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Conflict and policy intelligence infrastructure should be transparent and extensible. Five public repositories — KAIROS (temporal vision), AGON (conflict vision), DIALECTICA (the neurosymbolic engine), the reference pipeline, and the ACO grammar — cover the full stack underneath every TACITUS product. Read them, run them, critique them, fork them.
Open stack
TACITUS can be a company and still make the core grammar inspectable. The open-source work exists so researchers and practitioners can see how policy and conflict context becomes a graph before a product uses it.
Spec
The shared grammar: primitives, subclasses, relationship types, graph layers, and serialization rules.
Pipeline
Source ingestion, primitive extraction, confidence handling, source-span binding, and schema validation.
Graph
Temporal graph construction, contradiction edges, commitment state, graph queries, and context packs.
Products
PRAXIS is the flagship workbench. Wind Tunnel and CONCORDIA are side projects. All run on the same DIALECTICA backbone.
Repositories
From temporal vision (KAIROS) and conflict vision (AGON) in Rust, through the neurosymbolic DIALECTICA engine, the Python reference pipeline, and the ACO grammar — pick the layer that fits your use case.
sargonxg/KAIROS-temporal-vision-TACITUS
The Rust temporal-vision layer. Turns long-form political, legal, and diplomatic text into a temporal knowledge graph — dated events, canonical actors, commitments, episodes, Allen-13 relations, and source-grounded evidence spans. The "when" of the engine.
What's inside
sargonxg/AGON
The Rust conflict-vision layer. Reads messy human conflict text and produces typed, evidence-backed primitives: actors, claims, denials, commitments, contradictions, escalation signals, quality gates. Preserves disagreement as data. The "who, what, and why" of the engine.
What's inside
sargonxg/TACITUS-Knowledge-Pipeline-open
The reference extraction and structuring pipeline: ingest, extract, structure, query, implemented end-to-end with the eight-primitive ontology and sample conflict datasets. The fastest way to run TACITUS locally.
What's inside
sargonxg/A2_DIALECTICAbyTACITUS
Reference implementation of the Dialectica engine, the neurosymbolic core that powers every TACITUS product. Ontology Augmented Generation (OAG), typed knowledge graph construction, multi-hop reasoning, full provenance.
What's inside
sargonxg/tacitus-ontology
The Agentic Conflict Ontology as a standalone spec. Pydantic schemas, OWL/Turtle dual export, RFC process for revisions. The open grammar every TACITUS product shares.
What's inside
Role in the engine
AGON and KAIROS are the two Rust vision layers we are building in the open. AGON reads conflict structure. KAIROS reads time. DIALECTICA composes both into the typed, temporally honest graph that PRAXIS, Wind Tunnel, and CONCORDIA reason over.
Conflict vision (Rust)
Reads who, what, and why. Actors, claims, denials, commitments, contradictions, escalation, quality gates. Preserves disagreement as data.
Temporal vision (Rust)
Reads when. Dated events, episodes, Allen-13 relations, commitment trajectories. The temporal scaffolding for everything else.
Neurosymbolic engine
Composes AGON + KAIROS into a single typed graph. Four reasoning layers (GND, CTX, EVD, RZN), Ontology-Augmented Generation, contestable output.
Grammar + reference plumbing
The eight-primitive kernel grammar and the Python reference pipeline that wires it all into a working end-to-end run.
Architecture
All three repositories share the same foundation: a typed, provenance-bound graph grounded by the Agentic Conflict Ontology, augmented by language models for extraction and fluent output.
8 primitives, 41+ typed classes. The schema that makes conflict machine-legible.
GND (ground truth), CTX (context), EVD (evidence), RZN (reasoning). Each layer serves a different intelligence function.
The graph never hallucinates. The LLM never flies blind. Deterministic facts plus probabilistic inference.
Every assertion traces back to a source document, timestamp, and actor. Audit trail by design.
We are a small team building something that does not yet exist: AI infrastructure purpose-built for conflict and human-friction intelligence. Engineers, conflict professionals, researchers: if the brief sounds right, we want to hear from you.
Fork a repo, open an issue, submit a PR, or write in with ideas. The best conflict ontology will be built by the community that uses it.
Engineers
Python, TypeScript, Neo4j, LLMs, graph databases, neurosymbolic AI.
Conflict professionals
Mediators, negotiators, diplomats, HR; domain expertise matters.
Researchers
Ontology engineering, conflict theory, computational social science, NLP.
Ideas and contributions welcome