# https://github.com/semantica-agi/semantica Project Manual
Generated at: 2026-07-07 15:43:13 UTC
## Table of Contents
- [Overview & System Architecture](#page-overview)
- [Data Pipeline & Knowledge Graph Construction](#page-pipeline)
- [Decision Intelligence, Reasoning & Governance](#page-intelligence)
- [Explorer, Integrations & Deployment](#page-ecosystem)
## Overview & System Architecture
### Related Pages
Related topics: [Data Pipeline & Knowledge Graph Construction](#page-pipeline), [Decision Intelligence, Reasoning & Governance](#page-intelligence), [Explorer, Integrations & Deployment](#page-ecosystem)
Related Source Files
The following source files were used to generate this page:
- [README.md](https://github.com/semantica-agi/semantica/blob/main/README.md)
- [ARCHITECTURE.md](https://github.com/semantica-agi/semantica/blob/main/ARCHITECTURE.md)
- [docs/index.md](https://github.com/semantica-agi/semantica/blob/main/docs/index.md)
- [docs/concepts.md](https://github.com/semantica-agi/semantica/blob/main/docs/concepts.md)
- [docs/architecture.md](https://github.com/semantica-agi/semantica/blob/main/docs/architecture.md)
- [docs/getting-started.md](https://github.com/semantica-agi/semantica/blob/main/docs/getting-started.md)
# Overview & System Architecture
## Purpose and Scope
Semantica is an open-source Python framework for building **context graphs** and **decision intelligence layers** for AI agents. It targets Python 3.8 – 3.12 and is distributed under the MIT license, positioning itself as a unified substrate that sits between heterogeneous source data and an agent's reasoning loop. Source: [README.md:1-40]()
Rather than acting as a single-purpose vector store or graph database, the framework provides a **layered pipeline** that turns raw inputs into a queryable semantic graph enriched with embeddings, lineage, and temporal reasoning. Source: [docs/concepts.md:1-30]()
## High-Level System Layout
The framework is organized as a stack of cooperating modules with a small, stable set of top-level entry points. Each layer has a focused responsibility and a well-defined contract with the layers above and below it.
| Layer | Responsibility | Representative Modules |
|---|---|---|
| Ingestion | Normalize heterogeneous sources into internal data classes | `ArrowIngestor`, `OntologyIngestor`, Snowflake connector |
| Extraction | Derive entities, relations, and events from text | NER, relation extraction, ontology alignment, BYOM |
| Graph & Vector Store | Persist semantic structure and embeddings | Native graph layer, Pinecone integration |
| Reasoning & Decision | Run analytics, distance, provenance, and decisions | Distance Intelligence, PROV-O lineage, bi-temporal engine |
| Interfaces | Serve results to agents and humans | Python API, Explorer, Ontology Hub |
Source: [ARCHITECTURE.md:1-80](), [docs/architecture.md:1-90]()
```mermaid
flowchart LR
A[Raw Sources\nCSV, RDF, Arrow, DB] --> B[Ingestion Layer]
B --> C[Semantic Extraction]
C --> D[Knowledge Graph]
C --> E[Vector Store]
D --> F[Reasoning & Decision]
E --> F
F --> G[Agent / Explorer]
```
Source: [docs/architecture.md:30-120]()
## Core Capabilities and Module Boundaries
Across releases the framework has accumulated a broad but orthogonal module surface. Capabilities documented in the repository include:
- **Semantic Extraction** with NER and relation extraction, configurable LLM retry logic, and Bring-Your-Own-Model support for custom Hugging Face models. Source: [docs/concepts.md:40-95]()
- **Ontology Ingestion** for RDF/OWL files in Turtle, RDF/XML, JSON-LD, and N3, exposed through `OntologyIngestor`, `ingest_ontology`, and the unified `ingest(source_type="ontology")` dispatch. Source: [README.md:60-110]()
- **Apache Arrow & Feather Ingestion** via `ArrowIngestor` for `.arrow`, `.feather`, and `.ipc` files, supporting selective column reads, row limits, and batch-aware iteration. Source: [docs/getting-started.md:50-90]()
- **W3C PROV-O Compliant Provenance Tracking** that spans all 17 modules for enterprise-grade lineage. Source: [docs/concepts.md:100-160]()
- **Bi-temporal Intelligence** introduced in v0.4.0, enabling reasoning over both valid time and transaction time. Source: [docs/architecture.md:130-180]()
- **Distance Intelligence** added in v0.5.0, measuring semantic distance between any two nodes across the graph, API, and Explorer. Source: [docs/index.md:20-60]()
Modules are intentionally decoupled: ingestion does not depend on extraction, extraction does not depend on a specific vector backend, and reasoning can run over any combination of graph and vector stores. Source: [ARCHITECTURE.md:90-150]()
## Design Principles and Extensibility
Three principles recur across the documentation and release notes:
1. **Unified dispatch surface** — every ingestion source type is reachable through a single `ingest(source_type=...)` entry point, alongside dedicated convenience functions such as `ingest_arrow()` and `ingest_ontology()`. This keeps user code stable as new formats are added. Source: [docs/getting-started.md:30-70]()
2. **Pluggable backends** — vector stores, LLMs, and graph engines are abstractions rather than hard-coded dependencies. Pinecone is supported natively, while community discussions (#240, #247) track the addition of sqlite-vec, pgVector, and Apache AGE as future pluggable targets. Source: [docs/architecture.md:160-210]()
3. **Traceability by default** — provenance and decision tracking are cross-cutting concerns rather than optional add-ons, with every module able to emit lineage metadata consumable by the Explorer and Ontology Hub. Source: [docs/concepts.md:160-220]()
The net effect is a system in which new data formats, new models, and new storage backends can be introduced without rewriting extraction or reasoning code, while still preserving a single, consistent provenance graph for any agent that consumes the output.
---
## Data Pipeline & Knowledge Graph Construction
### Related Pages
Related topics: [Overview & System Architecture](#page-overview), [Decision Intelligence, Reasoning & Governance](#page-intelligence), [Explorer, Integrations & Deployment](#page-ecosystem)
Related Source Files
The following source files were used to generate this page:
- [semantica/ingest/__init__.py](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/__init__.py)
- [semantica/ingest/file_ingestor.py](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/file_ingestor.py)
- [semantica/ingest/web_ingestor.py](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/web_ingestor.py)
- [semantica/ingest/parquet_ingestor.py](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/parquet_ingestor.py)
- [semantica/ingest/arrow_ingestor.py](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/arrow_ingestor.py)
- [semantica/ingest/snowflake_ingestor.py](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/snowflake_ingestor.py)
# Data Pipeline & Knowledge Graph Construction
## Overview
The Data Pipeline & Knowledge Graph Construction subsystem in Semantica converts heterogeneous raw data — files, web pages, columnar datasets, cloud warehouses, and ontologies — into a structured context graph that AI agents can query and reason over. It spans source-agnostic ingestion, semantic extraction, ontology alignment, and downstream persistence into vector and graph backends. The pipeline reached its first `Production/Stable` designation in v0.3.0 and continues to expand through v0.5.1, where Apache Arrow and Feather ingestion was added.
The architecture is organized as a three-stage flow: **Ingest → Extract → Construct**, with each stage exposing a uniform interface so callers can mix and match source types and extraction strategies without rewriting pipeline logic. The same pipeline feeds higher-level features such as Distance Intelligence (v0.5.0), the Ontology Hub (v0.5.0), and the bi-temporal intelligence stack (v0.4.0).
Source: [semantica/ingest/__init__.py]()
## Ingestion Layer
The ingestion layer abstracts diverse data sources through a registry of specialized `Ingestor` classes, each implementing a common contract that returns a normalized document payload suitable for downstream extraction.
### File-Based Sources
`FileIngestor` covers traditional document formats and serves as the default entry point for unstructured text inputs.
Source: [semantica/ingest/file_ingestor.py]()
For columnar data, two ingestors provide native integration with the Apache Arrow ecosystem:
- `ParquetIngestor` — introduced in v0.5.0, reads Parquet files with schema preservation.
- `ArrowIngestor` — added in v0.5.1 (PR #705), reads `.arrow`, `.feather`, and `.ipc` files via PyArrow. It supports IPC File, IPC Stream, and Feather v1/v2 formats, with selective column reads, row limits, and batch-aware iteration.
Source: [semantica/ingest/parquet_ingestor.py](); [semantica/ingest/arrow_ingestor.py]()
### Web and Cloud Sources
`WebIngestor` fetches and normalizes content from remote URLs, supporting both static HTML extraction and rendered HTML pipelines.
Source: [semantica/ingest/web_ingestor.py]()
`SnowflakeIngestor`, shipped in v0.2.7 (PR #276), provides native Snowflake connectivity with multi-authentication support — password, OAuth, key-pair, and SSO. It supports table and query ingestion, schema introspection, and SQL injection safeguards.
Source: [semantica/ingest/snowflake_ingestor.py]()
### Ontology Source
The `OntologyIngestor` (v0.2.4) parses RDF/OWL files in Turtle, RDF/XML, JSON-LD, and N3 syntaxes. It exposes `ingest_ontology` and supports recursive directory scanning for batch ontology ingestion. The `OntologyData` dataclass provides consistent metadata across loads.
### Unified Dispatch Interface
All ingestors are reachable through a single entry point, allowing callers to specify the source type rather than the concrete class:
```python
from semantica.ingest import ingest
docs = ingest(source="data.parquet", source_type="parquet")
docs = ingest(source="table.feather", source_type="arrow")
docs = ingest(source="snowflake://...", source_type="snowflake")
docs = ingest(source="ontology.ttl", source_type="ontology")
```
This unified `ingest(..., source_type=...)` interface and the convenience helper `ingest_arrow()` were introduced alongside each new ingestor and are aggregated in the package's public API, which also re-exports the per-source convenience functions.
Source: [semantica/ingest/__init__.py]()
## Knowledge Graph Construction
Once documents are ingested, they flow into the extraction and graph construction stage, where text is converted into nodes (entities, concepts, decisions) and edges (relations, provenance links).
### Semantic Extraction
The extraction pipeline performs NER and relation extraction. It supports Bring Your Own Model (BYOM) workflows for custom Hugging Face models introduced in v0.2.5, and includes configurable LLM retry logic. A multi-founder LLM extraction fix in v0.3.0-beta resolved unmatched-relation cases that previously degraded graph completeness.
### Ontology Alignment and the Ontology Hub
Ontology ingestion feeds directly into graph construction, where ontologies are aligned with extracted entities. The Ontology Hub, shipped in v0.5.0, layers a workspace on top for browsing, loading, aligning, and validating ontologies within the Explorer UI.
### Decision Tracking and Provenance
Provenance is tracked W3C PROV-O compliant across all 17 modules (v0.2.6), ensuring every node and edge carries audit metadata. Decision tracking was introduced in v0.3.0-alpha with lifecycle management, audit trails, and lineage. v0.4.0 layered a bi-temporal intelligence stack on top, allowing the graph to reason over both transaction time and valid time.
## End-to-End Pipeline Data Flow
```mermaid
flowchart LR
A[Sources
File · Web · Arrow · Parquet · Snowflake · Ontology] --> B[Unified Ingest
ingest(source_type=...)]
B --> C[Semantic Extraction
NER · Relations · BYOM]
C --> D[Graph Construction
Nodes · Edges · Provenance]
D --> E[Vector Store
Pinecone]
D -.future.-> F[pgVector · Apache AGE]
D -.future.-> G[SQLite + sqlite-vec]
```
## Community Considerations
Several open feature requests relate directly to this pipeline:
- **#247 — Vector/Graph DB Integration**: Users have requested first-class support for pgVector and Apache AGE so that PostgreSQL can act as a unified relational+vector+graph backend. This affects the post-construction storage stage.
- **#240 — SQLite with sqlite-vec**: A request to add SQLite as a lightweight vector store for embedded and edge deployments, complementing the existing Pinecone support added in v0.2.5.
These gaps indicate that the pipeline currently targets heavier, hosted backends, and that lightweight or self-hosted storage adapters remain a community priority. Together, they shape the roadmap for the construction stage's persistence layer.
Source: [semantica/ingest/__init__.py](); [semantica/ingest/file_ingestor.py](); [semantica/ingest/web_ingestor.py](); [semantica/ingest/parquet_ingestor.py](); [semantica/ingest/arrow_ingestor.py](); [semantica/ingest/snowflake_ingestor.py]()
---
## Decision Intelligence, Reasoning & Governance
### Related Pages
Related topics: [Overview & System Architecture](#page-overview), [Data Pipeline & Knowledge Graph Construction](#page-pipeline), [Explorer, Integrations & Deployment](#page-ecosystem)
Related Source Files
The following source files were used to generate this page:
- [semantica/context/__init__.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/__init__.py)
- [semantica/context/context_graph.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/context_graph.py)
- [semantica/context/agent_context.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/agent_context.py)
- [semantica/context/decision_context.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/decision_context.py)
- [semantica/context/decision_recorder.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/decision_recorder.py)
- [semantica/context/causal_analyzer.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/causal_analyzer.py)
# Decision Intelligence, Reasoning & Governance
Semantica frames itself as a framework for building **context graphs and decision intelligence layers for AI agents**. Within that vision, the `semantica/context/` subsystem provides the runtime, structured memory, and audit surface that turn raw extracted knowledge into governable decisions. The components covered on this page — the context graph, agent context, decision context, decision recorder, and causal analyzer — collectively implement reasoning, lifecycle tracking, and governance over agent decisions.
## Module Scope and Public Surface
The `semantica/context` package exposes a small, stable public API that funnels users into the higher-level orchestration classes rather than the storage primitives. The `__init__.py` re-exports the orchestrators and dataclasses that downstream consumers and the Explorer depend on, keeping storage backends (graph, vector, provenance) as implementation details. `Source: [semantica/context/__init__.py:1-80]()`
The major exports include the `ContextGraph` orchestrator (which composes the graph store, vector store, and provenance tracker), `AgentContext` for per-session memory, `DecisionContext` for per-decision scoped state, `DecisionRecorder` for lifecycle and audit logging, and `CausalAnalyzer` for reasoning over cause-effect relationships among entities and decisions. Each orchestrator follows the same construction pattern: ingest/inputs → registry binding → query/reason → persist. `Source: [semantica/context/__init__.py:20-75]()`
## Context Graph as the Reasoning Backbone
`ContextGraph` is the unifying object that holds nodes (entities), edges (relationships, decisions, provenance links), and embeddings. It is the substrate on which decision intelligence is computed. Internally it coordinates a graph store, a vector store, and a provenance tracker; externally it offers a unified query interface that hides which backend satisfies a query. `Source: [semantica/context/context_graph.py:1-60]()`
The graph supports hybrid retrieval by combining symbolic traversal (neighborhood expansion, path queries) with vector similarity, which is essential for decision reasoning where both explicit relationships and semantic similarity matter. The class also enforces schema constraints (node/edge types, allowed properties) so that downstream modules like the causal analyzer can rely on a well-typed graph. `Source: [semantica/context/context_graph.py:60-180]()`
## Agent and Decision Context
`AgentContext` represents the working memory for a single agent run or session. It accumulates intermediate observations, retrieved facts, candidate decisions, and tool outputs, and binds them to a session-scoped namespace inside the `ContextGraph`. This isolates concurrent agents from one another while still allowing cross-session queries when governance reviewers need to audit past runs. `Source: [semantica/context/agent_context.py:1-120]()`
`DecisionContext` is a narrower, per-decision view layered on top of `AgentContext`. It captures the inputs (evidence, constraints, alternatives), the chosen action, the rationale, and links to the entities involved. By scoping state at the decision level, Semantica makes it possible to reproduce, replay, or counterfactually analyze any individual choice without reconstructing the entire session. `Source: [semantica/context/decision_context.py:1-150]()`
## Decision Recording and Lifecycle Governance
`DecisionRecorder` implements the **decision tracking system** introduced as a major feature in v0.3.0-alpha. It manages the full lifecycle: proposal → evaluation → approval/rejection → execution → post-hoc review. Every transition is timestamped, attributed to an actor (human or agent), and written to the provenance tracker so the lineage of the decision can be reconstructed. `Source: [semantica/context/decision_recorder.py:1-200]()`
The recorder integrates with the **W3C PROV-O compliant provenance tracking** introduced in v0.2.6, which spans all modules. Decisions are stored as PROV-O `Activity` and `Entity` nodes; agents and humans become `Agent` nodes; justifications and supporting evidence become `Usage`/`Association` edges. This standard representation is what makes Semantica's governance output portable to external audit pipelines. `Source: [semantica/context/decision_recorder.py:120-260]()`
| Lifecycle Stage | Recorded Artifact | Governance Use |
|---|---|---|
| Proposal | Inputs, alternatives, rationale | Reproduce decision context |
| Evaluation | Scores, constraints checked | Audit policy compliance |
| Approval | Approver identity, timestamp | Accountability trail |
| Execution | Action taken, downstream effects | Impact analysis |
| Review | Outcomes, deltas vs. expectation | Continuous improvement |
## Causal Analysis and Distance-Based Reasoning
`CausalAnalyzer` operates on the typed graph produced by `ContextGraph` and `DecisionRecorder` to identify cause-and-effect chains, confounders, and counterfactuals. It uses both explicit `causes`/`causedBy` edges and statistical signals derived from co-occurrence and temporal ordering in the decision log. `Source: [semantica/context/causal_analyzer.py:1-160]()`
With the release of v0.5.0's **Distance Intelligence** feature, semantic distance between any two nodes — including decisions and their antecedents — can be measured through the same analyzer surface. This closes the loop: a reviewer can ask "how similar was this rejected decision to an approved one?" or "what evidence is closest to the trigger of this outcome?" without leaving the governance workflow. `Source: [semantica/context/causal_analyzer.py:160-280]()`
## Community Signals and Roadmap Context
The community has repeatedly asked for richer database backends — pgVector and Apache AGE in [#247](https://github.com/semantica-agi/semantica/issues/247), SQLite with sqlite-vec in [#240](https://github.com/semantica-agi/semantica/issues/240). Because decision intelligence in Semantica is mediated through `ContextGraph`'s storage abstractions, adding such backends extends governance coverage to embedded and PostgreSQL-centric deployments without changing the decision APIs. The lineage features shipped in v0.2.6 and the decision tracking system from v0.3.0-alpha remain the governance primitives that these backends must preserve.
---
## Explorer, Integrations & Deployment
### Related Pages
Related topics: [Overview & System Architecture](#page-overview), [Data Pipeline & Knowledge Graph Construction](#page-pipeline), [Decision Intelligence, Reasoning & Governance](#page-intelligence)
Related Source Files
The following source files were used to generate this page:
- [explorer/README.md](https://github.com/semantica-agi/semantica/blob/main/explorer/README.md)
- [explorer/src/App.tsx](https://github.com/semantica-agi/semantica/blob/main/explorer/src/App.tsx)
- [explorer/src/workspaces/GraphWorkspace/GraphWorkspace.tsx](https://github.com/semantica-agi/semantica/blob/main/explorer/src/workspaces/GraphWorkspace/GraphWorkspace.tsx)
- [explorer/src/workspaces/OntologyWorkspace/OntologyManager.tsx](https://github.com/semantica-agi/semantica/blob/main/explorer/src/workspaces/OntologyWorkspace/OntologyManager.tsx)
- [explorer/src/workspaces/DecisionWorkspace/DecisionWorkspace.tsx](https://github.com/semantica-agi/semantica/blob/main/explorer/src/workspaces/DecisionWorkspace/DecisionWorkspace.tsx)
- [explorer/src/workspaces/LineageWorkspace/LineageDiagram.tsx](https://github.com/semantica-agi/semantica/blob/main/explorer/src/workspaces/LineageWorkspace/LineageDiagram.tsx)
# Explorer, Integrations & Deployment
## Purpose and Scope
The **Explorer** is the web-based workspace that ships with Semantica. It is an interactive GUI for browsing, inspecting, and operating on the contextual artifacts produced by the core Python framework — graphs, ontologies, decisions, and data lineage — without writing pipeline code. Source: [explorer/README.md:1-40]()
In v0.5.0 the Explorer was expanded into two headline capabilities: a cross-graph **Distance Intelligence** view and a full **Ontology Hub** for browsing, loading, aligning, and validating ontologies. Source: [explorer/README.md:41-90]() The Explorer is therefore not a standalone product; it is a thin presentation layer that delegates to the underlying ingestors, vector stores, and graph algorithms provided by the Python package.
## Workspaces
The Explorer UI is structured around four pluggable workspaces, each mounted by the root application shell. Source: [explorer/src/App.tsx:1-120]()
| Workspace | Primary Component | Role |
|---|---|---|
| Graph | `GraphWorkspace.tsx` | Visualizes nodes/edges, supports Distance Intelligence queries introduced in v0.5.0 |
| Ontology | `OntologyManager.tsx` | Browser for the Ontology Hub: load, align, validate RDF/OWL inputs |
| Decision | `DecisionWorkspace.tsx` | Surfaces the Decision Tracking System (audit trails, provenance) shipped in v0.3.0-alpha |
| Lineage | `LineageDiagram.tsx` | Renders W3C PROV-O compliant lineage added in v0.2.6 |
Source: [explorer/src/App.tsx:60-150](), [explorer/src/workspaces/GraphWorkspace/GraphWorkspace.tsx:1-80](), [explorer/src/workspaces/OntologyWorkspace/OntologyManager.tsx:1-90](), [explorer/src/workspaces/DecisionWorkspace/DecisionWorkspace.tsx:1-75](), [explorer/src/workspaces/LineageWorkspace/LineageDiagram.tsx:1-60]()
The **GraphWorkspace** is the canonical entry point: it loads the in-memory graph and exposes interactive distance measurement between any two nodes — the same primitive used by the Python API. Source: [explorer/src/workspaces/GraphWorkspace/GraphWorkspace.tsx:80-160]() The **OntologyManager** complements the `OntologyIngestor` added in v0.2.4, which parses Turtle, RDF/XML, JSON-LD, and N3 files and produces an `OntologyData` dataclass for consistent metadata across the UI and API. Source: [explorer/src/workspaces/OntologyWorkspace/OntologyManager.tsx:90-180]()
```mermaid
flowchart LR
A[App.tsx
shell] --> G[GraphWorkspace]
A --> O[OntologyManager]
A --> D[DecisionWorkspace]
A --> L[LineageDiagram]
G --> API[(Semantica Python API)]
O --> API
D --> API
L --> API
```
## Integrations
The Explorer and its underlying framework expose integrations across three layers:
- **Data ingestion.** `ArrowIngestor` (`.arrow`, `.feather`, `.ipc`, v0.5.1), Parquet (v0.5.0), Snowflake connector (v0.2.7), and the long-standing `OntologyIngestor` (v0.2.4). Each ships with a convenience function and the unified `ingest(source_type=...)` dispatcher. Source: [explorer/README.md:90-160]()
- **Vector stores.** Pinecone arrived in v0.2.5 alongside configurable LLM retry logic and Bring-Your-Own-Model Hugging Face support. Source: [explorer/README.md:160-220]()
- **Provenance.** W3C PROV-O lineage is wired through all 17 modules in v0.2.6, so the LineageDiagram is fed by every ingestor and extractor rather than a dedicated store. Source: [explorer/src/workspaces/LineageWorkspace/LineageDiagram.tsx:60-150]()
Community members have requested two notable gaps: **pgVector + Apache AGE** in [#247](https://github.com/semantica-agi/semantica/issues/247), and **SQLite with sqlite-vec** in [#240](https://github.com/semantica-agi/semantica/issues/240). Both ask for first-class, lightweight vector backends that fall between Pinecone and Postgres-on-DBaaS.
## Deployment
Deployment centers on the Python package (`pip install semantica`) plus the Explorer front-end bundle. The Explorer is started against a running Semantica API endpoint and reads from the same artifacts the CLI produces, so the typical topology is: data source → ingestor → graph/vector store → API → Explorer. Source: [explorer/README.md:40-120]() Because distance queries, ontology hub operations, and lineage views are all served by the Python core, scaling out is a function of the chosen vector and graph backends rather than the UI itself — a fact that has shaped the v0.4.0 bi-temporal intelligence stack and the v0.3.0 decision-tracking primitives the Explorer merely exposes.
---
---
## Pitfall Log
Project: semantica-agi/semantica
Summary: Found 7 structured pitfall item(s), including 0 high/blocking item(s). Top priority: Configuration risk - Configuration risk requires verification.
## 1. Configuration risk - Configuration risk requires verification
- Severity: medium
- Evidence strength: source_linked
- Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: community_evidence:github | https://github.com/semantica-agi/semantica/issues/228
## 2. Capability evidence risk - Capability evidence risk requires verification
- Severity: medium
- Evidence strength: source_linked
- Finding: README/documentation is current enough for a first validation pass.
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: capability.assumptions | https://github.com/semantica-agi/semantica
## 3. Maintenance risk - Maintenance risk requires verification
- Severity: medium
- Evidence strength: source_linked
- Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: evidence.maintainer_signals | https://github.com/semantica-agi/semantica
## 4. Security or permission risk - Security or permission risk requires verification
- Severity: medium
- Evidence strength: source_linked
- Finding: no_demo
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: downstream_validation.risk_items | https://github.com/semantica-agi/semantica
## 5. Security or permission risk - Security or permission risk requires verification
- Severity: medium
- Evidence strength: source_linked
- Finding: no_demo
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: risks.scoring_risks | https://github.com/semantica-agi/semantica
## 6. Maintenance risk - Maintenance risk requires verification
- Severity: low
- Evidence strength: source_linked
- Finding: issue_or_pr_quality=unknown。
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: evidence.maintainer_signals | https://github.com/semantica-agi/semantica
## 7. Maintenance risk - Maintenance risk requires verification
- Severity: low
- Evidence strength: source_linked
- Finding: release_recency=unknown。
- User impact: May increase setup, validation, or first-run risk for the user.
- Evidence: evidence.maintainer_signals | https://github.com/semantica-agi/semantica