Agent analytics for Amplitude
Project description
amplitude-ai
Agent analytics for Amplitude. Track every LLM call as events in your Amplitude project, then build funnels, cohorts, and retention charts across AI and product behavior.
pip install amplitude-ai
from amplitude import Amplitude
import amplitude_ai
amplitude_ai.patch(amplitude=Amplitude("YOUR_API_KEY"))
# Your existing code -- unchanged
import openai
response = openai.OpenAI(api_key="sk-...").chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is retention?"}],
)
# [Agent] User Message + [Agent] AI Response now in your Amplitude project
One call auto-detects and patches every installed provider (OpenAI, Anthropic, Azure OpenAI, Gemini, Mistral). It also instruments libraries that use these SDKs under the hood (LiteLLM, Pydantic AI, LangChain).
Want more control? See Choose Your Integration Tier below.
Quickstart (5 minutes)
- Install:
pip install amplitude-ai - Get your API key: In Amplitude, go to Settings > Projects and copy the API key.
- Add two lines to the top of your app (see the example above).
- Run your app. Any
openai,anthropic,mistralai, orgoogle.genaicall is now instrumented automatically. - Open Amplitude > Events. You should see
[Agent] AI Responsewithin 30 seconds.
User identity is set per-request: pass amplitude_user_id="..." on individual LLM calls, use Session for multi-turn conversations, or use AmplitudeAIMiddleware in web apps. See User Identity below.
To verify locally before checking Amplitude, add debug=True:
amplitude_ai.patch(amplitude=Amplitude("YOUR_API_KEY"), debug=True)
# Prints: [amplitude-ai] [Agent] AI Response | model=gpt-4o | tokens=847 | cost=$0.0042 | latency=1,203ms
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 for proxy setups (LiteLLM, Portkey, custom gateways). See Path B. |
| 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 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 zero-code path 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 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 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, 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.
Choose Your Integration Tier
If you're adding to an existing codebase, use Zero-code or Wrap. If you're starting fresh, use Swap import. All three auto-track [Agent] User Message and [Agent] AI Response with full token, cost, and latency properties. Combine any of them with BoundAgent + Session to unlock the full event model: tool calls, scoring, implicit feedback, session lifecycle, and enrichment.
| Tier | When to use | How |
|---|---|---|
| Zero-code | You want tracking without touching existing code | amplitude_ai.patch(amplitude=amp) |
| Wrap | You've already created a client | wrap(client, amplitude=amp) |
| Swap import | Starting fresh or want richest field coverage | from amplitude_ai import OpenAI |
| Full control | Multi-agent, custom scoring, session lifecycle | BoundAgent + Session |
| FastAPI middleware | Web app, auto-session per request | AmplitudeAIMiddleware |
Zero-code patches provider modules so existing calls are instrumented automatically:
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:
amplitude_ai.patch_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)
Zero-code patching is available for OpenAI, 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.
No-code setup (CLI wrapper) — instrument your application using only environment variables, without modifying any Python source code:
pip install amplitude-ai
AMPLITUDE_AI_API_KEY=xxx AMPLITUDE_AI_AUTO_PATCH=true amplitude-ai-instrument python app.py
The CLI detects installed LLM providers, patches them, then runs your command. All standard openai, anthropic, mistralai, and google.genai calls in your app are instrumented automatically. Optional environment variables:
| 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 |
Wrap instruments a client you've already created (OpenAI, Anthropic, Azure OpenAI):
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:
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:
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. For the zero-code tier, pass debug=True to patch() (see Quickstart). For full-control usage, set it on AIConfig:
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:
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):
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:
ai = AmplitudeAI(api_key="unused", config=AIConfig(debug=True, dry_run=True, validate=True))
Inspect current configuration at any time:
ai.status()
# {"content_mode": "full", "debug": False, "dry_run": False,
# "redact_pii": False, "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:
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):
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:
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.
Contents
- Quickstart
- Current Limitations
- Get Started
- Core Concepts
- Integration Patterns
- Going Deeper
- API Reference
- Usage Examples
- Event Schema
- Testing
- Serverless Environments
- Error Handling and Reliability
- Under the Hood
- Documentation
- Requirements
Get Started
Pick the path that matches where you are today. Both converge on the same analytics: sessions, scoring, enrichments, funnels across product and AI events.
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: Swap your LLM import
pip install "amplitude-ai[openai]" # or [anthropic], [gemini], [bedrock], [mistral]
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)
# Use the wrapped client exactly like the original
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 now appear in your Amplitude project
# alongside your existing product events
That's it. You're already getting [Agent] User Message and [Agent] AI Response events with model, tokens, cost, and latency. Everything below is progressive enhancement for when you need session grouping, multi-agent hierarchy, or enrichments.
Step 2: Add session context (optional)
Wrap your code in agent.session(). The provider wrapper call itself doesn't change. It automatically inherits session_id, trace_id, agent_id, and turn_id from the active session via Python's contextvars.
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?")
# Same wrapper call as Step 1 -- no amplitude_user_id needed
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is retention?"}],
)
# NOW emits with session_id, trace_id, agent_id, turn_id
s.score(name="helpful", value=1.0, target_id="...")
# Session auto-ends, server-side enrichment kicks in
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:
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, 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.
# 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 at each step:
| Step | Events | Key fields | What's new |
|---|---|---|---|
| Swap import | [Agent] User Message, [Agent] AI Response |
model, provider, tokens (input/output/reasoning/cache), cost (cache-aware), latency, TTFB, system prompt, temperature, top_p, max_output_tokens, is_streaming, reasoning content, finish_reason, errors, implicit feedback (is_regeneration, is_edit, was_copied), file attachments | AI events appear alongside product events. Build funnels across both. |
| + session context | + [Agent] Session End, [Agent] Score |
+ session_id, trace_id, turn_id, agent_id, agent_version, env, abandonment_turn | Session grouping, scoring, abandonment analysis, server-side enrichments (when content_mode=full). Cohort by session quality, funnel from product events through AI sessions. Compare agent versions. |
| + manual track_* calls | + [Agent] Tool Call, [Agent] Embedding, [Agent] Span, [Agent] Session Enrichment |
+ event linking (parent_message_id, parent_span_id), custom properties, multi-agent fields (parent_agent_id) | Full event graph, customer-provided enrichments, multi-agent hierarchies. |
Next: Scoring | Enrichments | All providers | Privacy
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 spans and maps them to Amplitude [Agent] events.
Step 1: Add the bridge
pip install "amplitude-ai[otel]"
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:
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), basic cost, latency, system prompt, temperature, top_p, max_output_tokens, content (if opted-in), errors | Cache tokens, reasoning content/tokens, TTFB, streaming detection, implicit feedback, file attachments, event graph linking (parent_message_id) |
| + session context | + [Agent] Session End, [Agent] Score |
+ session_id, trace_id, turn_id, agent_id, 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 | + cache-aware cost, 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. |
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 | Scoring | Privacy | 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:
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 the zero-code tier):
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).
Initialization Options
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). |
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 for the full property reference. Every tracking method returns a unique ID (message_id, invocation_id, or span_id) that you can use to link related events into a graph. See 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):
{
"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": "0.3.0",
"[Agent] Runtime": "python"
}
}
A [Agent] Session Evaluation event (server-generated after session closes, when content_mode="full"):
{
"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).
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.
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,
)
# --- When conversation truly ends (or rely on server-side 30min idle timeout) ---
agent.track_session_end()
Key differences from Pattern A:
BoundAgentcarries all context (user_id,agent_id,session_id,env, etc.) without awithblock- Generate a new
trace_idfor each user-message-to-AI-response cycle - Pass explicit
turn_idvalues (or omit to auto-increment per session) - Call
track_session_end()explicitly when the conversation ends, or let the server auto-close after 30 minutes of inactivity
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.
orchestrator = ai.agent("orchestrator", env="production")
with orchestrator.session(user_id="u1") as s:
s.track_user_message(content="Compare our pricing to competitors")
# Fan out to sub-agents
researcher = orchestrator.child("researcher")
writer = orchestrator.child("writer")
# researcher.parent_agent_id == "orchestrator" (automatic)
# researcher inherits user_id, env, session_id, trace_id, groups
researcher.track_tool_call(
tool_name="web_search", latency_ms=500, success=True,
session_id=s.session_id, trace_id=s.trace_id,
)
# Fan in: orchestrator synthesizes results
s.track_ai_message(
content="Based on research...",
model="gpt-4o",
provider="openai",
latency_ms=500,
)
See 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_enrichedgives the same analytics output asfull. The difference is who runs the enrichment. Infullmode, Amplitude does it for you. Incustomer_enrichedmode, 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 automatically chunked to fit within Amplitude's per-property size limits (up to 8KB per message, with balanced head/tail truncation for longer content). See Content Storage for details.
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 |
False |
Scrub emails, phone numbers, credit cards, SSNs before sending. |
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. |
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, the OTEL GenAI spec marks message content as Opt-In. Our PrivacyConfig 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 PrivacyConfig | 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:
agent = ai.agent(
"support-bot",
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, 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:
# 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:
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 Context Manager. agent.session() returns a context manager that auto-calls track_session_end when the block exits (even on exception):
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:
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:
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:
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:
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_variantand 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] Scorewithsource="ai") do not yet inherit context keys. To segment server-generated quality scores by experiment arm, use Amplitude Derived Properties to extract from[Agent] Contexton 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,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:
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:
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:
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] Scoreby[Agent] Agent IDto 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 IDto 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 Evaluationsessions wherehas_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) to compare latency, error rate, and cost across agent roles (router, retriever, executor).
Event Linking
Every tracking call returns 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 |
Link events together:
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").
# 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] Scoreevents. A single chart shows all three side by side. No joins, no separate tables. Filter by[Agent] Evaluation Sourceto compare signal types. Filter by[Agent] Agent IDfor 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:
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:
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.
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"},
),
# L2 subcategory for finer classification
"error_domain": TopicClassification(l1="TAX", l2="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.
@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 # L2 subcategory — 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. |
l2 |
str |
Subcategory for finer classification. E.g., "WRONG_EVENT", "HALLUCINATION", "UNDERSPECIFIED". |
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:
from amplitude import Amplitude
from amplitude_ai import (
AmplitudeAI, SessionEnrichments, TopicClassification, RubricScore,
EvidenceQuote, MessageLabel,
)
amplitude = Amplitude("YOUR_API_KEY")
ai = AmplitudeAI(amplitude=amplitude, content_mode="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).
OpenAI
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
Anthropic
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
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
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
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
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
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
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.
pip install "amplitude-ai[openai-agents]"
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.
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.
pip install "amplitude-ai[crewai]"
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 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:
# 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
All tracking methods return a UUID string (message_id, invocation_id, or span_id) for event linking.
| 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
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:
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)
)
Implicit Feedback
Track behavioral signals that indicate whether a response met the user's need, without requiring explicit ratings:
# 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.
# 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):
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:
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):
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:
# 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):
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
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
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.):
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:
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:
@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:
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.
@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:
# 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:
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:
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, l1, l2, 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 for tier details.
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():
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:
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
Failing to call
flush()before the handler returns is the #1 integration error in serverless environments. The process terminates before the event buffer is sent, and events are silently lost. This applies to AWS Lambda, Cloud Functions, Azure Functions, and any runtime where the process lifecycle is managed by the platform.
Always call flush() before returning:
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
-
Tracking calls never throw. All
track_*methods catch exceptions internally and log errors. Your application code won't break if Amplitude is unreachable or if you pass unexpected values. Events that fail to send are logged atERRORlevel but do not propagate exceptions to your code. -
Events are buffered and retried automatically. The SDK delegates to the Amplitude Python SDK'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_callbackinAIConfigto monitor delivery status per event:
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. ReturnsFutureobjects; 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. Callai.shutdown()when your application exits to flush remaining events and release resources. Only necessary if the SDK created the Amplitude instance internally (viaapi_key=); if you passed in your ownamplitude=instance, manage its lifecycle yourself.
Under the Hood
Built on the official Amplitude-Python SDK (Amplitude, BaseEvent, track(), flush(), shutdown()).
Content Storage ($llm_message Chunking)
Message content (user messages and AI responses) is stored in a $llm_message nested property inside the event. The Amplitude SDK truncates individual string properties at 1024 characters. To preserve full content, the SDK automatically chunks text into multiple sub-properties:
- Short content (<=1024 chars): stored as
{"text": "..."}(no chunking, fully backward-compatible). - Long content (>1024 chars): split into
{"c0": "...", "c1": "...", ..., "n": chunk_count}. Up to 8 chunks of 1024 chars each (8KB max). - Truncated content (>8KB): the first 4 chunks hold the beginning and the last 4 hold the end, with a marker like
[...12,345 chars truncated...]at the boundary. The original length is preserved in a"len"field.
The LLM session viewer and enrichment pipeline reassemble chunks automatically. If you query raw events directly (e.g., via SQL export), you'll see c0..c7 sub-properties under $llm_message for long responses.
This chunking applies only in full content mode. In metadata_only mode, no content is sent. System prompts and reasoning content follow the same chunking rules.
Context Propagation
The SDK uses Python's contextvars module to propagate session context (session ID, trace ID, agent ID, 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 inheritContextVarstate. If you offload work to a thread pool within a session, you must explicitly copy the context:
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:
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 |
| 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 |
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
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[all]" # Everything
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