ARV — research with an evidence graph

Русский

ARV (Assumption–Research–Verification) helps an AI agent build a working picture from incomplete data and keep the grounds behind its conclusions inspectable. It bundles a research skill with a local MCP server and a persistent SQLite graph.

The agent reads sources, proposes hypotheses and judges their meaning. ARV stores the source trail, combines distinct grounds and revisits dependent conclusions when a premise changes.

Observation → hypothesis → new grounds or counterfacts → revised working model

Try the demo

Targets Windows, macOS and Linux with Python 3.13 and uv. Use PowerShell on Windows or your terminal on macOS/Linux; the commands are the same. Run these commands from the extracted repository folder. The first command downloads Python 3.13 if needed and installs the locked dependencies.

uv sync --frozen
uv run --frozen --offline python examples/crm-demo/run.py

No CRM account, model API key or paid service is needed for the demo. It drives the actual stdio MCP server with scripted calls and writes a new, separate demo graph on every run. It does not exercise autonomous model reasoning.

The command prints the output folder. Open its walkthrough.md, snapshots.json, model.json and trace.json. See the example and agent prompt.

Related MCP server: chronomem

Use with Codex

The repository includes a local plugin marketplace, portable plugin.json and mcp.json, plus the Codex compatibility manifests. Run uv sync --frozen first; the MCP launch uses cached dependencies in offline mode.

codex plugin marketplace add .
codex plugin add assumption-research-verification@arv-public

Alternatively, open the desktop Plugins Directory, select the ARV marketplace and install the plugin there. Start a new local chat and invoke $assumption-research-verification with your question and authorised sources. See installation, data location and troubleshooting.

What ARV adds

• Observations retain source identity, locator, date and scope.

• Hypotheses can accumulate new grounds and preserve actual counterfacts.

• Copies, derived summaries and repeated inference paths share their original roots.

• User conventions and ordinary domain defaults remain distinguishable from facts.

• Revisions retain history; new material data reopens a saved conclusion.

• Equivalent meanings can be reused or merged explicitly by the agent.

Support is ordinal bookkeeping, not a calibrated probability. The agent still judges source quality, semantic identity and which explanation makes sense. ARV does not certify factual truth or guarantee better answers than a plain agent. See the method and its limits.

Data and access

The graph server runs locally and has no source adapters, model credentials or telemetry. Source access comes from the host's separately authorised tools. Source passages sent to ARV are stored in its graph. A connected AI host may process tool calls and results under that host's own data policies.

The SQLite file is single-user storage, not a tenant isolation boundary. Use a separate graph when a project needs separate data. Details.

Development

uv run --frozen --offline python -m unittest discover -s tests -v
uv run --frozen --offline python scripts/package_release.py

The release builder uses an explicit file list and excludes databases, dependency environments and generated demo output. Contributing · Changes · Verification scope · Release preparation.

ARV 0.4.1 is distributed under the MIT licence. Source and releases.