API Reference¶

Complete module reference for the Latent framework, organized by category.

Orchestration¶

Module	Description
`latent.prefect.decorators`	`@flow` and `@task` decorators with auto-config, caching, retries
`latent.prefect.config_loader`	YAML loading, `get_config()`, `get_catalog()`
`latent.prefect.concurrency`	Batched submit/wait for concurrent task execution
`latent.prefect.checkpoint`	Checkpoint caching for intermediate results
`latent.prefect.context`	Context vars: `params`, `logger`, `get_flow_name()`

Module	Description
`latent.agents.LiteLLMAgent`	General-purpose LLM agent with tool support
`latent.agents.Judge`	Score data rows against a Pydantic output schema
`latent.agents.Classifier`	Semantic classification with accuracy/F1/precision/recall
`latent.agents.RAGAgent`	Config-driven agent with pluggable retrieval pipeline
`latent.agents.tool`	`@tool` decorator for standalone and method-based tools
`latent.agents.scores`	`ScoredModel`, `OrdinalScore`, `BinaryScore`, `ContinuousScore`

See Agents for usage guide.

Module	Description
`latent.agents.pipeline.PipelineAgent`	Phase-based agent with deterministic routing
`latent.agents.pipeline.decorators`	`@phase`, `@tool`, `@respond`, `@subagent`
`latent.agents.pipeline.types`	`PipelineState`, `Transition`, `Respond`

See Pipeline Agents for usage guide.

Module	Description
`latent.guardrails.decorators`	`@guardrail` method decorator for auto-discovery
`latent.guardrails.middleware`	`GuardrailMiddleware` composable scanner pipeline
`latent.guardrails.scanners.builtin`	`LanguageScanner`, `InvisibleTextScanner`, `TokenLimitScanner`
`latent.guardrails.scanners.llm`	`LLMGuardrailScanner` for custom LLM-backed rules
`latent.guardrails.config`	TOML-based guardrail configuration
`latent.guardrails.metrics`	Guardrail evaluation metrics

See Guardrails for usage guide.

Module	Description
`latent.optimize.dspy`	`DSPyOptimizer` -- teleprompter-based prompt optimization
`latent.optimize.ace`	`ACEOptimizer` -- section-level prompt refinement
`latent.optimize.combined`	`CombinedOptimizer` -- DSPy then ACE pipeline
`latent.optimize.autoresearch`	`AutoResearchOptimizer` -- git-based codebase optimization
`latent.optimize.tracker`	`ExperimentTracker` for optimization history
`latent.optimize.analysis`	Trajectory analysis and optimizer comparison

See Prompt Optimization for usage guide.

Module	Description
`latent.rag.chroma`	`ChromaRetriever` -- vector store with automatic embedding
`latent.rag.hybrid`	`HybridRetriever` -- dense + BM25 with RRF fusion
`latent.rag.bm25`	`BM25Retriever` -- sparse keyword search
`latent.rag.adaptive`	Query rewriting, HyDE, reranking, CRAG, caching
`latent.rag.pipeline`	`PipelineBuilder` -- config-driven pipeline composition
`latent.rag.tune`	`rag_grid_search` -- hyperparameter sweep
`latent.rag.metrics`	Retrieval evaluation metrics (NDCG, MRR, recall)
`latent.rag.embeddings`	Embedding provider adapters
`latent.raptor`	RAPTOR recursive abstractive retrieval

Flow	Description
`latent.flows.judge_flow`	Score free-text outputs with LLM judge
`latent.flows.classification_flow`	Classify + compute accuracy/F1/precision/recall
`latent.flows.comparison_flow`	Compare model A vs B with statistical tests

Flow	Description
`latent.flows.conversation_scoring_flow`	Turn-level scoring with aggregation
`latent.flows.conversation_sop_flow`	SOP compliance checking
`latent.flows.conversation_trajectory_flow`	Trajectory distance analysis

Flow	Description
`latent.flows.agent_eval_flow`	End-to-end agent evaluation
`latent.flows.agent_inference_flow`	Batch agent inference with tool tracking

Flow	Description
`latent.flows.drift_flow`	Distribution drift detection
`latent.flows.failure_mode_flow`	Failure mode clustering
`latent.flows.knowledge_coverage_flow`	Knowledge gap analysis
`latent.flows.retrieval_eval_flow`	RAG retrieval quality evaluation
`latent.flows.eval_report_flow`	Aggregate report generation

Flow	Description
`latent.flows.ner_flow`	Named entity recognition evaluation
`latent.flows.text_to_sql_flow`	Text-to-SQL accuracy evaluation
`latent.flows.model_garden_flow`	Multi-model comparison
`latent.flows.autoresearch_flow`	Optimization experiment orchestration
`latent.flows.validate_dataset`	Dataset schema validation

See Eval Flows for usage guide.

Module	Description
`latent.stats.bootstrap`	Bootstrap confidence intervals
`latent.stats.intervals`	Wilson intervals, paired bootstrap
`latent.stats.comparison`	`compare_systems()`, McNemar's test, non-inferiority
`latent.stats.classification`	`classification_metrics()`, confusion matrices with CIs
`latent.stats.structured`	Field-level accuracy, composite accuracy, schema compliance
`latent.stats.ordinal`	Ordinal distributions, binarization
`latent.stats.drift`	`detect_drift()`, `drift_report()`, multi-run trends
`latent.stats.effect_size`	Cohen's d, odds ratio, risk ratio
`latent.stats.gating`	`threshold_gate()`, multiple comparison correction
`latent.stats.agreement`	Cohen's kappa, Fleiss' kappa, Krippendorff's alpha
`latent.stats.conversation`	Turn-level scoring, aggregation
`latent.stats.trajectory`	Edit distance, tool trajectory distance
`latent.stats.sop`	SOP compliance scoring
`latent.stats.bayesian`	Beta-binomial posterior
`latent.stats.calibration`	Score calibration
`latent.stats.ppi`	Prediction-powered inference
`latent.stats.markdown`	Markdown report rendering
`latent.stats.models`	`StatisticalReport`, `ComparisonResult`, `GatingResult`

See Statistical Analysis for usage guides.

Module	Description
`latent.mlflow.mlflow`	Wrapped MLflow API (safe when tracking disabled)
`latent.mlflow.mlflow_setup`	Experiment creation and configuration
`latent.mlflow.evaluate`	MLflow evaluation integration
`latent.mlflow.wrapper`	Safe MLflow wrapper (no-ops when disabled)

See MLflow Tracking for usage guide.

Type	Extension	Engine	Description
`pandas.CSV`	`.csv`	pandas	Comma-separated values
`pandas.Parquet`	`.parquet`	pandas	Apache Parquet format
`pandas.JSON`	`.jsonl`	pandas	JSON Lines (line-delimited)
`polars.CSV`	`.csv`	polars	CSV via Polars
`polars.Parquet`	`.parquet`	polars	Parquet via Polars
`json.JSON`	`.json`	stdlib	Plain JSON
`pickle.Pickle`	`.pkl`	cloudpickle	Pickle serialization
`text.Text`	`.txt`	stdlib	Plain text (file or directory)
`text.Markdown`	`.md`	stdlib	Markdown (file or directory)
`conversation.JSONL`	`.jsonl`	stdlib	JSONL with turn structure
`agent_studio.JSONL`	`.jsonl`	httpx	Remote Agent Studio dataset
`agent_studio.Report`	`.jsonl`	httpx	Agent Studio report dataset

Module	Description
`latent.infra`	Docker-based service management (PostgreSQL, Prefect, MLflow)
`latent.cli`	CLI entry point (`latent run`, `latent list`, `latent chat`, etc.)
`latent.workspace`	Path resolution with env var overrides
`latent.config`	TOML configuration loading

See CLI Tools and Infrastructure for usage guides.

Module	Description
`latent.events`	Event system for agent lifecycle hooks
`latent.pricing`	Token usage and cost tracking

Module	Description
`latent.registry`	Task and flow registry for pipeline visualization
`latent.flow_registry`	Flow discovery and registration
`latent.agent_registry`	Agent discovery via `@agent` decorator
`latent.testing`	Testing utilities for unit tests
`latent.exceptions`	Custom exception types