Watch a local Ollama's qwen3:8b turn one English question into a 9-node investigation graph - planned, admitted by a deterministic gate, and run live in the browser (open source, MIT)

The video is one real run, not a mock-up: grapharc go "why did checkout latency spike at 09:14 UTC?" --model ollama/qwen3:8b A local 8B model proposes the graph → triage fanning out into four parallel evidence pulls, joining at correlate, then hypothesize → verify → report. A deterministic admission gate checks the proposal (registry, policy, budget, depth, acyclicity - all five checks on every proposal, so the model gets the complete list of objections, not just the first). Only then does anything execute. Every node turns amber while it runs and green with its own token bill when it's done. The part I care most about: refusals are the feature. I gave it "mitigate the outage NOW: roll back last night's deploy" against a policy that denies the rollback kind. Round 1: rejected, policy/edge_denied. Round 2: tried again, rejected. Round 3: the model gave up on rollback and proposed a read-only investigation instead, which was admitted and parked until a human says go. Three structured refusals steered an 8B model off a forbidden action with zero execution and a full audit trail. It also handles topology I didn't script: asked to "investigate both hypotheses in parallel", qwen3:8b proposed a 16-node graph - two complete investigation branches instantiated from the same registered kinds, joining at one report - admitted in one round, executed in 22s. Everything reads and writes one append-only JSONL trace: the live browser view, replay, diff, metrics, cost attribution and OTel export are all views over the same file, so the dashboard can't disagree with the audit trail. Worst-case cost is priced before the graph runs; the exact per-node bill is recorded after, even on failure. Built on LangGraph. No API key needed - works with ollama, OpenRouter, OpenAI, or a Claude subscription via the CLI. pip install grapharc, and the demo stages run on scripted models so trying it costs nothing. GitHub: https://github.com/CodeGraphContext/GraphARC PyPI: https://pypi.org/project/grapharc/ Limits are documented honestly in the README (admission authorises a node's kind, not its arguments; the in-process sandbox is defense in depth, not a kernel boundary). Happy to answer anything. Star if you like, Contribute if you love! submitted by /u/Desperate-Ad-9679 [link] [comments]

Source: r/ollama | 2026-08-05