CONTINUUM: Verifiable semantic recovery for long-running AI agents. Semantic checkpoints (not conversation dumps), an idempotent action ledger that refuses duplicate side effects, and a hash-chained t
CONTINUUM: Verifiable semantic recovery for long-running AI agents. Semantic checkpoints (not conversation dumps), an idempotent action ledger that refuses duplicate side effects, and a hash-chained tamper-evident event log, all exposed as a deny-by-default MCP server. Framework-agnostic, Python 3.11+.
- Why - Quick Start - How it works - Features - Empirical Verification - MCP Integration - Framework Integration - Core Concepts - Architecture - API and CLI - Roadmap - What CONTINUUM Is Not - Related work - Status and limitations - Contributing - License
Modern AI agents run long tasks (hundreds of LLM calls, tool invocations, file and database writes). When they crash, the usual response is to replay everything from scratch, which duplicates work, duplicates side effects, wastes tokens, and loses decisions.
CONTINUUM asks a narrower, harder question: can an agent resume from a compact semantic representation of its task state while independently verifying that state is still valid in the current environment? It is not a generic agent framework, a memory system, or a workflow engine. Its differentiator is three-part:
- Semantic checkpoints: a compact, versioned representation of what the agent needs to continue, not a conversation dump. - Independent environment revalidation: every checkpoint component is verified against the current environment before resume, with staleness propagating through the dependency graph. - Provenance-aware state: every fact traces to its origin, so agent-reported progress is never self-certifying.
From the project README.
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An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents.
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