agent" for this call only, not "support-bot"
agent.track_ai_message(agent_id="override-agent", ...)
```

**Child Agents.** For multi-agent orchestration, `child()` creates a new handle that inherits `env`, `session_id`, `trace_id`, and `groups` from the parent. It automatically sets `parent_agent_id`:

```python
orchestrator = ai.agent("orchestrator", env="production")

researcher = orchestrator.child("researcher")
# researcher.agent_id = "researcher"
# researcher.parent_agent_id = "orchestrator"  (automatic)
# researcher.env = "production"                 (inherited)

executor = researcher.child("executor")
# executor.parent_agent_id = "researcher"       (chains correctly)
```

**Session lifecycle and enrichment.** You do **not** need to call `track_session_end()` for sessions to work. Amplitude's server automatically closes sessions after 30 minutes of inactivity and queues them for enrichment (topic classification, quality scoring, session evaluation) at that point. The only reason to call `track_session_end()` is to **trigger enrichment sooner** — for example, if you know the conversation is over and want evaluation results immediately rather than waiting for the idle timeout.

"Closed" is a server-side concept meaning "queued for enrichment" — it does **not** prevent new events from flowing into the same session. If the user resumes a conversation after session end, new messages with the same `session_id` are still associated with that session. The SDK has no local "closed" state.

If you use a `Session` context manager (`with agent.session(...) as s:`), `track_session_end()` is called automatically when the `with` block exits. For long-lived conversations (chatbots, support agents), you can skip explicit session end entirely and let the server handle it.

**Session Context Manager.** `agent.session()` returns a context manager that auto-calls `track_session_end` when the block exits (even on exception):

```python
agent = ai.agent("support-bot", env="prod")

with agent.session(user_id="u1") as s:
    s.new_trace()                          # auto-generate trace_id (UUID)
    msg = s.track_user_message(content="How do I set up a funnel?")
    ai_msg = s.track_ai_message(
        content="To create a funnel...",
        model="gpt-4o",
        provider="openai",
        latency_ms=450.0,
    )
    s.score(name="user-feedback", value=1.0, target_id=ai_msg)
# session auto-ended here -- track_session_end("sess-1") called automatically
```

Switch traces mid-session, set enrichments, auto-generate session IDs:

```python
with agent.session() as s:                     # auto-generated UUID session_id
    t1 = s.new_trace()
    s.track_user_message(content="First question")
    s.track_ai_message(content="Answer 1", model="gpt-4o", provider="openai", latency_ms=200)

    t2 = s.new_trace()                         # new trace for a follow-up
    s.track_user_message(content="Follow-up question")
    s.track_ai_message(content="Answer 2", model="gpt-4o", provider="openai", latency_ms=150)

    s.set_enrichments(SessionEnrichments(overall_outcome="response_provided"))
# session auto-ended with enrichments
```

Works with async too:

```python
async with agent.session("sess-1") as s:
    s.new_trace()
    ...
```

How it works: `Session.__enter__()` publishes a `ContextVar` with the active session/agent context. Provider wrappers and the OTEL bridge read this `ContextVar` and auto-fill any missing fields. `Session.__exit__()` restores the previous context. This is the same pattern used by OpenTelemetry and works correctly with threads and `asyncio`.

---

### Context Dict Conventions

The `context` parameter on `ai.agent()` accepts an arbitrary `dict[str, Any]` that is JSON-serialized and attached to every event as `[Agent] Context`. This is the recommended way to add segmentation dimensions without requiring new global properties.

**Recommended keys:**

| Key | Example Values | Use Case |
|-----|---------------|----------|
| `agent_type` | `"planner"`, `"executor"`, `"retriever"`, `"router"`, `"evaluator"` | Filter/group analytics by agent role in multi-agent systems. Build charts like "latency by agent type" or "error rate by agent role." |
| `experiment_variant` | `"control"`, `"treatment-v2"`, `"prompt-rewrite-a"` | Segment AI sessions by A/B test variant. Compare quality scores, abandonment rates, or cost across experiment arms. See note below. |
| `feature_flag` | `"new-rag-pipeline"`, `"reasoning-model-enabled"` | Track which feature flags were active during the session. Correlate flag states with quality regressions. |
| `surface` | `"chat"`, `"search"`, `"copilot"`, `"email-draft"` | Identify which UI surface or product area triggered the AI interaction. Build per-surface quality dashboards. |
| `prompt_revision` | `"v7"`, `"abc123"`, `"2026-02-15"` | Track which prompt version was used. Detect prompt regression when combined with `agent_version`. |
| `deployment_region` | `"us-east-1"`, `"eu-west-1"` | Segment by deployment region for latency analysis or compliance tracking. |
| `canary_group` | `"canary"`, `"stable"` | Identify canary vs. stable deployments for progressive rollout monitoring. |

**Example:**

```python
agent = ai.agent(
    "support-bot",
    user_id="u1",
    agent_version="4.2.0",
    context={
        "agent_type": "executor",
        "experiment_variant": "reasoning-enabled",
        "surface": "chat",
        "feature_flag": "new-rag-pipeline",
    },
)

# All events from this agent (and its sessions, child agents, and provider
# wrappers) will include [Agent] Context with these keys.
```

**Context merging in child agents:**

```python
parent = ai.agent("orchestrator", context={"experiment_variant": "treatment", "surface": "chat"})
child = parent.child("researcher", context={"agent_type": "retriever"})
# child.context == {"experiment_variant": "treatment", "surface": "chat", "agent_type": "retriever"}
# Child keys override parent keys; parent keys absent from the child are preserved.
```

**Querying in Amplitude:** The `[Agent] Context` property is a JSON string. Use Amplitude's JSON property parsing to extract individual keys for charts, cohorts, and funnels. For example, group by `[Agent] Context.agent_type` to see metrics by agent role.

> **Note on `experiment_variant` and server-generated events:** Context keys appear on all SDK-emitted events (`[Agent] User Message`, `[Agent] AI Response`, etc.). Server-generated events (`[Agent] Session Evaluation`, `[Agent] Score` with `source="ai"`) do not yet inherit context keys. To segment server-generated quality scores by experiment arm, use Amplitude Derived Properties to extract from `[Agent] Context` on SDK events. First-class support is planned.

> **Why a dict instead of first-class fields?** Context is a dict for flexibility without schema migrations. Adding a new segmentation dimension takes one line of code, not a data catalog update. First-class properties exist for universal, stable dimensions (`agent_id`, `description`, `agent_version`, `env`). The context dict exists for customer-specific, evolving dimensions (`experiment_variant`, `feature_flags`, `prompt_revision`). Adding dedicated event properties for each dimension would consume global property slots, which are limited per organization. If usage patterns converge and the Amplitude product builds dedicated chart support for specific keys, they can be promoted to first-class fields later.

---

### Multi-Agent Patterns

The SDK supports multi-agent orchestration via `BoundAgent.child()` and `parent_agent_id`. Here are common patterns:

**Pattern 1: Linear delegation chain**

A simple pipeline where each agent hands off to the next:

```python
orchestrator = ai.agent("orchestrator", env="production")

with orchestrator.session(user_id="u1") as s:
    # Orchestrator decides to delegate to researcher
    researcher = orchestrator.child("researcher")
    with researcher.session(session_id=s.session_id) as rs:
        rs.track_user_message(content="Find pricing info")
        rs.track_ai_message(content="Found 3 articles...", model="gpt-4o",
                            provider="openai", latency_ms=200)

    # Researcher done, orchestrator delegates to writer
    writer = orchestrator.child("writer")
    with writer.session(session_id=s.session_id) as ws:
        ws.track_ai_message(content="Here is a summary...", model="gpt-4o",
                            provider="openai", latency_ms=300)

# Events show: orchestrator -> researcher -> writer
# Each agent's events carry its own agent_id and parent_agent_id
```

**Pattern 2: Fan-out / fan-in**

An orchestrator dispatches multiple sub-agents in parallel:

```python
orchestrator = ai.agent("orchestrator", context={"agent_type": "router"})

with orchestrator.session(user_id="u1") as s:
    s.track_user_message(content="Compare our pricing to competitors")

    # Fan out to parallel agents
    researcher_a = orchestrator.child("researcher-web", context={"agent_type": "retriever"})
    researcher_b = orchestrator.child("researcher-db", context={"agent_type": "retriever"})

    # Both share the same session and parent_agent_id="orchestrator"
    # Run in parallel (via asyncio, threads, etc.)
    # ...

    # Fan in: orchestrator synthesizes results
    s.track_ai_message(content="Based on research...", model="gpt-4o",
                       provider="openai", latency_ms=500)
```

**Pattern 3: Dynamic routing**

A router agent selects from a pool of specialist agents at runtime:

```python
router = ai.agent("router", context={"agent_type": "router"})

with router.session(user_id="u1") as s:
    user_msg = s.track_user_message(content="I need a refund")

    # Router decides based on intent
    intent = classify_intent(user_msg)
    specialist = router.child(f"specialist-{intent}", context={"agent_type": "executor"})

    with specialist.session(session_id=s.session_id) as ss:
        ss.track_ai_message(content="I can help with your refund...",
                            model="gpt-4o", provider="openai", latency_ms=400)
```

**Analytics this enables:**

- **Per-agent quality scores:** Filter `[Agent] Score` by `[Agent] Agent ID` to see which agents produce high-quality responses and which don't, across user feedback, automated evals, and server-generated rubric scores.
- **Cost attribution:** Group cost by `[Agent] Agent ID` to see which sub-agent is expensive relative to its quality contribution. Find the agent that accounts for 60% of token spend but only 20% of task completions.
- **Failure attribution:** When a multi-agent chain produces a bad outcome, per-agent quality scores help identify which agent introduced the failure. Filter `[Agent] Session Evaluation` sessions where `has_task_failure=True`, then drill into individual agent scores.
- **Handoff analysis:** Build funnels across agent boundaries using `[Agent] Parent Agent ID`: "orchestrator dispatches → researcher completes → writer delivers." Measure conversion and drop-off at each handoff.
- **Role-based dashboards:** Use `[Agent] Context.agent_type` (see [Context Dict Conventions](#context-dict-conventions)) to compare latency, error rate, and cost across agent roles (router, retriever, executor).

---

### Event Linking

Message, tool, and span tracking calls return a unique ID. Use these IDs to wire events into a graph:

| Method | Returns | ID Name |
|--------|---------|---------|
| `track_user_message()` | `str` | `message_id` |
| `track_ai_message()` | `str` | `message_id` |
| `track_tool_call()` | `str` | `invocation_id` |
| `track_embedding()` | `str` | `span_id` |
| `track_span()` | `str` | `span_id` |
| `track_session_end()` | `None` | — |
| `track_session_enrichment()` | `None` | — |
| `score()` | `None` | — |

Link events together:

```python
agent = ai.agent("support-bot", env="prod")

with agent.session(user_id="u1") as s:
    s.new_trace()

    # 1. User asks a question
    msg = s.track_user_message(content="Explain funnels")

    # 2. AI decides to call a tool -- link to the user message
    tool_inv = s.track_tool_call(
        tool_name="search_docs",
        latency_ms=85.0,
        success=True,
        parent_message_id=msg,              # ← links tool call to the user message
    )

    # 3. AI responds
    ai_msg = s.track_ai_message(
        content="A funnel measures conversion...",
        model="gpt-4o",
        provider="openai",
        latency_ms=450.0,
    )

    # 4. Score the AI response
    s.score(name="user-feedback", value=1.0, target_id=ai_msg)  # ← links score to AI response

    # 5. Nested spans for pipeline operations
    parent_span = s.track_span(span_name="rag_pipeline", latency_ms=200.0)
    child_span = s.track_span(
        span_name="vector_search",
        latency_ms=50.0,
        parent_span_id=parent_span,         # ← links span to parent
    )
```

---

### Scoring

Attach quality signals to any message or session. Covers user feedback, AI evals, and human reviews. Use `source` to distinguish origin (`"user"`, `"ai"`, `"reviewer"`).

```python
# User feedback (thumbs up/down on a specific response)
ai.score(user_id="user-1", name="user-feedback", value=1.0,
         target_id=ai_msg_id, target_type="message", source="user")

# Automated evaluation (LLM-as-judge)
ai.score(user_id="user-1", name="accuracy", value=0.92,
         target_id=ai_msg_id, source="ai", comment="Matches ground truth")

# Human review (internal review queue, RLHF labeling)
ai.score(user_id="reviewer-1", name="groundedness", value=0.8,
         target_id=ai_msg_id, source="reviewer", comment="Minor hallucination in step 3")

# Session-level rating
ai.score(user_id="user-1", name="csat", value=4.0,
         target_id="sess-1", target_type="session", source="user")
```

Common scoring patterns:

| Use Case | Example |
|----------|---------|
| User thumbs up/down | `score(name="user-feedback", value=1, target_type="message", source="user")` |
| Star rating (1-5) | `score(name="user-rating", value=4, target_type="message", source="user")` |
| LLM-as-judge eval | `score(name="accuracy", value=0.92, target_type="message", source="ai")` |
| Human reviewer | `score(name="quality", value=0.8, target_type="message", source="reviewer")` |
| Session-level CSAT | `score(name="csat", value=4, target_type="session", source="user")` |
| Server rubric score | Emitted automatically by enrichment pipeline with `source="ai"` for each configured rubric |

Each `score()` produces a `[Agent] Score` event. The server enrichment pipeline also emits `[Agent] Score` events with `source="ai"` for each configured rubric. User feedback, AI evals, and server-generated rubric scores all share the same event type, enabling unified queries across all quality signals in a single chart.

> **All quality signals in one event type.** User feedback (`source="user"`), human reviewer annotations (`source="reviewer"`), and automated rubric scores from the enrichment pipeline (`source="ai"`) all produce `[Agent] Score` events. A single chart shows all three side by side. No joins, no separate tables. Filter by `[Agent] Evaluation Source` to compare signal types. Filter by `[Agent] Agent ID` for per-agent quality attribution.

---

### Labeling and Tagging Messages

Attach custom key-value labels to any message event for filtering and segmentation in Amplitude. Labels are flexible; use whatever keys make sense for your product.

**Common use cases:**

- **Routing tags**: `flow`, `surface`, `experiment_variant`. Segment by where the message originated.
- **Classifier output**: `intent`, `sentiment`, `toxicity`. Attach ML classifier results with confidence scores.
- **Business context**: `tier`, `plan`, `feature_area`. Slice by customer attributes.

#### Inline Labels (at tracking time)

Pass `labels` when you already know the tags at tracking time:

```python
from amplitude_ai import MessageLabel

# Custom tags -- no confidence needed
msg_id = ai.track_user_message(
    user_id="user-1",
    content="How do I create a funnel?",
    session_id="sess-1",
    labels=[
        MessageLabel(key="flow", value="onboarding"),
        MessageLabel(key="surface", value="chat_widget"),
        MessageLabel(key="experiment", value="new_prompt_v2"),
    ],
)

# Classifier output -- include confidence scores
ai_msg_id = ai.track_ai_message(
    user_id="user-1",
    content="To create a funnel, go to...",
    session_id="sess-1",
    model="gpt-4o",
    provider="openai",
    latency_ms=300.0,
    labels=[
        MessageLabel(key="intent", value="how_to", confidence=0.94),
        MessageLabel(key="sentiment", value="neutral", confidence=0.88),
    ],
)
```

Labels are emitted as `[Agent] Message Labels` on the event. In Amplitude, filter or group by label key/value to build charts like "messages by intent" or "sessions where flow=onboarding".

#### Retrospective Labels (after the session)

When classifier results arrive after the session ends (e.g., from a background pipeline), attach them via `SessionEnrichments.message_labels`, keyed by the `message_id` returned from tracking calls:

```python
from amplitude_ai import SessionEnrichments, MessageLabel

enrichments = SessionEnrichments(
    message_labels={
        msg_id: [
            MessageLabel(key="intent", value="how_to", confidence=0.94),
        ],
        ai_msg_id: [
            MessageLabel(key="quality", value="good", confidence=0.91),
        ],
    },
)
ai.track_session_enrichment(user_id="user-1", session_id="sess-1", enrichments=enrichments)
```

---

### Enrichments

Session enrichments attach structured classifications to a completed session: topic categories, rubric scores, outcome labels, and behavioral flags. They work differently depending on your privacy configuration:

**When `content_mode` is `"full"`**, Amplitude's enrichment pipeline runs automatically on every session after it closes. You get topic classifications, rubric scores, behavioral flags, and session outcomes without writing or maintaining any eval code. The pipeline classifies sessions across configurable dimensions:

| Category | Description | Configurable |
|----------|-------------|--------------|
| **Quality Scores** | Task completion, response quality, user satisfaction, agent confusion (0-1 scores with rationales) | Rubrics customizable per org |
| **Safety** | Toxicity detection, prompt injection detection, content policy violations | Custom policies per org |
| **Emotions** | User emotion classification with trajectory tracking | Custom emotion taxonomy per org |
| **Dialog Acts** | Conversation patterns: complaints, requests, apologies, completions | Default taxonomy provided |
| **Behavioral Patterns** | Anti-patterns: retry storms, clarification loops, early abandonment | Fixed taxonomy |

Three event types are produced per session:

| Event | What It Contains | Cardinality |
|-------|-----------------|-------------|
| `[Agent] Session Evaluation` | Session summary: outcome, turn count, boolean flags (`has_task_failure`, `has_negative_feedback`), metadata | 1 per session |
| `[Agent] Topic Classification` | Category label per topic model (e.g., `query_intent`, `product_area`, `error_domain`) | 1 per topic model per session |
| `[Agent] Score` (ai) | Rubric score with rationale (e.g., `task_completion: 0.85`), `source="ai"` | 1 per rubric per session |

**Configurability:** Topic models, rubric definitions, safety policies, and emotion taxonomies are configurable per organization. The categories in the table above are defaults, not fixed. Contact your Amplitude team to customize which dimensions are evaluated and what category values are used.

**When do enrichments run?** Enrichment runs **asynchronously** after the session closes, not inline with your SDK calls. A session closes when you call `track_session_end()`, or after 30 minutes of inactivity if you don't. Enrichment events typically appear within minutes of session close. Calling `track_session_end()` explicitly is recommended because it ensures timely enrichment and lets you attach `SessionEnrichments` in the same call.

**When `content_mode` is `"metadata_only"` or `"customer_enriched"`**, server-side enrichment is not available (the pipeline needs raw text to classify content). Use `customer_enriched` with `track_session_enrichment()` to bridge this gap: run your own classifier in your environment, then send structured labels (topics, rubric scores, outcomes) to Amplitude. No raw content leaves your environment, but you get the same analytics power (cohorts, funnels, retention segmented by session quality) as customers using `full` mode. This is how you get full analytics value without sending content to Amplitude.

#### Defining Your Taxonomy

The topic model names, rubric names, and category values are **yours to define**. The examples below use values from Amplitude's internal taxonomy as a reference, but you should use whatever categories make sense for your product and agents.

```python
from amplitude_ai import (
    SessionEnrichments, TopicClassification, RubricScore,
    EvidenceQuote, MessageLabel,
)

enrichments = SessionEnrichments(
    # Topic models -- categorical labels for your sessions
    topic_classifications={
        # Single-select: what was the user trying to do?
        "query_intent": TopicClassification(l1="quantitative_diagnostic"),

        # Multi-select: which product areas were involved?
        "product_area": TopicClassification(
            values=["charts", "cohorts"], primary="charts",
            topics_covered=["charts", "cohorts", "funnels"],
            outcomes_by_topic={"charts": "response_provided", "funnels": "abandoned"},
        ),

        # Subcategories for finer classification
        "error_domain": TopicClassification(l1="TAX", subcategories=["WRONG_EVENT"]),
    },

    # Rubrics -- scored evaluation dimensions (0.0 to 1.0)
    rubrics=[
        RubricScore(name="task_completion", score=0.85),
        RubricScore(
            name="response_quality", score=0.92,
            rationale="Clear and accurate",
            evidence=[
                EvidenceQuote(quote="Here is how to build a funnel...", turn_index=2, role="assistant"),
            ],
            improvement_opportunities="Could include a screenshot link",
        ),
    ],

    # Session outcome
    overall_outcome="response_provided",  # or "abandoned", "escalated", etc.

    # Session-level scores
    quality_score=0.88,
    sentiment_score=0.75,

    # Boolean flags for quick filtering
    has_task_failure=False,
    has_negative_feedback=False,

    # Failure detail (when has_task_failure=True)
    # task_failure_type="unable_to_complete",
    # task_failure_reason="Data source not connected",

    # Agent chain metadata (multi-agent flows)
    agent_chain=["router", "analytics-agent"],
    root_agent_name="router",

    # Request classification
    request_complexity="moderate",

    # Supplementary data
    error_categories=["timeout"],
    behavioral_patterns=["multi_turn_refinement"],
    custom_metadata={"deployment": "canary-v2"},

    # Retrospective message labels (keyed by message_id)
    message_labels={
        "msg-uuid-1": [MessageLabel(key="intent", value="how_to", confidence=0.94)],
        "msg-uuid-2": [MessageLabel(key="quality", value="good", confidence=0.91)],
    },
)

# Attach when ending a session
ai.track_session_end(user_id="user-1", session_id="sess-1", enrichments=enrichments)

# Or send enrichments at any time from a background pipeline
ai.track_session_enrichment(user_id="user-1", session_id="sess-1", enrichments=enrichments)
# Note: each call creates a separate [Agent] Session Enrichment event (not an overwrite).
# Call multiple times for streaming enrichment -- e.g., topics first, then rubric scores later.
```


#### SessionEnrichments Dataclass

The `SessionEnrichments` dataclass uses the same vocabulary as Amplitude's enrichment taxonomy framework: **topic models** for categorical classification and **rubrics** for scored evaluation. This ensures `[Agent] Session Enrichment` events from the SDK have property naming consistent with the server-side `[Agent] Session Evaluation`, `[Agent] Topic Classification`, and `[Agent] Score` events.

```python
@dataclass
class MessageLabel:
    """A key-value label attached to a message event."""
    key: str                          # e.g., "intent", "flow", "sentiment"
    value: str                        # e.g., "how_to", "onboarding", "neutral"
    confidence: float | None = None   # Optional 0.0-1.0

@dataclass
class EvidenceQuote:
    """A quoted excerpt from the conversation supporting a rubric score."""
    quote: str                        # The quoted text
    turn_index: int                   # 0-based position in conversation
    role: str | None = None           # "user", "assistant", "tool"

@dataclass
class TopicClassification:
    """Result of classifying a session for a single topic model."""
    l1: str | None = None             # Single-select mode (MECE) — e.g., "quantitative_diagnostic"
    values: list[str] | None = None   # Multi-select mode — e.g., ["charts", "cohorts"]
    primary: str | None = None        # Primary value in multi-select — e.g., "charts"
    l2: str | None = None             # Deprecated — use subcategories instead
    subcategories: list[str] | None = None  # Subcategory codes — e.g., ["WRONG_EVENT"]
    topics_covered: list[str] | None = None   # All topics discussed
    outcomes_by_topic: dict[str, str] | None = None  # Outcome per topic

@dataclass
class RubricScore:
    """Result of scoring a session on a single rubric."""
    name: str                         # e.g., "task_completion", "response_quality"
    score: float                      # 0.0-1.0
    rationale: str | None = None      # Optional explanation
    evidence: list[EvidenceQuote] | None = None  # Supporting quotes
    improvement_opportunities: str | None = None  # Suggested improvements

@dataclass
class SessionEnrichments:
    # Topic models — categorical classification per topic model
    topic_classifications: dict[str, TopicClassification] | None = None

    # Rubrics — scored evaluation dimensions
    rubrics: list[RubricScore] | None = None

    # Outcome
    overall_outcome: str | None = None  # "response_provided", "abandoned", etc.

    # Session-level scores
    quality_score: float | None = None       # 0.0-1.0
    sentiment_score: float | None = None     # 0.0-1.0

    # Boolean flags
    has_task_failure: bool = False
    has_negative_feedback: bool = False
    has_data_quality_issues: bool = False
    has_technical_failure: bool = False

    # Failure detail
    task_failure_type: str | None = None     # e.g., "unable_to_complete"
    task_failure_reason: str | None = None   # Free-text explanation

    # Feedback and error detail
    negative_feedback_phrases: list[str] | None = None
    data_quality_issues: list[str] | None = None
    technical_error_count: int | None = None

    # Agent chain metadata
    agent_chain: list[str] | None = None     # Ordered agent delegation chain
    root_agent_name: str | None = None       # Entry-point agent

    # Request classification
    request_complexity: str | None = None    # "simple", "moderate", "complex", "ambiguous"

    # Supplementary data
    error_categories: list[str] | None = None
    behavioral_patterns: list[str] | None = None
    custom_metadata: dict[str, Any] | None = None  # Arbitrary customer-defined metadata
    schema_version: str = "2.0"

    # Retrospective message labels (keyed by message_id)
    message_labels: dict[str, list[MessageLabel]] | None = None
```


#### TopicClassification Fields

Topics classify sessions along a dimension you define. Use `l1` for single-select (one category per session) or `values` + `primary` for multi-select (session touches multiple areas):

| Field | Type | Description |
|-------|------|-------------|
| `l1` | `str` | Single-select category. E.g., `"quantitative_diagnostic"`, `"help_guidance"`, `"artifact_creation"`. |
| `values` | `list[str]` | Multi-select categories. E.g., `["charts", "cohorts", "experiments"]`. |
| `primary` | `str` | Primary value in multi-select. Must be one of `values`. |
| `subcategories` | `list[str]` | Subcategory codes for finer classification. E.g., `["WRONG_EVENT"]`, `["HALLUCINATION"]`. |
| `l2` | `str` | **Deprecated.** Use `subcategories` instead. Scalar shorthand kept for backward compatibility. |
| `topics_covered` | `list[str]` | All topics discussed in multi-topic sessions. |
| `outcomes_by_topic` | `dict[str, str]` | Outcome per topic. E.g., `{"charts": "response_provided", "funnels": "abandoned"}`. |

#### RubricScore Fields

Rubrics are scored evaluation dimensions. Define whatever rubrics matter for your use case:

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `name` | `str` | Yes | Rubric name, e.g., `"task_completion"`, `"helpfulness"`, `"safety"`, `"groundedness"`. |
| `score` | `float` | Yes | 0.0 to 1.0. |
| `rationale` | `str` | No | Explanation for the score. Useful for debugging and auditing. |
| `evidence` | `list[EvidenceQuote]` | No | Quoted excerpts from the conversation supporting this score. |
| `improvement_opportunities` | `str` | No | Suggested improvements based on this evaluation. |

#### MessageLabel Fields

Labels are flexible key-value pairs for filtering and segmentation:

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `key` | `str` | Yes | Label key, e.g., `"intent"`, `"flow"`, `"sentiment"`, `"experiment"`. |
| `value` | `str` | Yes | Label value, e.g., `"how_to"`, `"onboarding"`, `"neutral"`. |
| `confidence` | `float` | No | Confidence score (0.0 to 1.0) when the label comes from a classifier. |

#### EvidenceQuote Fields

Quoted excerpts that support rubric scores:

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `quote` | `str` | Yes | The quoted text from the conversation. |
| `turn_index` | `int` | Yes | 0-based position in the conversation. |
| `role` | `str` | No | Role of the speaker (`"user"`, `"assistant"`, `"tool"`). |

#### Complete `customer_enriched` Example

End-to-end example for teams running their own classifiers. No conversation content leaves your environment; only structured labels:

```python
from amplitude import Amplitude
from amplitude_ai import (
    AIConfig, AmplitudeAI, ContentMode, SessionEnrichments, TopicClassification,
    RubricScore, EvidenceQuote, MessageLabel,
)

amplitude = Amplitude("YOUR_API_KEY")
ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(content_mode=ContentMode.CUSTOMER_ENRICHED))
agent = ai.agent(agent_id="support-agent")

with agent.session(user_id="user-1") as s:
    msg_id = s.track_user_message(content="How do I create a funnel?")
    ai_msg_id = s.track_ai_message(
        content="To create a funnel...",
        model="gpt-4o", provider="openai", latency_ms=450.0,
    )

# After session: run your classifiers, then send structured labels
enrichments = SessionEnrichments(
    topic_classifications={
        "query_intent": TopicClassification(l1="how_to"),
    },
    rubrics=[
        RubricScore(
            name="task_completion", score=0.85,
            evidence=[EvidenceQuote(quote="To create a funnel...", turn_index=1, role="assistant")],
        ),
    ],
    overall_outcome="response_provided",
    quality_score=0.85,
    request_complexity="simple",
    message_labels={
        msg_id: [MessageLabel(key="intent", value="how_to", confidence=0.94)],
        ai_msg_id: [MessageLabel(key="quality", value="good", confidence=0.91)],
    },
)
ai.track_session_enrichment(user_id="user-1", session_id=s.session_id, enrichments=enrichments)
```


#### How Scores and Enrichments Relate

Scores (`score()`) and enrichments (`track_session_enrichment()`) coexist and serve different purposes:

| Concern | `score()` | `track_session_enrichment()` |
|---------|-----------|------------------------------|
| **Purpose** | Rate a specific message or session | Classify a session holistically |
| **Granularity** | Message-level or session-level | Session-level only |
| **Data shape** | Single name/value pair per call | Structured batch: topics + rubrics + outcomes + flags |
| **Source tracking** | Yes (`user`, `ai`, `reviewer`) | No (assumed system/customer) |
| **Primary use** | User feedback, automated evals, human annotations | `content_mode="customer_enriched"` flow; background pipelines |
| **Categorical data** | No (numeric only) | Yes (topic classifications, outcomes, behavioral patterns) |

Use enrichments for comprehensive session classification (topic models + rubrics + outcomes in one batch). Use `score()` for individual quality signals, especially from end-users or at the message level.

---

### Provider Wrappers

Drop-in replacements that automatically track every LLM call, including reasoning content from thinking models, system prompts, and model configuration (temperature, top_p, max_tokens, streaming mode).

**Feature coverage by provider:**

| Feature            | OpenAI | AsyncOpenAI | Anthropic | Gemini | AzureOpenAI | Bedrock | Mistral |
| ------------------ | ------ | ----------- | --------- | ------ | ----------- | ------- | ------- |
| Streaming          | Yes    | Yes         | Yes       | Yes    | Yes         | Yes     | Yes     |
| Tool call tracking | Yes    | Yes         | Yes       | No     | Yes         | Yes     | No      |
| TTFB measurement   | Yes    | Yes         | Yes       | No     | Yes         | No      | No      |
| Cache token stats  | Yes    | Yes         | Yes       | No     | No          | No      | No      |
| Reasoning content  | Yes    | Yes         | Yes       | No     | Yes         | No      | No      |
| System prompt capture | Yes | Yes         | Yes       | Yes    | Yes         | Yes     | Yes     |
| Cost estimation    | Yes    | Yes         | Yes       | Yes    | Yes         | Yes     | Yes     |

#### OpenAI

```python
from amplitude_ai import OpenAI

client = OpenAI(amplitude=amplitude, api_key="sk-...")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is retention?"}],
    amplitude_user_id="user-1",
)
# [Agent] User Message + [Agent] AI Response tracked automatically
```


#### AsyncOpenAI

For async frameworks (FastAPI, pydantic_ai, etc.) that use `openai.AsyncOpenAI`:

```python
from amplitude_ai import AsyncOpenAI

client = AsyncOpenAI(amplitude=amplitude, api_key="sk-...")
response = await client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is retention?"}],
    amplitude_user_id="user-1",
)
```

Same feature coverage as the synchronous `OpenAI` wrapper — streaming, tool calls, reasoning, TTFB, cost estimation.

#### Anthropic

```python
from amplitude_ai import Anthropic

client = Anthropic(amplitude=amplitude, api_key="sk-ant-...")
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain funnels"}],
    amplitude_user_id="user-1",
)
```


#### Google Gemini

```python
from amplitude_ai import Gemini

client = Gemini(amplitude=amplitude, api_key="...", model_name="gemini-2.0-flash")
response = client.generate_content("What are cohorts?", amplitude_user_id="user-1")
```


#### Azure OpenAI

```python
from amplitude_ai import AzureOpenAI

client = AzureOpenAI(
    amplitude=amplitude,
    azure_endpoint="https://your-resource.openai.azure.com",
    api_key="...",
    api_version="2024-02-01",
)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is retention?"}],
    amplitude_user_id="user-1",
)
```


#### AWS Bedrock

```python
from amplitude_ai import Bedrock

client = Bedrock(amplitude=amplitude, region_name="us-east-1")
response = client.converse(
    modelId="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[{"role": "user", "content": [{"text": "Explain funnels"}]}],
    amplitude_user_id="user-1",
)
```


#### Mistral

```python
from amplitude_ai import Mistral

client = Mistral(amplitude=amplitude, api_key="...")
response = client.chat.complete(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "What are cohorts?"}],
    amplitude_user_id="user-1",
)
```


#### LangChain

```python
from amplitude_ai import create_amplitude_callback

callback = create_amplitude_callback(amplitude=amplitude, user_id="user-1")
# Pass as callback to any LangChain chain or agent
```


#### LlamaIndex

```python
from amplitude_ai import AmplitudeLlamaIndexHandler

handler = AmplitudeLlamaIndexHandler(amplitude=amplitude, user_id="user-1")
# Set as the global callback handler or pass to individual components
```


#### OpenAI Agents SDK

Tracing processor that plugs into the OpenAI Agents SDK's tracing system. Maps `GenerationSpanData` to `[Agent] AI Response`, `FunctionSpanData` to `[Agent] Tool Call`, and agent/handoff/guardrail spans to `[Agent] Span`.

```bash
pip install "amplitude-ai[openai-agents]"
```

```python
from agents import Agent, Runner, RunConfig
from amplitude import Amplitude
from amplitude_ai.integrations.openai_agents import AmplitudeTracingProcessor

amplitude = Amplitude("YOUR_API_KEY")
processor = AmplitudeTracingProcessor(
    amplitude=amplitude,
    user_id="user-1",
    agent_id="my-agent",
    env="production",
)

agent = Agent(name="support-bot", instructions="You are a helpful assistant.")
result = Runner.run_sync(
    agent,
    "What is retention?",
    run_config=RunConfig(tracing_processors=[processor]),
)
# All generations, tool calls, handoffs, and guardrail checks tracked automatically
```


#### Anthropic Tool Use Loop

Managed multi-turn tool_use loop that handles the Anthropic agentic pattern of repeated `tool_use` -> `tool_result` cycles. Tracks every turn automatically.

```python
from anthropic import Anthropic
from amplitude import Amplitude
from amplitude_ai.integrations.anthropic_tools import AmplitudeToolLoop

amplitude = Amplitude("YOUR_API_KEY")
client = Anthropic()

loop = AmplitudeToolLoop(
    amplitude=amplitude,
    client=client,
    user_id="user-1",
    tool_handlers={
        "get_weather": lambda city: f"72°F in {city}",
        "search": lambda query: f"Results for {query}",
    },
)

result = loop.run(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "What's the weather in SF?"}],
    tools=[{"name": "get_weather", "description": "Get weather", "input_schema": {...}}],
)
# Each turn emits [Agent] AI Response + [Agent] Tool Call events
# Loop stops when model returns stop_reason != "tool_use"
```

Supports async via `await loop.arun(...)` with `anthropic.AsyncAnthropic`.

#### CrewAI

Event listener hooks that capture CrewAI's LLM calls and tool usage across all agents in a crew.

```bash
pip install "amplitude-ai[crewai]"
```

```python
from crewai import Crew, Agent, Task
from amplitude import Amplitude
from amplitude_ai.integrations.crewai import AmplitudeCrewAIHooks

amplitude = Amplitude("YOUR_API_KEY")

with AmplitudeCrewAIHooks(amplitude=amplitude, user_id="user-1") as hooks:
    researcher = Agent(role="Researcher", goal="Find information", ...)
    writer = Agent(role="Writer", goal="Write content", ...)
    crew = Crew(agents=[researcher, writer], tasks=[...])
    result = crew.kickoff()
# All LLM calls and tool invocations across all agents tracked automatically
# Agent roles are captured as agent_id when no explicit agent_id is set
```


#### Reasoning Extraction

Provider wrappers auto-extract reasoning content from each provider's native response format:

| Provider | Extraction Method |
|----------|-------------------|
| OpenAI (o1, o3, etc.) | `response.choices[0].message.reasoning_content` |
| Anthropic (extended thinking) | Filter `response.content` for blocks with `type == "thinking"`, concatenate text |
| Google Gemini | Extract thinking parts from response |
| Mistral | `choice.message.reasoning_content` or typed content blocks |
| AWS Bedrock | Reasoning blocks from Bedrock Converse API response |

---

### OTEL GenAI Bridge Reference

The `AmplitudeAgentExporter` consumes any [OTEL GenAI semantic convention](https://opentelemetry.io/docs/specs/semconv/gen-ai/) spans and maps them to Amplitude `[Agent]` events. Works with any tool that emits standard OTEL GenAI spans:

- **OpenLIT**
- **Traceloop / OpenLLMetry**
- **OpenAI Python SDK** (with OTEL instrumentation enabled)
- Any manual OpenTelemetry instrumentation following the GenAI semantic conventions

> **Note on Langfuse:** Langfuse v3+ uses OTEL internally for transport and can *receive* OTEL traces as a backend. However, Langfuse's own SDK integrations (100+) use proprietary APIs, not standard OTEL GenAI spans. If you use an OTEL-native instrumentation library (OpenLIT, Traceloop) alongside Langfuse, the same GenAI spans can flow to both Langfuse and Amplitude simultaneously.

**Attribute mapping: OTEL GenAI semantic conventions to Amplitude properties:**

| OTEL GenAI Attribute | Amplitude Property | Notes |
|---|---|---|
| `gen_ai.operation.name` | (event routing) | `chat`/`text_completion`/`generate_content` -> User Message + AI Response; `embeddings` -> Embedding; `execute_tool` -> Tool Call; `invoke_agent`/`create_agent` -> AI Response with agent metadata |
| `gen_ai.response.model` | `[Agent] Model Name` | Preferred; often contains the versioned name (e.g., `gpt-4o-2024-11-20`) |
| `gen_ai.request.model` | `[Agent] Model Name` | Fallback when response model is absent |
| `gen_ai.provider.name` | `[Agent] Provider` | |
| `gen_ai.usage.input_tokens` | `[Agent] Input Tokens` | |
| `gen_ai.usage.output_tokens` | `[Agent] Output Tokens` | |
| (computed) input + output | `[Agent] Total Tokens` | Auto-summed when both present |
| `gen_ai.response.finish_reasons` | `[Agent] Finish Reason` | First element of array |
| `gen_ai.input.messages` | User message content | Last `role=user` message; respects `privacy_config` |
| `gen_ai.output.messages` | AI response content | First output message; respects `privacy_config` |
| `gen_ai.system_instructions` | `[Agent] System Prompt` | Respects `privacy_config` |
| `gen_ai.request.temperature` | `[Agent] Temperature` | |
| `gen_ai.request.max_tokens` | `[Agent] Max Output Tokens` | |
| `gen_ai.request.top_p` | `[Agent] Top P` | |
| `gen_ai.conversation.id` | `[Agent] Session ID` | Fallback when no ContextVar session active |
| `gen_ai.agent.id` | `[Agent] Agent ID` | `invoke_agent`/`create_agent` operations |
| `gen_ai.agent.name` | `[Agent] Agent Name` | `invoke_agent`/`create_agent` operations |
| `gen_ai.tool.name` | Tool name | `execute_tool` operations; falls back to span name parsing |
| `gen_ai.embeddings.dimension.count` | `[Agent] Embedding Dimensions` | `embeddings` operations only |
| `error.type` | `[Agent] Is Error` + `[Agent] Error Message` | |
| Span `trace_id` (hex) | `[Agent] Trace ID` | Fallback when no ContextVar trace_id active |
| Span duration (ns) | `[Agent] Latency Ms` | Computed from span timestamps |
| (computed) model + tokens | `[Agent] Cost USD` | Auto-calculated via built-in `genai-prices` database |
| `enduser.id` | `user_id` | Fallback when no SessionContext or default user_id |

**Not mapped by the bridge** (use native SDK provider wrappers for these): cache tokens, reasoning content/tokens, TTFB, streaming detection, event graph linking (`parent_message_id`).

**Scope filtering:** control which spans reach Amplitude:

```python
# Only process spans from specific instrumentation scopes
exporter = AmplitudeAgentExporter(
    amplitude=amplitude,
    user_id="user-123",
    allowed_scopes={"langfuse-sdk", "openai"},
)

# Or block infrastructure scopes
exporter = AmplitudeAgentExporter(
    amplitude=amplitude,
    user_id="user-123",
    blocked_scopes={"fastapi", "sqlalchemy", "psycopg"},
)
```

**OTEL bridge vs. native wrappers, field-level comparison:**

| Capability | OTEL Bridge | Native Wrapper |
|---|---|---|
| Model, provider, tokens, latency, errors | Yes | Yes |
| Cost calculation | Yes (basic) | Yes (cache-aware, 2-5x more accurate for prompt-cached workloads) |
| System prompt, temperature, top_p | Yes | Yes |
| Message content (when opted-in) | Yes | Yes |
| Cache read / creation tokens | No (not in OTEL spec) | Yes |
| Reasoning content and tokens | No (not in OTEL spec) | Yes |
| Time to first byte (TTFB) | No | Yes |
| Streaming detection | No | Yes |
| Event graph linking (parent_message_id) | No | Yes |
| Session / agent context | With `agent.session()` | With `agent.session()` |

---

## API Reference

Message, tool, and span tracking methods return a UUID string (`message_id`, `invocation_id`, or `span_id`) for event linking. Session lifecycle methods and `score()` return `None`.

| Category | Methods |
|---|---|
| Messages | `track_user_message(user_id, content, session_id, labels=..., ...) -> str` | 
| | `track_ai_message(user_id, content, session_id, model, provider, latency_ms, labels=..., ...) -> str` |
| Operations | `track_tool_call(user_id, tool_name, latency_ms, success, ...) -> str` |
| | `track_embedding(user_id, model, provider, latency_ms, ...) -> str` |
| | `track_span(user_id, span_name, trace_id, latency_ms, ...) -> str` |
| Sessions | `track_session_end(user_id, session_id, ...)` |
| | `track_session_enrichment(user_id, session_id, enrichments)` |
| Scoring | `score(user_id, name, value, target_id, ...)` |
| Types | `MessageLabel(key, value, confidence=...)` (inline message labels) |
| | `EvidenceQuote(quote, turn_index, role=...)` (rubric evidence) |
| | `SessionEnrichments(...)` (structured session classifications) |
| Agents | `agent(agent_id, ...) -> BoundAgent` — pre-configured handle with inherited context |
| | `agent.child(agent_id, ...) -> BoundAgent` — inherits parent context, sets `parent_agent_id` |
| | `agent.session(session_id=None) -> Session` — context manager, auto-closes on exit |
| Utilities | `status() -> dict` — config, available providers, patched providers |
| | `tenant(customer_org_id, ...) -> TenantHandle` — multi-tenant factory for `BoundAgent` |
| Lifecycle | `flush()`, `shutdown()` |

---

## Usage Examples

### Messages

```python
msg_id = ai.track_user_message(
    user_id="user-1",
    content="How do I set up a funnel?",
    session_id="sess-1",
    trace_id="trace-1",
    turn_id=1,
    agent_id="support-agent",
    env="production",
)

ai_msg_id = ai.track_ai_message(
    user_id="user-1",
    content="To create a funnel chart...",
    session_id="sess-1",
    trace_id="trace-1",
    model="gpt-4o",
    provider="openai",
    latency_ms=450.0,
    input_tokens=120,
    output_tokens=340,
    total_tokens=460,
    total_cost_usd=0.0023,
    turn_id=2,
    agent_id="support-agent",
    env="production",
    ttfb_ms=85.0,
)
```


### Cache-Aware Cost Calculation

LLM providers cache repeated token prefixes (system prompts, tool definitions) at reduced rates. Pass cache breakdowns for accurate cost tracking. When `total_cost_usd` is omitted, the SDK auto-calculates with cache-aware pricing:

```python
ai_msg_id = ai.track_ai_message(
    user_id="user-1",
    content="Here's how to configure...",
    session_id="sess-1",
    model="claude-sonnet-4-20250514",
    provider="anthropic",
    latency_ms=800.0,
    input_tokens=5000,
    output_tokens=200,
    cache_read_tokens=4500,        # ~10% cost (Anthropic), ~50% (OpenAI)
    cache_creation_tokens=500,     # ~125% cost (Anthropic)
)
```

> **Note — pricing data freshness.** Cost calculation relies on pricing data bundled in the installed `genai-prices` package. Newly released models may return `$0` until the package is updated. To get the latest pricing between package releases, opt in to live updates at startup:
>
> ```python
> from amplitude_ai import enable_live_price_updates
> enable_live_price_updates()  # fetches latest prices from genai-prices GitHub repo hourly
> ```
>
> This makes periodic HTTPS requests to `raw.githubusercontent.com` (~26 KB each). Only enable in environments where outbound network access is permitted.

### Implicit Feedback

Track behavioral signals that indicate whether a response met the user's need, without requiring explicit ratings:

```python
# User asks a question
msg1 = ai.track_user_message(
    user_id="u1", content="How do I create a funnel?", session_id="s1",
)

# AI responds -- user copies the answer (positive signal)
ai_msg = ai.track_ai_message(
    user_id="u1", content="To create a funnel, go to...",
    session_id="s1", model="gpt-4o", provider="openai", latency_ms=300.0,
    was_copied=True,
)

# User regenerates (negative signal -- first response wasn't good enough)
msg2 = ai.track_user_message(
    user_id="u1", content="How do I create a funnel?",
    session_id="s1", is_regeneration=True,
)

# User edits their question (refining intent)
msg3 = ai.track_user_message(
    user_id="u1", content="How do I create a conversion funnel for signups?",
    session_id="s1", is_edit=True, edited_message_id=msg1,
)

# Session where user abandoned after the first exchange
ai.track_session_end(user_id="u1", session_id="s1", abandonment_turn=1)
```


### File Attachments

Track rich media (images, PDFs, audio, video) without sending file content through the SDK. Include a `url` pointing to the resource on your own infrastructure (CDN, S3, internal docs system) and the LLM session viewer renders it on-the-fly in the reviewer's browser. Amplitude never stores or proxies the file; the browser fetches directly from your URL using your existing network access and auth.

If the URL is live when someone reviews the session, they see the full resource inline. If it has expired or is unreachable, the viewer falls back to the filename and type.

```python
# Image -- session viewer renders it inline from your CDN
s.track_user_message(
    content="What's wrong with this error?",
    attachments=[{
        "type": "image",
        "name": "error_screenshot.png",
        "url": "https://cdn.example.com/uploads/error_screenshot.png",
        "mime_type": "image/png",
    }],
)

# PDF -- session viewer opens it in an embedded viewer from your docs system
s.track_user_message(
    content="Summarize the key risks in this contract",
    attachments=[{
        "type": "pdf",
        "name": "vendor_agreement_v3.pdf",
        "url": "https://docs.internal.example.com/contracts/vendor_agreement_v3.pdf",
        "mime_type": "application/pdf",
        "page_count": 23,
        "department": "legal",
    }],
)

# Multiple attachments, mixed types
s.track_user_message(
    content="Compare these datasets and explain the chart",
    attachments=[
        {"type": "csv", "name": "sales_2025.csv", "url": "https://s3.example.com/data/sales_2025.csv"},
        {"type": "csv", "name": "sales_2024.csv", "url": "https://s3.example.com/data/sales_2024.csv"},
        {"type": "image", "name": "revenue_chart.png", "url": "https://cdn.example.com/charts/revenue.png"},
    ],
)

# AI-generated attachment (works on track_ai_message too)
s.track_ai_message(
    content="Here's the visualization you requested",
    model="gpt-4o", provider="openai", latency_ms=3200,
    attachments=[{
        "type": "image",
        "name": "forecast_chart.png",
        "url": "https://cdn.example.com/generated/forecast_chart.png",
        "mime_type": "image/png",
    }],
)
```

The attachment dict is free-form. Add any extra keys you need (`size_bytes`, `page_count`, `duration_seconds`, `department`, `internal_doc_id`). The SDK extracts `type` for aggregate analytics; everything else is serialized as-is into the event and available to viewers and downstream consumers.

### Multi-Agent & Multi-Tenant

**With BoundAgent** (recommended):

```python
orchestrator = ai.agent("orchestrator", agent_version="v4.2", env="prod", customer_org_id="acme-123")
billing = orchestrator.child("billing-agent")  # inherits agent_version="v4.2"

with billing.session(user_id="u1") as s:
    s.new_trace()
    s.track_user_message(content="Check my billing status")
    s.track_ai_message(content="Your balance is...", model="gpt-4o", provider="openai", latency_ms=200)
```

**With `ai.tenant()`** (multi-tenant shorthand):

For platforms serving multiple customers, `ai.tenant()` pre-fills `customer_org_id` and `groups` on every agent:

```python
tenant = ai.tenant("acme-corp", groups={"company": "acme-corp"}, env="production")

support_bot = tenant.agent("support-bot", user_id="u1")
billing_bot = tenant.agent("billing-bot", user_id="u1")
# Both agents inherit customer_org_id="acme-corp" and groups automatically
```

Explicit kwargs on `tenant.agent()` override the defaults.

**Without BoundAgent** (manual, same result):

```python
msg_id = ai.track_user_message(
    user_id="user-1",
    content="Check my billing status",
    session_id="sess-1",
    trace_id="trace-1",
    agent_id="billing-agent",
    parent_agent_id="orchestrator",
    customer_org_id="cust-acme-123",
    env="production",
)
```


### A/B Testing with Context

The `context` dict lets you attach experiment variants, feature flags, prompt revisions, and any other segmentation dimension to every event:

```python
# Variant assigned at session start (e.g., from your experiment framework)
variant = get_experiment_variant(user_id, "prompt-rewrite-v2")

agent = ai.agent(
    "support-bot",
    user_id="u1",
    env="production",
    context={
        "experiment_variant": variant,          # "control" or "treatment"
        "feature_flags": {"rag_v2": True},
        "prompt_revision": "abc123",
    },
)

# All events in this session carry the context -- segment quality metrics
# by experiment_variant in Amplitude charts and cohorts
with agent.session() as s:
    s.track_user_message(content="How do I set up billing?")
    s.track_ai_message(content="...", model="gpt-4o", provider="openai", latency_ms=300)
```

Child agents merge context (child keys override parent keys):

```python
orchestrator = ai.agent("orchestrator",
                         context={"experiment_variant": "treatment"})
researcher = orchestrator.child("researcher",
                                 context={"sub_experiment": "rag-rerank"})
# researcher.context == {"experiment_variant": "treatment", "sub_experiment": "rag-rerank"}
```


### Tool Calls

```python
inv_id = ai.track_tool_call(
    user_id="user-1",
    tool_name="search_docs",
    latency_ms=85.0,
    success=True,
    session_id="sess-1",
    trace_id="trace-1",
    turn_id=3,
    input={"query": "funnel setup"},
    output="Found 3 matching docs...",
    parent_message_id=ai_msg_id,     # links this tool call to the AI response
    agent_id="support-agent",
    env="production",
)
```


### Embeddings

```python
span_id = ai.track_embedding(
    user_id="user-1",
    model="text-embedding-3-small",
    provider="openai",
    latency_ms=25.0,
    input_tokens=45,
    dimensions=1536,
    total_cost_usd=0.00001,
    session_id="sess-1",
)
```


### Generic Spans

Track any pipeline operation (vector search, rerank, guardrails, retrieval, etc.):

```python
span_id = ai.track_span(
    user_id="user-1",
    span_name="vector_search",
    trace_id="trace-1",
    latency_ms=120.0,
    input_state={"query": "funnel setup", "top_k": 10},
    output_state={"results_count": 3},
    session_id="sess-1",
)
```


### `@tool` Decorator

Automatically track function calls as `[Agent] Tool Call` events:

```python
from amplitude_ai import tool

@tool(amplitude=amplitude)
def search_knowledge_base(query: str) -> str:
    """Search the knowledge base for relevant articles."""
    return "Found 3 results..."

# Every call tracked with latency, input, output, and success status
result = search_knowledge_base(query="retention", amplitude_user_id="user-1")
```

Works with `async` functions too. The decorator detects coroutines automatically:

```python
@tool(amplitude=amplitude)
async def fetch_user_profile(user_id: str) -> dict:
    """Fetch user profile from the API."""
    async with httpx.AsyncClient() as client:
        resp = await client.get(f"/users/{user_id}")
        return resp.json()

# Tracked identically to sync — latency, input, output, success
profile = await fetch_user_profile(user_id="u-123", amplitude_user_id="user-1")
```


### `@observe` Decorator

Track any function as a `[Agent] Span` event with automatic latency measurement, error capture, and session lifecycle management:

```python
from amplitude_ai import observe

@observe
def summarize_document(text: str) -> str:
    """Summarize a document using an LLM pipeline."""
    chunks = chunk_text(text)
    summaries = [call_llm(chunk) for chunk in chunks]
    return combine_summaries(summaries)

result = summarize_document(long_text)
# Tracked: span_name="summarize_document", latency, input/output state
```

Sessions are handled automatically: `@observe` joins an active session if one exists, or creates and closes its own. Nested calls share the outer session. Use `@observe(name="custom-span-name")` to override the function name. Async functions are detected automatically. In `metadata_only` mode, only function name, latency, and error status are captured.

```python
@observe
def pipeline(query):
    step1(query)    # @observe — attaches to pipeline's session, not a new one
    step2(query)    # @observe — same session, same trace
```


### Common Recipes

**Tracking agent actions and side effects.** When your agent takes real-world actions (issuing refunds, sending emails, creating tickets), use `track_span()`. The `span_name` is the action type, `output_state` carries the result, and `is_error` captures failures:

```python
# Agent issues a refund via Stripe
span_id = s.track_span(
    span_name="issue_refund",
    latency_ms=340.0,
    input_state={"order_id": "ord-789", "amount": 49.99},
    output_state={"transaction_id": "txn_abc", "success": True},
)

# Agent sends an email via SendGrid
span_id = s.track_span(
    span_name="send_email",
    latency_ms=120.0,
    input_state={"template": "refund_confirmation", "recipient": "user@example.com"},
    output_state={"message_id": "sg-456", "success": True},
)

# Failed action
span_id = s.track_span(
    span_name="create_ticket",
    latency_ms=2100.0,
    is_error=True,
    error_message="Zendesk API rate limited",
    input_state={"subject": "Refund follow-up"},
)
```

Filter by `[Agent] Span Name` in Amplitude to build dashboards for action success rates, latency by target system, and error attribution.

**Tracking guardrails and safety checks.** Content filters, injection detection, and policy checks are spans too:

```python
span_id = s.track_span(
    span_name="content_filter",
    latency_ms=15.0,
    input_state={"check": "prompt_injection"},
    output_state={"blocked": True, "reason": "injection_detected"},
    is_error=True,
    error_message="Prompt injection detected -- blocked",
)
```

**Tracking RAG pipelines.** Use nested spans to capture the full retrieval pipeline (embed, search, rerank) as a single traceable unit:

```python
rag_span = s.track_span(span_name="rag_pipeline", latency_ms=280.0)

embed_id = s.track_embedding(
    model="text-embedding-3-small",
    provider="openai",
    input_tokens=8,
    latency_ms=45.0,
)

search_span = s.track_span(
    span_name="vector_search",
    latency_ms=90.0,
    parent_span_id=rag_span,
    input_state={"query": "billing setup", "top_k": 10},
    output_state={"results_count": 5, "best_score": 0.94},
)

rerank_span = s.track_span(
    span_name="rerank",
    latency_ms=60.0,
    parent_span_id=rag_span,
    input_state={"candidates": 5},
    output_state={"kept": 3},
)
```

**Connecting AI events to business outcomes.** Your existing product events already track business outcomes. Because AI events share the same `user_id`, you build cross-product funnels directly in Amplitude. No dedicated "goal" event needed:

```
[Agent] User Message  ->  [Agent] AI Response  ->  Purchase Completed
```

Build a cohort of users whose AI sessions scored above 0.8 on `task_completion` and compare their conversion rate to everyone else. The funnel builder connects AI sessions to any downstream product event.

---

## Event Schema

The SDK produces 8 event types, all prefixed with `[Agent]`.

### SDK Events

| Event | Method | Description |
|-------|--------|-------------|
| `[Agent] User Message` | `track_user_message()` | Session/trace/turn IDs, message content, agent IDs, implicit feedback (regeneration, edit), file attachments |
| `[Agent] AI Response` | `track_ai_message()` | Model, provider, latency, tokens (including reasoning), cost, finish reason, reasoning content, system prompt, model config, copy signal |
| `[Agent] Tool Call` | `track_tool_call()` | Tool name, input/output, latency, success status |
| `[Agent] Embedding` | `track_embedding()` | Model, provider, latency, tokens, vector dimensions, cost |
| `[Agent] Span` | `track_span()` | Generic operation tracking (name, input/output state, latency, parent span hierarchy) |
| `[Agent] Session End` | `track_session_end()` | Explicit session close with optional enrichments, abandonment tracking |
| `[Agent] Session Enrichment` | `track_session_enrichment()` | Customer-provided session classifications. Distinct from server-side `[Agent] Session Evaluation`. |
| `[Agent] Score` | `score()` | Quality signal attached to a message or session (user feedback, automated evals, human annotations) |

### Server-Side Events (automatic)

| Event | Description |
|-------|-------------|
| `[Agent] Session Evaluation` | Session-level summary: outcome, turn count, flags (`has_task_failure`, `has_negative_feedback`), metadata |
| `[Agent] Topic Classification` | One event per configured topic model per session: `model_name`, `primary`, `subcategories`, `values` |
| `[Agent] Score` (reused) | One event per configured rubric per session, with `[Agent] Evaluation Source = "ai"` |

### How Behavioral Signals Become Analytics

The SDK captures **behavioral facts** at the application layer, and when `content_mode="full"`, server-side enrichment detects patterns across the full session. Both converge into the same charts, cohorts, and funnels:

| SDK call | What appears in Amplitude | What you can build |
|---|---|---|
| `track_user_message(is_regeneration=True)` | `[Agent] User Message` with `is_regeneration=True` + `[Agent] Session Evaluation` with `behavioral_patterns=["retry_storm"]` | Cohort of frustrated users -> target with Guide -> measure churn delta |
| `track_ai_message(was_copied=True)` | `[Agent] AI Response` with `was_copied=True` | Copy rate as positive quality signal, no explicit rating required |
| `score(source="user", value=1.0)` | `[Agent] Score` with `source="user"` | Single chart: user feedback, LLM-as-judge, and human annotations side by side |

All content properties respect the configured `content_mode`. See [Privacy & Content Control](#privacy-content-control) for tier details.

---

## Event Property Reference

All event properties are prefixed with `[Agent]` (except `[Amplitude] Session Replay ID`). This reference is auto-generated and matches what gets registered in Amplitude's data catalog via the `amplitude-ai-register-catalog` CLI.

### Common Properties (present on all SDK events)

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Session ID` | string | Yes | Unique session identifier. All events in one conversation share the same session ID. |
| `[Agent] Trace ID` | string | No | Identifies one user-message-to-AI-response cycle within a session. |
| `[Agent] Turn ID` | number | No | Monotonically increasing counter for event ordering within a session. |
| `[Agent] Agent ID` | string | No | Identifies which AI agent handled the interaction (e.g., 'support-bot', 'houston'). |
| `[Agent] Parent Agent ID` | string | No | For multi-agent orchestration: the agent that delegated to this agent. |
| `[Agent] Customer Org ID` | string | No | Organization ID for multi-tenant platforms. Enables account-level group analytics. |
| `[Agent] Agent Version` | string | No | Agent code version (e.g., 'v4.2'). Enables version-over-version quality comparison. |
| `[Agent] Agent Description` | string | No | Human-readable description of the agent's purpose (e.g., 'Handles user chat requests via OpenAI GPT-4o'). Enables observability-driven agent registry from event streams. |
| `[Agent] Context` | string | No | Serialized JSON dict of arbitrary segmentation dimensions (experiment_variant, surface, feature_flag, prompt_revision, etc.). |
| `[Agent] Env` | string | No | Deployment environment: 'production', 'staging', or 'dev'. |
| `[Agent] SDK Version` | string | Yes | Version of the amplitude-ai SDK that produced this event. |
| `[Agent] Runtime` | string | Yes | SDK runtime: 'python' or 'node'. |

### User Message Properties

Event-specific properties for `[Agent] User Message` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Message ID` | string | Yes | Unique identifier for this message event (UUID). Used to link scores and tool calls back to specific messages. |
| `[Agent] Component Type` | string | Yes | Type of component that produced this event: 'user_input', 'llm', 'tool', 'embedding'. |
| `[Agent] Locale` | string | No | User locale (e.g., 'en-US'). |
| `[Amplitude] Session Replay ID` | string | No | Links to Amplitude Session Replay (format: device_id/session_id). Enables one-click navigation from AI session to browser replay. |
| `[Agent] Is Regeneration` | boolean | No | Whether the user requested the AI regenerate a previous response. |
| `[Agent] Is Edit` | boolean | No | Whether the user edited a previous message and resubmitted. |
| `[Agent] Edited Message ID` | string | No | The message_id of the original message that was edited (links the edit to the original). |
| `[Agent] Has Attachments` | boolean | No | Whether this message includes file attachments (uploads, images, etc.). |
| `[Agent] Attachment Types` | string[] | No | Distinct attachment types (e.g., 'pdf', 'image', 'csv'). Serialized JSON array. |
| `[Agent] Attachment Count` | number | No | Number of file attachments included with this message. |
| `[Agent] Total Attachment Size Bytes` | number | No | Total size of all attachments in bytes. |
| `[Agent] Attachments` | string | No | Serialized JSON array of attachment metadata (type, name, size_bytes, mime_type). Only metadata, never file content. |
| `[Agent] Message Labels` | string | No | Serialized JSON array of MessageLabel objects (key-value pairs with optional confidence). Used for routing tags, classifier output, business context. |
| `[Agent] Message Source` | string | No | Origin of the user message: 'user' for real end-user input, 'agent' for inter-agent delegation (parent agent sending instructions to a child agent). Automatically set by provider wrappers based on parent_agent_id context. |

### AI Response Properties

Event-specific properties for `[Agent] AI Response` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Message ID` | string | Yes | Unique identifier for this message event (UUID). Used to link scores and tool calls back to specific messages. |
| `[Agent] Component Type` | string | Yes | Type of component that produced this event: 'user_input', 'llm', 'tool', 'embedding'. |
| `[Agent] Model Name` | string | Yes | LLM model identifier (e.g., 'gpt-4o', 'claude-sonnet-4-20250514'). |
| `[Agent] Provider` | string | Yes | LLM provider name (e.g., 'openai', 'anthropic', 'google', 'mistral', 'bedrock'). |
| `[Agent] Latency Ms` | number | Yes | Total wall-clock latency in milliseconds for this operation. |
| `[Agent] Is Error` | boolean | Yes | Whether this event represents an error condition. |
| `[Agent] Error Message` | string | No | Error message text when Is Error is true. |
| `[Agent] Locale` | string | No | User locale (e.g., 'en-US'). |
| `[Agent] Span Kind` | string | No | Classification of the span type for OTEL bridge compatibility. |
| `[Amplitude] Session Replay ID` | string | No | Links to Amplitude Session Replay (format: device_id/session_id). Enables one-click navigation from AI session to browser replay. |
| `[Agent] TTFB Ms` | number | No | Time to first byte/token in milliseconds. Measures perceived responsiveness for streaming. |
| `[Agent] Input Tokens` | number | No | Number of input/prompt tokens consumed by this LLM call. |
| `[Agent] Output Tokens` | number | No | Number of output/completion tokens generated by this LLM call. |
| `[Agent] Total Tokens` | number | No | Total tokens consumed (input + output). |
| `[Agent] Reasoning Tokens` | number | No | Tokens consumed by reasoning/thinking (o1, o3, extended thinking models). |
| `[Agent] Cache Read Tokens` | number | No | Input tokens served from the provider's prompt cache (cheaper rate). Used for cache-aware cost calculation. |
| `[Agent] Cache Creation Tokens` | number | No | Input tokens that created new prompt cache entries. |
| `[Agent] Cost USD` | number | No | Estimated cost in USD for this LLM call. Cache-aware when cache token counts are provided. |
| `[Agent] Finish Reason` | string | No | Why the model stopped generating: 'stop', 'end_turn', 'tool_use', 'length', 'content_filter', etc. |
| `[Agent] Tool Calls` | string | No | Serialized JSON array of tool call requests made by the AI in this response. |
| `[Agent] Has Reasoning` | boolean | No | Whether the AI response included reasoning/thinking content. |
| `[Agent] Reasoning Content` | string | No | The AI's reasoning/thinking content (when available and content_mode permits). |
| `[Agent] System Prompt` | string | No | The system prompt used for this LLM call (when content_mode permits). Chunked for long prompts. |
| `[Agent] System Prompt Length` | number | No | Character length of the system prompt. |
| `[Agent] Tool Definitions` | string | No | Normalized JSON array of tool definitions sent to the LLM (when content_mode permits). Each entry contains name, description, and parameters schema. |
| `[Agent] Tool Definitions Count` | number | No | Number of tool definitions in the LLM request. |
| `[Agent] Tool Definitions Hash` | string | No | Stable SHA-256 hash of the normalized tool definitions. Always present regardless of content_mode; enables toolset change detection without exposing schemas. |
| `[Agent] Temperature` | number | No | Temperature parameter used for this LLM call. |
| `[Agent] Max Output Tokens` | number | No | Maximum output tokens configured for this LLM call. |
| `[Agent] Top P` | number | No | Top-p (nucleus sampling) parameter used for this LLM call. |
| `[Agent] Is Streaming` | boolean | No | Whether this response was generated via streaming. |
| `[Agent] Prompt ID` | string | No | Identifier for the prompt template or version used. |
| `[Agent] Was Copied` | boolean | No | Whether the user copied this AI response content. An implicit positive quality signal. |
| `[Agent] Was Cached` | boolean | No | Whether this response was served from a semantic/full-response cache (distinct from token-level prompt caching). |
| `[Agent] Model Tier` | string | No | Model tier classification: 'fast' (GPT-4o-mini, Haiku, Flash), 'standard' (GPT-4o, Sonnet, Pro), or 'reasoning' (o1, o3, DeepSeek-R1). Auto-inferred from model name. |
| `[Agent] Has Attachments` | boolean | No | Whether this AI response includes generated attachments (images, charts, files). |
| `[Agent] Attachment Types` | string[] | No | Distinct attachment types in this AI response. Serialized JSON array. |
| `[Agent] Attachment Count` | number | No | Number of attachments generated by the AI in this response. |
| `[Agent] Total Attachment Size Bytes` | number | No | Total size of all AI-generated attachments in bytes. |
| `[Agent] Attachments` | string | No | Serialized JSON array of AI-generated attachment metadata. |
| `[Agent] Message Labels` | string | No | Serialized JSON array of MessageLabel objects attached to this AI response. |
| `[Agent] Message Label Map` | string | No | Serialized JSON map of label key to value for quick lookup. |

### Tool Call Properties

Event-specific properties for `[Agent] Tool Call` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Component Type` | string | Yes | Type of component that produced this event: 'user_input', 'llm', 'tool', 'embedding'. |
| `[Agent] Latency Ms` | number | Yes | Total wall-clock latency in milliseconds for this operation. |
| `[Agent] Is Error` | boolean | Yes | Whether this event represents an error condition. |
| `[Agent] Error Message` | string | No | Error message text when Is Error is true. |
| `[Agent] Locale` | string | No | User locale (e.g., 'en-US'). |
| `[Agent] Span Kind` | string | No | Classification of the span type for OTEL bridge compatibility. |
| `[Amplitude] Session Replay ID` | string | No | Links to Amplitude Session Replay (format: device_id/session_id). Enables one-click navigation from AI session to browser replay. |
| `[Agent] Invocation ID` | string | Yes | Unique identifier for this tool invocation (UUID). Used to link tool calls to parent messages. |
| `[Agent] Tool Name` | string | Yes | Name of the tool/function that was invoked (e.g., 'search_docs', 'web_search'). |
| `[Agent] Tool Success` | boolean | Yes | Whether the tool call completed successfully. |
| `[Agent] Tool Input` | string | No | Serialized JSON of the tool's input arguments. Only sent when content_mode='full'. |
| `[Agent] Tool Output` | string | No | Serialized JSON of the tool's output/return value. Only sent when content_mode='full'. |
| `[Agent] Parent Message ID` | string | No | The message_id of the user message that triggered this tool call. Links the tool call into the event graph. |

### Embedding Properties

Event-specific properties for `[Agent] Embedding` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Component Type` | string | Yes | Type of component that produced this event: 'user_input', 'llm', 'tool', 'embedding'. |
| `[Agent] Model Name` | string | Yes | LLM model identifier (e.g., 'gpt-4o', 'claude-sonnet-4-20250514'). |
| `[Agent] Provider` | string | Yes | LLM provider name (e.g., 'openai', 'anthropic', 'google', 'mistral', 'bedrock'). |
| `[Agent] Latency Ms` | number | Yes | Total wall-clock latency in milliseconds for this operation. |
| `[Agent] Span ID` | string | Yes | Unique identifier for this embedding operation (UUID). |
| `[Agent] Input Tokens` | number | No | Number of input tokens processed by the embedding model. |
| `[Agent] Embedding Dimensions` | number | No | Dimensionality of the output embedding vector. |
| `[Agent] Cost USD` | number | No | Estimated cost in USD for this embedding operation. |

### Span Properties

Event-specific properties for `[Agent] Span` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Latency Ms` | number | Yes | Total wall-clock latency in milliseconds for this operation. |
| `[Agent] Is Error` | boolean | Yes | Whether this event represents an error condition. |
| `[Agent] Error Message` | string | No | Error message text when Is Error is true. |
| `[Agent] Span ID` | string | Yes | Unique identifier for this span (UUID). |
| `[Agent] Span Name` | string | Yes | Name of the operation (e.g., 'rag_pipeline', 'vector_search', 'rerank'). |
| `[Agent] Parent Span ID` | string | No | Span ID of the parent span for nested pipeline steps. |
| `[Agent] Input State` | string | No | Serialized JSON of the span's input state. Only sent when content_mode='full'. |
| `[Agent] Output State` | string | No | Serialized JSON of the span's output state. Only sent when content_mode='full'. |

### Session End Properties

Event-specific properties for `[Agent] Session End` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Enrichments` | string | No | Serialized JSON of SessionEnrichments (topic classifications, rubric scores, outcome, flags). Attached when enrichments are provided at session close. |
| `[Agent] Abandonment Turn` | number | No | Turn ID of the last user message that received an AI response before the user left. Low values (e.g., 1) strongly signal first-response dissatisfaction. |
| `[Agent] Session Idle Timeout Minutes` | number | No | Custom idle timeout for this session (default 30 min). Tells the server how long to wait before auto-closing. |

### Session Enrichment Properties

Event-specific properties for `[Agent] Session Enrichment` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Enrichments` | string | Yes | Serialized JSON of SessionEnrichments: topic_classifications, rubrics, overall_outcome, quality_score, sentiment_score, boolean flags, agent chain metadata, and message labels. |

### Score Properties

Event-specific properties for `[Agent] Score` (in addition to common properties above).

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Score Name` | string | Yes | Name of the score (e.g., 'user-feedback', 'task_completion', 'accuracy', 'groundedness'). |
| `[Agent] Score Value` | number | Yes | Numeric score value. Binary (0/1), continuous (0.0-1.0), or rating scale (1-5). |
| `[Agent] Target ID` | string | Yes | The message_id or session_id being scored. |
| `[Agent] Target Type` | string | Yes | What is being scored: 'message' or 'session'. |
| `[Agent] Evaluation Source` | string | Yes | Source of the evaluation: 'user' (end-user feedback), 'ai' (automated/server pipeline), or 'reviewer' (human expert). |
| `[Agent] Comment` | string | No | Optional text explanation for the score (respects content_mode). |
| `[Agent] Taxonomy Version` | string | No | Which taxonomy config version produced this enrichment (from ai_category_config.config_version_id). |
| `[Agent] Evaluated At` | number | No | Epoch milliseconds when this enrichment/evaluation was computed. |
| `[Agent] Score Label` | string | No | Direction-neutral magnitude label derived from score value. Default 5-tier: very_high (>=0.8), high (>=0.6), moderate (>=0.4), low (>=0.2), very_low (>=0.0). Server-side only. |

### Server-Side: Session Evaluation Properties

`[Agent] Session Evaluation` is emitted automatically by the server-side enrichment pipeline — do not send this event from your code.

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Session ID` | string | Yes | Unique session identifier. All events in one conversation share the same session ID. |
| `[Agent] Agent ID` | string | Yes | Identifies which AI agent handled the interaction (e.g., 'support-bot', 'houston'). |
| `[Agent] Customer Org ID` | string | Yes | Organization ID for multi-tenant platforms. Enables account-level group analytics. |
| `[Agent] Evaluation Source` | string | Yes | Source of the evaluation: 'user' (end-user feedback), 'ai' (automated/server pipeline), or 'reviewer' (human expert). |
| `[Agent] Taxonomy Version` | string | Yes | Which taxonomy config version produced this enrichment (from ai_category_config.config_version_id). |
| `[Agent] Evaluated At` | number | Yes | Epoch milliseconds when this enrichment/evaluation was computed. |
| `[Agent] Overall Outcome` | string | Yes | Session outcome classification: 'success', 'partial_success', 'failure', 'abandoned', 'response_provided', etc. |
| `[Agent] Turn Count` | number | Yes | Number of conversation turns in this session. |
| `[Agent] Session Total Tokens` | number | No | Total LLM tokens consumed across all turns in this session. |
| `[Agent] Session Avg Latency Ms` | number | No | Average AI response latency in milliseconds across the session. |
| `[Agent] Request Complexity` | string | No | Complexity classification of the user's request: 'simple', 'moderate', 'complex', or 'ambiguous'. |
| `[Agent] Has Task Failure` | boolean | Yes | Whether the agent failed to complete the user's request. |
| `[Agent] Has Negative Feedback` | boolean | Yes | Whether the user expressed dissatisfaction during the session. |
| `[Agent] Has Technical Failure` | boolean | Yes | Whether technical errors occurred (tool timeouts, API failures, etc.). |
| `[Agent] Has Data Quality Issues` | boolean | Yes | Whether the AI output had data quality problems (wrong data, hallucinations, etc.). |
| `[Agent] Models Used` | string[] | No | LLM models used in this session. JSON array of strings. |
| `[Agent] Root Agent Name` | string | No | Entry-point agent in multi-agent flows. |
| `[Agent] Agent Chain Depth` | number | No | Number of agents in the delegation chain. |
| `[Agent] Task Failure Type` | string | No | Specific failure type when has_task_failure is true (e.g., 'wrong_answer', 'unable_to_complete'). |
| `[Agent] Technical Error Count` | number | No | Count of technical errors that occurred during the session. |
| `[Agent] Error Categories` | string[] | No | Categorized error types (e.g., 'chart_not_found', 'timeout'). JSON array of strings. |
| `[Agent] Behavioral Patterns` | string[] | No | Detected behavioral anti-patterns (e.g., 'retry_storm', 'clarification_loop', 'early_abandonment'). JSON array of strings. |
| `[Agent] Session Cost USD` | number | No | Total LLM cost in USD for this AI session (aggregated from per-message costs). |
| `[Agent] Enrichment Cost USD` | number | No | Cost in USD of running the enrichment pipeline's LLM inference for this session. Distinct from the session's own LLM cost. |
| `[Agent] Quality Score` | number | No | Overall quality score (0.0-1.0) computed by the enrichment pipeline for this session. |
| `[Agent] Sentiment Score` | number | No | User sentiment score (0.0-1.0) inferred from the conversation by the enrichment pipeline. |
| `[Agent] Task Failure Reason` | string | No | Explanation of why the task failed when has_task_failure is true (e.g., 'chart data source unavailable'). |
| `[Agent] Agent Chain` | string[] | No | Serialized JSON array of agent IDs representing the delegation chain in multi-agent flows. |
| `[Agent] Project ID` | string | No | Amplitude project ID that owns the AI session being evaluated. |
| `[Agent] Has User Feedback` | boolean | Yes | Whether the session received explicit user feedback (thumbs up/down, rating). |
| `[Agent] User Score` | number | No | Aggregate user feedback score for the session (0.0-1.0). Present only when has_user_feedback is true. |
| `[Agent] Agent Version` | string | No | Agent code version (e.g., 'v4.2'). Enables version-over-version quality comparison. |
| `[Agent] Agent Description` | string | No | Human-readable description of the agent's purpose (e.g., 'Handles user chat requests via OpenAI GPT-4o'). Enables observability-driven agent registry from event streams. |

### Server-Side: Topic Classification Properties

`[Agent] Topic Classification` is emitted automatically by the server-side enrichment pipeline — do not send this event from your code.

| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `[Agent] Session ID` | string | Yes | Unique session identifier. All events in one conversation share the same session ID. |
| `[Agent] Agent ID` | string | Yes | Identifies which AI agent handled the interaction (e.g., 'support-bot', 'houston'). |
| `[Agent] Customer Org ID` | string | Yes | Organization ID for multi-tenant platforms. Enables account-level group analytics. |
| `[Agent] Evaluation Source` | string | Yes | Source of the evaluation: 'user' (end-user feedback), 'ai' (automated/server pipeline), or 'reviewer' (human expert). |
| `[Agent] Taxonomy Version` | string | Yes | Which taxonomy config version produced this enrichment (from ai_category_config.config_version_id). |
| `[Agent] Evaluated At` | number | Yes | Epoch milliseconds when this enrichment/evaluation was computed. |
| `[Agent] Topic` | string | Yes | Which topic model this classification is for (e.g., 'product_area', 'query_intent', 'error_domain'). |
| `[Agent] Selection Mode` | string | Yes | Whether this topic model uses 'single' (MECE) or 'multiple' (multi-label) selection. |
| `[Agent] Primary` | string | No | Primary classification value (e.g., 'charts', 'billing_issues'). |
| `[Agent] Secondary` | string[] | No | Secondary classifications for multi-label topics. JSON array of strings. |
| `[Agent] Subcategories` | string[] | No | Subcategories for finer classification within the primary topic (e.g., 'TREND_ANALYSIS', 'WRONG_EVENT'). JSON array of strings. |

---

## Sending Events Without the SDK

The `[Agent]` event schema is not tied to this SDK. If your stack is Go, Java, Rust, or any language without an Amplitude AI SDK, you can send the same events directly via Amplitude's ingestion APIs.

### What the SDK handles for you

When you use this SDK, the following are managed automatically. If you send events directly, you are responsible for these:

| Concern | SDK behavior | DIY equivalent |
|---|---|---|
| **Session ID** | Generated once per `Session` and propagated to every event | Generate a UUID per conversation and include it as `[Agent] Session ID` on every event |
| **Deduplication** | Automatic `insert_id` on each event | Set a unique `insert_id` per event to prevent duplicates on retry |
| **Property prefixing** | All properties are prefixed with `[Agent]` | You must include the `[Agent] ` prefix in every property name |
| **Cost / token calculation** | Auto-computed from model and token counts | Compute and send `[Agent] Cost USD`, `[Agent] Input Tokens`, etc. yourself |
| **Server-side enrichment** | `[Agent] Session Evaluation`, `[Agent] Topic Classification`, and `[Agent] Score` events are emitted automatically by the enrichment pipeline after `[Agent] Session End` | These fire automatically — you do **not** need to send them. Just send the SDK-level events and close the session with `[Agent] Session End`. |

### Ingestion methods

| Method | Best for | Docs |
|---|---|---|
| **HTTP V2 API** | Real-time, low-to-medium volume | [HTTP V2 API docs](https://www.docs.developers.amplitude.com/analytics/apis/http-v2-api/) |
| **Batch Event Upload API** | High volume, backfills | [Batch API docs](https://www.docs.developers.amplitude.com/analytics/apis/batch-event-upload-api/) |
| **Amazon S3 Import** | Bulk historical import, warehouse-first workflows | [S3 Import docs](https://amplitude.com/docs/data/source-catalog/amazon-s3) |

### Minimal HTTP API example

```bash
curl -X POST https://api2.amplitude.com/2/httpapi \
  -H 'Content-Type: application/json' \
  -d '{
    "api_key": "YOUR_API_KEY",
    "events": [
      {
        "event_type": "[Agent] User Message",
        "user_id": "user-42",
        "insert_id": "evt-unique-id-1",
        "event_properties": {
          "[Agent] Session ID": "sess-abc123",
          "[Agent] Trace ID": "trace-def456",
          "[Agent] Turn ID": 1,
          "[Agent] Agent ID": "support-bot",
          "[Agent] Message ID": "msg-001"
        }
      },
      {
        "event_type": "[Agent] AI Response",
        "user_id": "user-42",
        "insert_id": "evt-unique-id-2",
        "event_properties": {
          "[Agent] Session ID": "sess-abc123",
          "[Agent] Trace ID": "trace-def456",
          "[Agent] Turn ID": 1,
          "[Agent] Message ID": "msg-002",
          "[Agent] Agent ID": "support-bot",
          "[Agent] Model Name": "gpt-4o",
          "[Agent] Provider": "openai",
          "[Agent] Latency Ms": 1203,
          "[Agent] Input Tokens": 150,
          "[Agent] Output Tokens": 420,
          "[Agent] Cost USD": 0.0042
        }
      }
    ]
  }'
```

Refer to the [Event Schema](#event-schema) tables above for required and optional properties per event type.

---

## Register Event Schema in Your Data Catalog

Amplitude's data catalog is **per-project**. When your application sends `[Agent]` events, Amplitude auto-discovers the event types and property names — but they appear without descriptions, types, or required flags. The SDK ships a CLI tool that populates your project's data catalog with the full `[Agent]` event schema.

### Prerequisites

- **Amplitude Enterprise plan** (required for [Taxonomy API](https://amplitude.com/docs/apis/analytics/taxonomy) access)
- **API Key and Secret Key** from **Settings > Projects** in Amplitude

### Usage

```bash
# Register the full [Agent] event schema in your project
amplitude-ai-register-catalog --api-key YOUR_API_KEY --secret-key YOUR_SECRET_KEY

# Preview what would be registered (no API calls)
amplitude-ai-register-catalog --dry-run

# EU residency endpoint
amplitude-ai-register-catalog --api-key KEY --secret-key SECRET --eu
```

The command is **idempotent** — safe to re-run. It creates events and properties that don't exist yet and updates descriptions for those that do. Run it once after installing the SDK, and again after SDK upgrades to pick up any new events or properties.

### What gets registered

All 10 `[Agent]` event types and their 225+ properties, including:
- Event descriptions explaining what each event captures
- Property types (string, number, boolean)
- Required flags for critical properties
- Array type annotations for list-valued properties

After registration, your team can browse the full schema in **Amplitude > Data > Events** with descriptions visible inline.

---

## Testing

`MockAmplitudeAI` is a drop-in replacement that captures events in-memory instead of sending them over the network. It supports all SDK features including `agent()` and `session()`:

```python
from amplitude_ai import MockAmplitudeAI

mock = MockAmplitudeAI()
agent = mock.agent("test-bot", user_id="u1")

with agent.session("s1") as s:
    s.new_trace()
    s.track_user_message(content="Hello")
    s.track_ai_message(content="Hi!", model="gpt-4o", provider="openai", latency_ms=100.0)

assert len(mock.events) == 3  # user msg + ai msg + session end
mock.assert_event_tracked("[Agent] User Message", user_id="u1")
mock.assert_event_tracked("[Agent] AI Response", **{"[Agent] Model Name": "gpt-4o"})
mock.reset()
```

Filter and assert by session or agent:

```python
mock.events_for_session("s1")          # list of events for that session
mock.events_for_agent("test-bot")      # list of events for that agent
mock.assert_session_closed("s1")       # assert [Agent] Session End exists
```


### Disabling Tracking in Tests

If you don't need to assert on events and just want tracking to be a no-op, use `MockAmplitudeAI` without inspecting `events`. It never makes network calls. Alternatively, skip SDK initialization entirely in your test config.

---

## Serverless Environments

The SDK auto-detects serverless environments (AWS Lambda, Vercel, Netlify, Google Cloud Functions, Azure Functions, Cloudflare Pages). When detected, `agent.session()` context managers automatically flush all pending events on exit — no explicit `ai.flush()` needed. You can also control this explicitly via the `auto_flush` parameter:

```python
# Auto-detected: flushes automatically in serverless, skips in long-running servers
with agent.session(user_id=uid, session_id=sid) as s:
    ...

# Explicit control:
with agent.session(user_id=uid, session_id=sid, auto_flush=True) as s:   # always flush
    ...
with agent.session(user_id=uid, session_id=sid, auto_flush=False) as s:  # never flush
    ...
```

If you track events **outside** of `agent.session()`, you still need `ai.flush()` before your handler returns:

```python
def handler(event, context):
    agent = ai.agent("lambda-bot")
    with agent.session(user_id=event["user_id"]) as s:
        s.new_trace()
        s.track_user_message(content=event["message"])
        ai_msg = s.track_ai_message(
            content=generate_response(event["message"]),
            model="gpt-4o", provider="openai", latency_ms=500.0,
        )
    ai.flush()  # block until all events are delivered
    return {"statusCode": 200}
```

`flush()` returns a list of `Future` objects. Call `.result()` on each to block until delivery completes. For long-running servers, the Amplitude SDK flushes automatically on a timer; explicit `flush()` is only needed for short-lived processes.

---

## Error Handling and Reliability

- **Delivery failures are caught.** All `track_*` methods catch `amplitude.track()` exceptions internally and log errors. Your application code won't break if Amplitude is unreachable. Events that fail to send are logged at `ERROR` level but do not propagate exceptions to your code. Note: malformed inputs may still raise during preprocessing (e.g., validation or privacy sanitization) before the event is sent.

- **Events are buffered and retried automatically.** The SDK delegates to the [Amplitude Python SDK](https://github.com/amplitude/Amplitude-Python)'s event pipeline, which buffers events in memory and retries failed deliveries with exponential backoff. You don't need to implement retry logic.

- **Delivery status callback.** Use `on_event_callback` in `AIConfig` to monitor delivery status per event:

```python
def on_event(event, code, message):
    if code != 200:
        logging.warning(f"Event delivery failed: {code} {message}")

ai = AmplitudeAI(amplitude=amplitude, config=AIConfig(on_event_callback=on_event))
```

- **`flush()` blocks until delivery.** Returns `Future` objects; call `.result()` to block until all buffered events are sent. Required in serverless environments; optional in long-running processes where the SDK flushes automatically on a timer.

- **`shutdown()` for clean exit.** Call `ai.shutdown()` when your application exits to flush remaining events and release resources. Only necessary if the SDK created the Amplitude instance internally (via `api_key=`); if you passed in your own `amplitude=` instance, manage its lifecycle yourself.

---

## Under the Hood

Built on the official [Amplitude-Python](https://github.com/amplitude/Amplitude-Python) SDK (`Amplitude`, `BaseEvent`, `track()`, `flush()`, `shutdown()`).

### Content Storage (`$llm_message`)

Message content (user messages and AI responses) is stored in a `$llm_message` nested property inside the event as `{"text": "..."}`. Content is preserved at full length with no truncation or size limits — the SDK bypasses the base Amplitude SDK's per-property string truncation for this property, and the server-side ingestion pipeline whitelists it as well.

This applies only in `full` content mode. In `metadata_only` mode, no content is sent.

> **Legacy note:** SDK versions prior to this change split long content into `c0`..`c7` chunk sub-properties. The LLM session viewer and enrichment pipeline continue to handle both formats transparently.

### Context Propagation

The SDK uses Python's `contextvars` module to propagate session context (session ID, trace ID, agent ID, description, turn counter) across function calls. This is how provider wrappers, `@tool`, `@observe`, and the FastAPI middleware all share the same session without explicit parameter threading.

**How it works:**

- **Sync code**: context flows naturally through the call stack.
- **`asyncio.create_task()`**: Python 3.10+ automatically copies the parent context into the new task. No action needed; the child task inherits the active session.
- **`ThreadPoolExecutor`**: threads do **not** inherit `ContextVar` state. If you offload work to a thread pool within a session, you must explicitly copy the context:

```python
import contextvars
from concurrent.futures import ThreadPoolExecutor

with agent.session() as s:
    s.track_user_message(content="hello")
    ctx = contextvars.copy_context()
    with ThreadPoolExecutor() as pool:
        # ctx.run() ensures the thread sees the active session
        future = pool.submit(ctx.run, my_blocking_function, arg1, arg2)
```

Without `ctx.run()`, the thread sees no active session and provider wrappers fall back to the global `ToolCallTracker` config (if set).

**Nesting**: sessions nest correctly. An inner `with agent.session()` or `@observe` call saves and restores the outer context on exit. No session leaks.

### Debug Logging

The SDK uses the Amplitude SDK's built-in logger. Enable verbose output to see every event as it's tracked:

```python
import logging

# Option 1: Enable via Amplitude's configuration
amplitude.configuration.logger = logging.getLogger("amplitude")
amplitude.configuration.logger.setLevel(logging.DEBUG)

# Option 2: Set min_id_length to bypass validation in dev
amplitude.configuration.min_id_length = 1
```


### Supported Integrations

The SDK provides broad coverage across LLM providers, agent frameworks, and observability standards:

**Provider Wrappers** (drop-in replacements with full field coverage):

| Provider | Class | Install Extra | Key Capabilities |
|----------|-------|---------------|------------------|
| OpenAI | `OpenAI` | `[openai]` | Chat, streaming, reasoning (o1/o3/o4), function calling, prompt caching |
| OpenAI (async) | `AsyncOpenAI` | `[openai]` | Same as OpenAI — for `openai.AsyncOpenAI` users (FastAPI, pydantic_ai, etc.) |
| Anthropic | `Anthropic` | `[anthropic]` | Messages, streaming, extended thinking, tool_use, prompt caching |
| Google Gemini | `Gemini` | `[gemini]` | Generate content, streaming, thinking models |
| Azure OpenAI | `AzureOpenAI` | `[azure]` | Same as OpenAI, Azure-hosted |
| AWS Bedrock | `Bedrock` | `[bedrock]` | Converse API, streaming, cross-provider (Claude, Titan, etc.) |
| Mistral | `Mistral` | `[mistral]` | Chat, streaming, function calling, reasoning |

**Framework Integrations** (callbacks and processors for popular frameworks):

| Framework | Class | Install Extra | Integration Pattern |
|-----------|-------|---------------|---------------------|
| LangChain | `AmplitudeCallbackHandler` | `[langchain]` | Callback handler for chains and agents |
| LlamaIndex | `AmplitudeLlamaIndexHandler` | `[llamaindex]` | Callback handler for queries and retrievals |
| OpenTelemetry | `AmplitudeAgentExporter` | `[otel]` | OTEL span exporter (GenAI semantic conventions) |
| OpenAI Agents SDK | `AmplitudeTracingProcessor` | `[openai-agents]` | Tracing processor for multi-agent workflows |
| Anthropic tool_use | `AmplitudeToolLoop` | `[anthropic]` | Managed multi-turn tool_use loop |
| CrewAI | `AmplitudeCrewAIHooks` | `[crewai]` | Event listener hooks for LLM and tool calls |
| Claude Agent SDK | `ClaudeAgentSDKTracker` | `[anthropic]` | PreToolUse/PostToolUse hooks + message stream processing with real tool latency |

**Managed / Hosted Agent Adapters** (for architectures where LLM calls happen server-side):

| Platform | Class | Install Extra | Integration Pattern |
|----------|-------|---------------|---------------------|
| Anthropic Managed Agents | `ManagedAgentTracker` | `[anthropic]` | Polls session events, maps to `track_user_message` / `track_ai_message` / `track_tool_call` |

See `examples/anthropic_managed_agents_example.py` and the coding agent guide (`amplitude-ai.md`, Step 3f) for full usage.

### Claude Agent SDK

Track tool calls with execution latency and AI messages from Claude Agent SDK.

**Essential fields:** `agentId` (on `ai.agent()`) identifies which AI feature produced the events — it maps to the LLM Usage Application Registry. `user_id` + `session_id` (on `agent.session()`) tie all events into a single user conversation, powering funnels, retention, and conversation views. The session automatically emits `[Agent] Session Start` and `[Agent] Session End`.

```python
from amplitude import Amplitude
from amplitude_ai import AmplitudeAI
from amplitude_ai.integrations.claude_agent_sdk import ClaudeAgentSDKTracker

ai = AmplitudeAI(amplitude=Amplitude("YOUR_API_KEY"))
agent = ai.agent("code-reviewer")
tracker = ClaudeAgentSDKTracker()

with agent.session(user_id="u1", session_id="s1") as session:
    for message in query(
        prompt="Analyze this codebase",
        options=ClaudeAgentOptions(hooks=tracker.hooks(session)),
    ):
        tracker.process(session, message)
```

`tracker.hooks(session)` returns PreToolUse/PostToolUse hooks that track each tool execution with precise latency. `tracker.process(session, message)` processes streamed messages to track AI responses and user messages. See `examples/claude_agent_sdk_example.py` for a complete example.

**Coverage summary:** The OTEL GenAI bridge provides baseline coverage for any OTEL-instrumented provider or framework. The 6 dedicated provider wrappers add full field coverage (cache tokens, reasoning, TTFB, streaming). The framework integrations capture agent-level structure (multi-agent, tool loops, handoffs). Together, this covers the vast majority of production LLM deployments.

### Package Structure

```
amplitude-ai/
├── amplitude_ai/
│   ├── __init__.py          # Public API
│   ├── client.py            # AmplitudeAI, BoundAgent, TenantHandle, Session
│   ├── config.py            # AIConfig, ContentMode
│   ├── context.py           # SessionContext, ContextVar propagation
│   ├── exceptions.py        # AmplitudeAIError, ValidationError, etc.
│   ├── middleware.py         # FastAPI/Starlette AmplitudeAIMiddleware
│   ├── patching.py          # Zero-code monkey-patching (patch_openai, etc.)
│   ├── testing.py           # MockAmplitudeAI
│   ├── wrappers.py          # wrap() convenience function
│   ├── core/
│   │   ├── tracking.py      # Event tracking functions
│   │   ├── privacy.py       # Privacy/redaction
│   │   ├── enrichments.py   # SessionEnrichments, TopicClassification, RubricScore
│   │   └── decorators.py    # @tool and @observe decorators
│   ├── providers/           # OpenAI, Anthropic, Gemini, Azure, Bedrock, Mistral
│   ├── integrations/        # LangChain, LlamaIndex, OTEL, OpenAI Agents, Anthropic tools, CrewAI
│   └── utils/               # Cost (genai-prices), tokens, streaming
└── README.md
```

---

## Troubleshooting

| Symptom | Cause | Fix |
| --- | --- | --- |
| No events in Amplitude | API key not set or incorrect | Run `amplitude-ai-doctor` — it checks `AMPLITUDE_AI_API_KEY` and reports a fix command |
| Events tracked but `[Agent] Cost USD` is $0 | Model not in the pricing database, or `total_cost_usd` not passed | Pass `total_cost_usd` explicitly, or install `genai-prices`: `pip install genai-prices` |
| `patch()` doesn't instrument calls | `patch()` called after the provider client was created | Call `patch()` before importing or instantiating provider clients |
| Session context missing on events | LLM calls made outside a `with agent.session()` block | Wrap your LLM calls inside `with agent.session() as s:` |
| `flush()` hangs or times out in serverless | Process exits before flush completes | Call `ai.flush()` before returning from your Lambda/Cloud Function handler |
| `@tool` / `@observe` not emitting events | No active session context | Ensure the decorated function is called within `with agent.session()` or another `@observe` scope |
| Import error for provider wrapper | Optional dependency not installed | Install the provider extra: `pip install 'amplitude-ai[openai]'` (or `[anthropic]`, `[gemini]`, etc.) |

Run `amplitude-ai-doctor` for automated environment diagnostics with fix suggestions.

---

## For AI Coding Agents

This SDK is designed to be discovered and used by AI coding agents (Cursor, Claude Code, Windsurf, Cline, Copilot, or any MCP-compatible assistant). The following files ship with the package to help agents understand and integrate the SDK without reading the full README:

| File | Purpose |
| --- | --- |
| `amplitude-ai.md` | **Primary guide** — self-contained 4-phase instrumentation workflow and full API reference |
| `AGENTS.md` | Machine-readable decision tree, canonical patterns, MCP surface, gotchas, and CLI reference |
| `llms.txt` | Compact discovery file listing tools, resources, and event names |
| `llms-full.txt` | Extended reference with full API signatures, provider coverage matrix, and common error resolutions |
| `mcp.schema.json` | Structured JSON describing the MCP server's tools, resources, and prompt |

Run `amplitude-ai-mcp` to start the MCP server (standard stdio protocol). Any MCP-compatible AI coding agent can call tools like `scan_project` to analyze your codebase, `instrument_file` to transform source files, `validate_file` to detect uninstrumented LLM call sites, and `generate_verify_test` to produce CI tests.

CLI commands for coding agent workflows:

```bash
amplitude-ai                     # Print setup prompt for your AI coding agent
amplitude-ai --print-guide       # Print the full amplitude-ai.md guide to stdout
amplitude-ai mcp                 # Start the MCP server
amplitude-ai doctor              # Validate environment and event pipeline
amplitude-ai status              # Show SDK version, installed providers, and env config
amplitude-ai-register-catalog    # Push event catalog to Amplitude Data
```

---

## Requirements

- Python >= 3.10
- `amplitude-analytics >= 1.0.0`

Optional provider extras:

```
pip install "amplitude-ai[openai]"           # OpenAI / Azure OpenAI
pip install "amplitude-ai[anthropic]"        # Anthropic Claude (provider + tool_use loop)
pip install "amplitude-ai[gemini]"           # Google Gemini
pip install "amplitude-ai[bedrock]"          # AWS Bedrock
pip install "amplitude-ai[mistral]"          # Mistral
pip install "amplitude-ai[langchain]"        # LangChain
pip install "amplitude-ai[llamaindex]"       # LlamaIndex
pip install "amplitude-ai[otel]"             # OpenTelemetry
pip install "amplitude-ai[openai-agents]"    # OpenAI Agents SDK
pip install "amplitude-ai[crewai]"           # CrewAI
pip install "amplitude-ai[tokens]"           # tiktoken (accurate token counting; falls back to estimation if absent)
pip install "amplitude-ai[all]"              # Everything (includes tiktoken)
```


## Reporting Issues

Found a bug or have a feature request? File an issue on the public tracker:

- **Python SDK issues**: [github.com/amplitude/Amplitude-Python/issues](https://github.com/amplitude/Amplitude-Python/issues) (use the `amplitude-ai` label)
- **General questions**: [Amplitude Support](https://help.amplitude.com)
