Rust (Beta)

Beta. The Rust SDK is at 0.0.8. The wire contract against the Raindrop ingestion API is stable and verified end-to-end against the live backend on every push, but the crate API may still change in minor ways before 0.1.0. We recommend pinning the git tag in your Cargo.toml.

Installation

The Rust SDK is hosted on GitHub (not on crates.io). Add it to your Cargo.toml:

[dependencies]
raindrop-ai = { git = "https://github.com/raindrop-ai/raindrop-rust", tag = "v0.0.8" }
tokio = { version = "1", features = ["macros", "rt-multi-thread"] }
serde_json = "1"

Smallest possible program — drops in as src/main.rs and runs:

use raindrop::Client;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Empty / missing key → SDK becomes a no-op (zero HTTP), so you can integrate
    // the SDK code first and add the key later without crashing your app.
    let client = Client::builder()
        .write_key(std::env::var("RAINDROP_WRITE_KEY").unwrap_or_default())
        .build()?;

    // ... use client ...

    client.close().await?;
    Ok(())
}

Source code and releases live in raindrop-ai/raindrop-rust.

The Rust SDK requires Rust 1.88+ (MSRV). It is async-first and uses tokio. Most fallible methods return Result<_, Error> — track_ai, track_event, identify, track_signal, the Interaction mutators (set_input, set_property, set_properties, add_attachments, patch, finish), and Client::flush / Client::close. Propagate errors with ? as you would for any fallible call. The two constructors — Client::begin(...).await and Client::resume_interaction(...) — are infallible: they always return an Interaction (a no-op handle when the client is disabled), so don’t put a ? on those.

Quick Start: Interaction API

The Interaction API uses a simple three-step pattern:

begin() – Create an interaction and log the initial user input
Update – Optionally call set_property, set_properties, set_input, or add_attachments
finish() – Record the AI’s final output and close the interaction

Example: Chat Completion

use std::collections::BTreeMap;

use raindrop::{BeginOptions, Client, FinishOptions};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::builder()
        .write_key(std::env::var("RAINDROP_WRITE_KEY").unwrap_or_default())
        .build()?;

    let mut props = BTreeMap::new();
    props.insert("system_prompt".into(), json!("you are a helpful..."));

    // 1. Start the interaction
    let interaction = client
        .begin(BeginOptions {
            event_id: "evt_123".into(),
            user_id: "user-123".into(),
            event: "chat_message".into(),
            input: "Can you suggest a calm Saturday morning in San Francisco?".into(),
            model: "gpt-4o".into(),
            convo_id: "conv-123".into(),
            properties: props,
            ..Default::default()
        })
        .await;

    // 2. Make the LLM call
    let output = call_llm().await;

    // 3. Finish the interaction
    interaction
        .finish(FinishOptions {
            output,
            ..Default::default()
        })
        .await?;

    client.close().await?;
    Ok(())
}

async fn call_llm() -> String {
    // Your LLM call here.
    String::new()
}

Updating an Interaction

Update an interaction at any point using set_property, set_properties, set_input, or add_attachments:

use raindrop::Attachment;
use serde_json::json;

interaction.set_property("stage", "retrieving").await?;
interaction
    .set_properties(BTreeMap::from([
        ("surface".into(), json!("chat")),
        ("region".into(), json!("us-west")),
    ]))
    .await?;
interaction.set_input("Can you make it a little more local?").await?;
interaction
    .add_attachments(vec![Attachment {
        kind: "text".into(),
        role: "input".into(),
        name: "preferences".into(),
        value: "Prefers coffee, a quiet walk, and no museum stops.".into(),
        ..Default::default()
    }])
    .await?;

Resuming an Interaction

If you no longer have the interaction object returned from begin(), resume it with resume_interaction():

let interaction = client.resume_interaction("evt_123");
interaction.set_property("stage", "follow-up").await?;
interaction
    .finish(FinishOptions {
        output: "Here is a shorter version of that itinerary.".into(),
        ..Default::default()
    })
    .await?;

resume_interaction() recovers an active in-memory interaction created by begin() in the same process. It is not a cross-process restore mechanism. If the event ID is not found in memory, a new interaction handle is created for that ID.

Single-Shot Tracking (`track_ai`)

For simple request-response interactions, you can use track_ai() directly:

use raindrop::AiEvent;

client
    .track_ai(AiEvent {
        user_id: "user-123".into(),
        event: "chat_message".into(),
        input: "Who won the 2023 AFL Grand Final?".into(),
        output: "Collingwood by four points!".into(),
        model: "gpt-4o".into(),
        convo_id: "conv-123".into(),
        properties: BTreeMap::from([
            ("ai.usage.prompt_tokens".into(), json!(10)),
            ("ai.usage.completion_tokens".into(), json!(5)),
        ]),
        ..Default::default()
    })
    .await?;

We recommend using begin() → finish() for new code to take advantage of partial-event buffering and tracing.

Use track_event() for non-AI events:

use raindrop::Event;

client
    .track_event(Event {
        user_id: "user-123".into(),
        event: "session_started".into(),
        properties: BTreeMap::from([("entrypoint".into(), json!("dashboard"))]),
        ..Default::default()
    })
    .await?;

Tracking Signals (Feedback)

Signals capture quality ratings on AI events. Use track_signal() with the same event ID from begin() or track_ai():

Field	Type	Description
`event_id`	`String`	The ID of the AI event you’re evaluating
`name`	`String`	Signal name (e.g. `"thumbs_up"`, `"thumbs_down"`)
`kind`	`String`	One of `SignalKind::{DEFAULT, STANDARD, FEEDBACK, EDIT, AGENT, AGENT_INTERNAL}`. Defaults to `"default"`.
`sentiment`	`String`	`"POSITIVE"` or `"NEGATIVE"`
`comment`	`String`	Merged into `properties.comment` for `feedback` signals
`after`	`String`	Merged into `properties.after` for `edit` signals
`attachment_id`	`String`	Optional attachment ID to associate the signal with
`properties`	`BTreeMap<String, _>`	Additional metadata

use raindrop::{Signal, SignalKind};

client
    .track_signal(Signal {
        event_id: "evt_123".into(),
        name: "thumbs_down".into(),
        kind: SignalKind::FEEDBACK.into(),
        sentiment: "NEGATIVE".into(),
        comment: "Answer was off-topic".into(),
        ..Default::default()
    })
    .await?;

Identifying Users

use raindrop::User;

client
    .identify(User {
        user_id: "user-123".into(),
        traits: BTreeMap::from([
            ("name".into(), json!("Jane")),
            ("email".into(), json!("jane@example.com")),
            ("plan".into(), json!("paid")), // we recommend "free", "paid", "trial"
        ]),
    })
    .await?;

Attachments

Attachments let you include additional context — documents, images, code, or embedded content — with your events. They work with both begin() interactions and track_ai() calls.

Field	Type	Description
`kind`	`String`	`"code"`, `"text"`, `"image"`, or `"iframe"` (serialized as `type` on the wire)
`role`	`String`	`"input"` or `"output"`
`name`	`String`	Optional display name
`value`	`String`	Content or URL
`language`	`String`	Programming language (only meaningful for `"code"` attachments)
`attachment_id`	`String`	Optional UUID. Backend auto-assigns one if empty. Set explicitly to round-trip with `Signal::attachment_id`.

use raindrop::Attachment;

interaction
    .add_attachments(vec![
        Attachment {
            kind: "code".into(),
            role: "input".into(),
            language: "rust".into(),
            name: "example.rs".into(),
            value: "println!(\"hello\");".into(),
            ..Default::default()
        },
        Attachment {
            kind: "text".into(),
            role: "input".into(),
            name: "Additional Info".into(),
            value: "Some extra text".into(),
            ..Default::default()
        },
        Attachment {
            kind: "image".into(),
            role: "output".into(),
            value: "https://example.com/image.png".into(),
            ..Default::default()
        },
        Attachment {
            kind: "iframe".into(),
            role: "output".into(),
            value: "https://example.com/embed".into(),
            ..Default::default()
        },
    ])
    .await?;

The dashboard’s attachment viewer renders text, image, and iframe attachments. code attachments survive ingestion and are searchable, but are not currently displayed in the visual attachments tab.

Configuration

use std::time::Duration;

let client = Client::builder()
    .write_key(std::env::var("RAINDROP_WRITE_KEY").unwrap_or_default())
    .debug(std::env::var("ENV").as_deref() != Ok("production"))
    .partial_flush_interval(Duration::from_secs(1))
    .trace_flush_interval(Duration::from_secs(1))
    .build()?;

Builder method	Description	Default
`.write_key(&str)`	Your Raindrop API key. Empty/missing key → SDK becomes a no-op.	—
`.project_id(&str)`	Send events to a specific project; omit for the default Production project	`"default"`
`.endpoint(&str)`	Override the API endpoint	`https://api.raindrop.ai/v1/`
`.debug(bool)`	Verbose debug logging via `tracing`	`false`
`.partial_flush_interval(Duration)`	Periodic event flush. `Duration::ZERO` disables periodic flush	`1s`
`.trace_flush_interval(Duration)`	Periodic span flush. `Duration::ZERO` disables periodic flush	`1s`
`.trace_max_batch_size(usize)`	Max spans per trace export request	`50`
`.trace_max_queue_size(usize)`	Max spans buffered before back-pressuring	`5000`
`.max_attempts(u32)`	HTTP retries. `1` disables retries.	`3`
`.base_delay(Duration)`	Backoff base (exponential, ±20% jitter)	`1s`
`.jitter_fraction(f64)`	Backoff jitter fraction (0.0–1.0)	`0.2`
`.service_name(&str)`	OTLP `resource.service.name`	`"raindrop.rust-sdk"`
`.library_name(&str)`	`$context.library.name` reported with each event	`"raindrop-rust"`
`.library_version(&str)`	`$context.library.version`	crate version
`.http_client(reqwest::Client)`	Inject a custom `reqwest::Client`	new client w/ 10s timeout
`.local_workshop_url(&str)`	Mirror cloud-bound posts to a local Workshop daemon	auto-detected localhost
`.disable_local_workshop()`	Disable env/probe-based local Workshop mirroring	—

Call client.close().await? before your process exits to flush buffered events and spans. If write_key is empty and no local Workshop is configured, the client becomes a no-op (zero HTTP calls) instead of failing. Local Workshop mirroring is enabled when RAINDROP_LOCAL_DEBUGGER is set to a URL, when RAINDROP_WORKSHOP is a truthy value or URL, or when the SDK can connect to the default Workshop daemon at http://localhost:5899/v1/.

Projects

Pass .project_id(...) on the builder to scope every event from a client to a specific project. Under the hood this sets the X-Raindrop-Project-Id header on each request.

let client = Client::builder()
    .write_key(std::env::var("RAINDROP_WRITE_KEY").unwrap_or_default())
    .project_id("support-prod")
    .build()?;

A project slug is up to 63 lowercase letters, digits, and hyphens, and it must start and end with a letter or digit (^[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?$). Omitting .project_id(...) (or passing "default") sends to the default Production project, which is the existing behavior, so single-project orgs need nothing here. See Projects for isolation, archival, and the full behavior table.

Tracing

Tracing captures detailed execution information from your AI pipelines — multi-model interactions, chained prompts, and tool calls. This helps you:

Visualize the full execution flow of your AI application
Debug and optimize prompt chains
Understand the intermediate steps that led to a response

Manual Spans

Use manual spans for workflow, task, retrieval, or any other work that is not specifically an LLM generation or tool call. Build span trees by passing a parent into SpanOptions::parent:

use raindrop::{Attribute, SpanOptions};

let parent = client.start_span(SpanOptions {
    name: "agent.run".into(),
    event_id: "evt_123".into(),
    operation_id: "ai.workflow".into(),
    ..Default::default()
});

let retrieval = client.start_span(SpanOptions {
    name: "rag.retrieve".into(),
    event_id: "evt_123".into(),
    operation_id: "ai.task".into(),
    parent: Some(parent.clone()),
    attributes: vec![Attribute::string("retrieval.index", "docs")],
    ..Default::default()
});
retrieval.set_attributes([
    Attribute::int("retrieval.results", 4),
]);
retrieval.end();

parent.end();

Spans started from an Interaction automatically inherit its user_id, convo_id, and event as traceloop.association.properties.* attributes, so the dashboard groups them under the same user, conversation, and event:

let interaction = client
    .begin(BeginOptions {
        user_id: "user-123".into(),
        convo_id: "conv-456".into(),
        event: "agent_run".into(),
        ..Default::default()
    })
    .await;

let span = interaction.start_span(SpanOptions {
    name: "rag.retrieve".into(),
    ..Default::default()
});
// ... do work ...
span.end();

Plain client.start_span(...) calls only need name + event_id. The SDK automatically emits traceloop.association.properties.event_id so the span survives the backend’s ingestion filter. For non-event-bound spans, set operation_id (e.g. "ai.workflow") or pass properties so the span has at least one of ai.operationId, traceloop.span.kind, traceloop.workflow.name, traceloop.association.properties.*, or gen_ai.* — otherwise it will be dropped server-side.

LLM Spans

Use start_llm_span for model calls. LlmSpan emits the attributes that Raindrop’s backend and frontend understand for prompt messages, response text, model/provider, and token usage.

use raindrop::{LlmMessage, LlmOptions, SpanOptions};

let agent = interaction.start_span(SpanOptions {
    name: "agent.run".into(),
    operation_id: "ai.workflow".into(),
    ..Default::default()
});

let llm = interaction.start_llm_span(
    "openai.responses",
    LlmOptions {
        parent: Some(agent.clone()),
        provider: "openai".into(),
        model: "gpt-4o".into(),
        messages: vec![
            LlmMessage::system("You are concise."),
            LlmMessage::user("What is the weather in San Francisco?"),
        ],
        ..Default::default()
    },
);

let output = call_llm().await;

llm.set_output(output);
llm.set_token_usage("gpt-4o", /* input */ 47, /* output */ 11);
llm.end();
agent.end();

messages is the chat-shaped prompt representation. Use it when your provider call takes a role/content array instead of a single text prompt:

let llm = interaction.start_llm_span(
    "openai.chat",
    LlmOptions {
        model: "gpt-4o".into(),
        provider: "openai".into(),
        messages: vec![
            LlmMessage::system("You answer in one sentence."),
            LlmMessage::user("Summarize this support ticket."),
            LlmMessage::assistant("The user is asking about billing."),
            LlmMessage::user("Add the next action."),
        ],
        ..Default::default()
    },
);

llm.set_output("Ask the user for the invoice number.");
llm.end();

LlmMessage::system, LlmMessage::user, and LlmMessage::assistant are convenience constructors; use LlmMessage::new(role, content) for provider-specific roles. If both input and messages are set in LlmOptions, messages wins. The backend uses the last user message as the span’s input_payload, while the frontend renderer can show the full message array. If you only know the messages after creating the span, use set_messages. It replaces any previous input or prompt-message attributes:

let llm = interaction.start_llm_span(
    "anthropic.messages",
    LlmOptions {
        provider: "anthropic".into(),
        model: "claude-sonnet-4-5".into(),
        ..Default::default()
    },
);

llm.set_messages([
    LlmMessage::system("You answer using the provided tool result."),
    LlmMessage::user("What is the weather in San Francisco?"),
    LlmMessage::assistant("I will call get_weather."),
    LlmMessage::user("The tool returned 67°F and windy."),
]);
llm.set_output("It is 67°F and windy in San Francisco.");
llm.end();

You can also seed the LLM span from LlmOptions:

let llm = interaction.start_llm_span(
    "anthropic.messages",
    LlmOptions {
        provider: "anthropic".into(),
        model: "claude-sonnet-4-5".into(),
        input: Some("Summarize this ticket.".into()),
        output: Some("The user needs help with billing.".into()),
        input_tokens: 120,
        output_tokens: 24,
        ..Default::default()
    },
);
llm.end();

LlmSpan exposes set_model, set_provider, set_input, set_messages, set_output, set_io, set_token_usage, set_error, end, and end_at.

Closure-style helpers

If you prefer scoped instrumentation, with_span runs a closure inside a span and automatically marks the span as failed on Err:

interaction
    .with_span::<_, _, _, std::io::Error>(
        SpanOptions { name: "summarize".into(), ..Default::default() },
        |span| async move {
            span.set_attributes([Attribute::string("phase", "draft")]);
            Ok::<_, std::io::Error>(())
        },
    )
    .await?;

Tool Spans

Tool spans use the dedicated wire format (traceloop.span.kind=tool) so they surface in the dashboard’s event.toolCalls[] array.

use raindrop::ToolOptions;
use serde_json::json;

let tool = interaction.start_tool_span("weather_lookup", ToolOptions {
    input: Some(json!({ "location": "San Francisco" })),
    ..Default::default()
});
tool.set_output(&json!({ "forecast": "sunny" }));
tool.end();

For retroactive logging of an already-completed call:

use raindrop::TrackToolOptions;
use std::time::Duration;

interaction.track_tool(TrackToolOptions {
    name: "web_search".into(),
    input: Some(json!({ "query": "weather in NYC" })),
    output: Some(json!({ "results": ["Sunny, 72°F"] })),
    duration: Some(Duration::from_millis(150)),
    properties: BTreeMap::from([("engine".into(), json!("google"))]),
    ..Default::default()
});

// Failed tool calls (status=ERROR on the dashboard, with the message in output_payload)
interaction.track_tool(TrackToolOptions {
    name: "database_query".into(),
    input: Some(json!({ "query": "SELECT * FROM users" })),
    duration: Some(Duration::from_millis(50)),
    error: Some("connection timeout".into()),
    ..Default::default()
});

Field	Type	Description
`name`	`String`	Tool name
`input`	`Option<Value>`	JSON input
`output`	`Option<Value>`	JSON output
`duration`	`Option<Duration>`	Total duration
`start_time`	`Option<OffsetDateTime>`	When the tool started (defaults to `now - duration`)
`end_time`	`Option<OffsetDateTime>`	When the tool ended
`error`	`Option<String>`	Error message; sets `status=ERROR`
`parent`	`Option<Span>`	Parent span for nesting
`properties`	`BTreeMap<String, _>`	Additional metadata

For functional wrapping, the SDK exposes with_tool and with_tool_async free helpers that run a closure inside a tool span and JSON-serialize the result onto traceloop.entity.output:

let result = raindrop::with_tool::<_, _, std::io::Error>(
    &interaction,
    "park_check",
    ToolOptions {
        input: Some(json!({ "location": "Dolores Park" })),
        ..Default::default()
    },
    || Ok(json!({ "recommendation": "yes" })),
)?;

Standalone Tracer

Use Client::tracer() for batch jobs or non-conversation work where you still want spans, LLM spans, and tool traces:

let tracer = client.tracer(BTreeMap::from([("job_id".into(), json!("batch-123"))]));

let span = tracer.start_span(SpanOptions { name: "embed".into(), ..Default::default() });
span.end();

let llm = tracer.start_llm_span(
    "batch.summarize",
    LlmOptions {
        model: "gpt-4o-mini".into(),
        input: Some("Summarize the batch.".into()),
        ..Default::default()
    },
);
llm.set_output("Batch summary complete.");
llm.end();

tracer.track_tool(TrackToolOptions {
    name: "vector_lookup".into(),
    input: Some(json!({ "query": "mission coffee" })),
    output: Some(json!({ "winner": "Ritual Coffee Roasters" })),
    properties: BTreeMap::from([("step".into(), json!("retrieve"))]),
    ..Default::default()
});

Span Attributes

The SDK provides typed helpers for OTLP-compatible attributes:

span.set_attributes([
    Attribute::string("ai.model.id", "gpt-4o"),
    Attribute::int("ai.usage.prompt_tokens", 150),
    Attribute::float("ai.latency_seconds", 1.23),
    Attribute::bool("ai.stream", true),
    Attribute::string_array("ai.tools", vec!["search".into(), "calculator".into()]),
]);

Known Limitations

No automatic LLM-client instrumentation. Unlike the Python and TypeScript SDKs, the Rust SDK does not auto-hook into LLM frameworks. Create spans manually via start_span, start_llm_span, start_tool_span, with_span, with_tool, or track_tool.
No PII redaction. The Python SDK exposes set_redact_pii and the TypeScript SDK has redactPii. The Rust SDK does not yet implement client-side redaction. Redact at the call site or upstream of track_ai / track_event if needed.
Oversized payload guard. Payloads larger than 1 MiB after JSON serialization are dropped client-side (matching the JS / Python SDKs) to avoid 413s on the gateway. The drop is logged via tracing::warn! so production callers can detect it.

That’s it! You’re ready to explore your events in the Raindrop dashboard. Ping us on Slack or email us if you get stuck!

Getting Started

SDK

Integrations

Platform

Organization

Workshop

Query API

MCP

Security

Support

Installation

Quick Start: Interaction API

Example: Chat Completion

Updating an Interaction

Resuming an Interaction

Single-Shot Tracking (`track_ai`)

Tracking Signals (Feedback)

Identifying Users

Attachments

Configuration

Projects

Tracing

Manual Spans

LLM Spans

Closure-style helpers

Tool Spans

Standalone Tracer

Span Attributes

Known Limitations

​Installation

​Quick Start: Interaction API

​Example: Chat Completion

​Updating an Interaction

​Resuming an Interaction

​Single-Shot Tracking (track_ai)

​Tracking Signals (Feedback)

​Identifying Users

​Attachments

​Configuration

​Projects

​Tracing

​Manual Spans

​LLM Spans

​Closure-style helpers

​Tool Spans

​Standalone Tracer

​Span Attributes

​Known Limitations

Installation

Quick Start: Interaction API

Example: Chat Completion

Updating an Interaction

Resuming an Interaction

Single-Shot Tracking (`track_ai`)

Tracking Signals (Feedback)

Identifying Users

Attachments

Configuration

Projects

Tracing

Manual Spans

LLM Spans

Closure-style helpers

Tool Spans

Standalone Tracer

Span Attributes

Known Limitations