Foglamp vs Semantica: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Foglamp and Semantica — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Foglamp

Freemium

Observability for AI agents: see the cost, latency, traces, and output quality of every LLM call with one SDK.

Two-Line SDK Instrumentation: Wrap your model once and every generateText / streamText call is automatically instrumented.
Per-Agent Spans and Spend: View per-agent spans, latency, and spend with the full call flow across orchestrator, researcher, writer, and critic.
Evals: Score production traffic with code checks and LLM judges, including PII checks and pass-rate scoring.
Distributed Traces: Waterfall every run with the exact prompt and response captured per span.
Alerts: Set threshold rules on cost, latency, and error rate to catch problems early.
Cost Intelligence: Know exactly what every call costs broken down by model, agent, and customer.

Catching Cost Regressions: Detect a sudden 10x cost spike days after shipping before it drains the budget.
Debugging Bad Output: Trace the exact prompt and response that produced a wrong or hallucinated answer.
Quality Gating with Evals: Continuously score production traffic to verify agents stay accurate and PII-safe.
Latency Monitoring: Alert when per-agent latency crosses a threshold so slow responses are caught fast.
Per-Customer Spend Analysis: Break down LLM spend by customer and model to understand unit economics.

semantica-agi

Free

Open-source, graph-native context and knowledge-graph infrastructure for accountable AI agents in regulated domains.

Graph-Native Ingestion: Ingest enterprise data and extract entities, relationships, and provenance into a Context Graph plus a formal Knowledge Graph.
Polyglot Graph Storage: Native support for both RDF and Labeled Property Graphs so you can pick the model that fits each domain.
W3C Standards & Interoperability: SPARQL, OWL, and related standards keep the graph portable across tooling with zero vendor lock-in.
Deterministic Reasoning: Rule-based and causal reasoning over the graph so agent decisions are reproducible, not black-box.
Decision Provenance: Every decision is traceable back to the ingested evidence and the reasoning steps that produced it.
Ontology & Knowledge Modeling: First-class tools for defining, evolving, and enforcing the domain ontology that governs agent context.
Self-Hostable: Deploy the whole stack inside your own infrastructure — the code is MIT-licensed and open.
Regulated-Domain Ready: Built for high-stakes, governed use cases where auditability and end-to-end traceability are mandatory.

Auditable Agent Decisions: Build agents in finance, healthcare, or public sector where every decision must be traceable to source data.
Enterprise Context Management: Turn scattered enterprise data into a queryable Context Graph the agent uses as its long-term memory.
Causal Analysis: Run causal reasoning over the graph to explain outcomes, not just correlations.
Ontology-Driven Extraction: Enforce a domain ontology so extracted entities and relationships remain consistent across sources.
Regulated Deployment: Self-host in a compliance-bounded environment with zero third-party data egress.
Knowledge Graph Bootstrapping: Ingest documents, databases, and events into a formal KG that agents and BI tools share.