A persistent, self-revising hypothesis DAG for agentic R&D, exposed as an MCP server. It enables agents to structure working knowledge as a directed acyclic graph of hypotheses, with automatic write-back belief revision and cascading pruning based on evidence.
Tracks what an agent KNOWS vs INFERS vs ASSUMES with calibrated confidence. Provides tools to register, query, and analyze epistemic status of claims across domains.
Enables AI agents to use a neuro-symbolic memory fabric with bi-temporal knowledge graph and holographic VSA, providing tools for adding, searching, temporal queries, auditing, and proving memories with cryptographic provenance and zero-LLM ingest.
Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.
A scientific experiment log MCP server for AI agents that stores predictions, causal claims, and verdicts, enabling queryable causal maps and calibration of intuition over diagnostics.
Enables AI agents to perform auditable A* reasoning, import world graphs, incorporate physical carrier constraints, and maintain a learning knowledge base through a zero-dependency local MCP stdio service.
