Python SDK - DecimalAI

The Python SDK is the primary way to integrate DecimalAI into your agent code. It wraps every public REST endpoint, auto-instruments 8 agent framework integrations plus 3 direct LLM provider SDKs (OpenAI, Anthropic, Google) — including native plugins for Google ADK and Anthropic’s Claude Agent SDK — and ships zero-config helpers for tracing, evaluation, and dataset export.

pip install decimalai

import decimalai
decimalai.init(api_key="dai_sk_...", openai_agents=True)
# Every supported framework call is now traced.

Sections

Init & Setup

decimalai.init(), environment variables, framework flags.

Tracing

@trace, start_trace, log_llm_call, log_tool_call, @tool.

Manifests

register_manifest, flush_manifest_for_ci.

Evaluations

eval, score, get_eval_breakdown, batch_eval, @eval, external imports.

Replay & Experiments

get_replay_prompts, create_replay_batch, get_replay_batch.

Datasets & Training

pull_dataset, push_to_hub, load_hf_dataset.

Framework Instrumentation

8 framework integrations — OpenAI Agents, LangChain, LlamaIndex, CrewAI, AutoGen, generic OTel, plus native plugins for Google ADK and Anthropic Claude Agent SDK.

Skills

Sync from disk, pull from platform, SkillRouter CRUD.

Complete API Summary

Top-level functions (in `decimalai`)

Grouped by surface. Every function below is importable directly from decimalai.

Tracing

Method	Description
`init()`	Initialize SDK and auto-instrument frameworks.
`@trace()`	Decorator to trace a function as an agent run.
`start_trace()`	Context manager for manual trace boundaries.
`log_llm_call()`	Log an LLM call inside a trace.
`log_tool_call()`	Log a tool call inside a trace.
`@tool`	Decorator to register a tracked tool.
`send()`	Send a fully-constructed trace payload.

Manifests

Method	Description
`register_manifest()`	Explicitly register agent configuration.
`flush_manifest_for_ci()`	Capture and write the manifest ID for the regression-check Action.

Evaluations

Method	Description
`eval()`	Pushes a single named score to a trace (alias of `score()`). The custom-evaluator decorator is `@decimalai.evals.eval`, not this top-level function.
`score()`	Push a single named score to a trace.
`batch_eval()`	Apply evaluators across multiple traces in parallel.
`get_eval_breakdown()`	Get full eval breakdown for a trace.
`push_deepeval_results()`	Import DeepEval scores.
`push_langsmith_scores()`	Import LangSmith feedback as eval scores.
`push_custom_scores()`	Push scores from any source.

Datasets

Method	Description
`pull_dataset()`	Download a dataset version to a local JSONL/Parquet file (returns a summary dict).
`push_to_hub()`	Push a dataset to HuggingFace Hub.
`load_hf_dataset()`	Load a dataset version directly as an in-memory `datasets.Dataset` (no file).

Replay

Method	Description
`get_replay_prompts()`	Download stale prompts for replay.
`create_replay_batch()`	Create a batch of replay tasks.
`get_replay_batch()`	Check replay batch progress.
`submit_replay_result()`	Submit a replayed trace result.

Framework instrumentation (subpackages)

8 framework integrations. Google ADK and Anthropic’s Claude Agent SDK use native plugins (not OTel).

Module	Function	Use it for
`decimalai.langchain`	`install()`	LangChain / LangGraph callbacks
`decimalai.openai_agents`	`install(agent=...)`	OpenAI Agents SDK — pass the Agent object for full schema introspection
`decimalai.llamaindex`	`install()`	LlamaIndex span handler
`decimalai.crewai`	`install()`	CrewAI via OpenTelemetry
`decimalai.autogen`	`install()`	AutoGen / AG2 via OpenTelemetry
`decimalai.adk`	`install()`	Google ADK — native plugin (Gemini-native)
`decimalai.claude_agent_sdk`	`install()`	Anthropic Claude Agent SDK — native plugin
`decimalai.otel`	`install()`	Generic OpenTelemetry GenAI span exporter

Class-based clients

Class	Use it for
`SkillRouter` (from `decimalai`)	Registry + skill lifecycle: `fork` (workspace copy), `install` (fork + write to disk), `preview` (read-only snapshot), `create_skill`, `list_skills`, `get_skill`, `update_skill`, `delete_skill`, `sync_skills`, `list_versions`, `get_menu`, `smart_route`, `export_to_disk`, `pull_missing`.

REST-only endpoints

For endpoints that don’t yet have a top-level SDK wrapper (list_datasets, get_trace_stats, get_manifest, repair preview/apply, compatibility policy set, etc.), use httpx directly against the REST API:

import httpx

resp = httpx.get(
    "https://api.decimal.ai/api/v1/datasets",
    headers={"Authorization": "Bearer dai_sk_..."},
)
resp.raise_for_status()

See the API Reference for the full endpoint registry.

​Sections

Init & Setup

Tracing

Manifests

Evaluations

Replay & Experiments

Datasets & Training

Framework Instrumentation

Skills

​Complete API Summary

​Top-level functions (in decimalai)

​Framework instrumentation (subpackages)

​Class-based clients

​REST-only endpoints

Sections

Complete API Summary

Top-level functions (in `decimalai`)

Framework instrumentation (subpackages)

Class-based clients

REST-only endpoints