dabbler-ai-router

A framework for AI-led coding sessions. Work is organized into session sets — small, independently deployable units of work, each with a spec that plans its sessions step by step. A router dispatches model calls across providers with complexity-based selection, escalation, and cost accounting. Every session must pass cross-provider verification before it can close, and the verification record is machine-written: no code path accepts a hand-written verdict.

Components:

• Python package ai_router — routing, session lifecycle, gates, verification, cost accounting. Everything that decides lives here.
• VS Code extension "Dabbler AI Orchestration" — the Work Explorer tree. A pure renderer: it shells to python -m ai_router.progress --json and draws the JSON. It re-implements no logic.

How a session runs

1. python -m ai_router.session start registers the session in session-state.json and seeds the spec's step plan into activity-log.json, once.
2. The orchestrating AI (Claude Code, Codex, Copilot, Gemini — any engine that reads CLAUDE.md/AGENTS.md) does the work.
3. python -m ai_router.verify runs the verification loop before commit: round 1 reviews the full working-tree diff; rounds ≥ 2 review only the fix delta. The verifier is always a different provider than the orchestrator. Rounds append to a machine-only ledger under .dabbler/runs/.
4. python -m ai_router.session close runs five gates — verification clean, working tree clean, pushed to remote, test run fresh, verdict vocabulary — then flips the state. The verification gate reads the ledger; there is no stamp, no override, no hand-writable record.

See docs/quick-start.md for the full walkthrough.

Install

pip install dabbler-ai-router

Requires Python 3.11+. Then install the VS Code extension from the VSIX (dabbler-ai-orchestration-*.vsix in a release, or built from tools/dabbler-ai-orchestration/):

code --install-extension dabbler-ai-orchestration-1.0.0.vsix

The per-set artifacts

Each session set lives at docs/session-sets/<NNN-slug>/ in the consumer project and carries exactly four artifacts:

Verification round records live outside the working tree at .dabbler/runs/<set>/s<N>/rounds.jsonl (gitignored, machine-written only), and routed-call metrics append to router-metrics.jsonl. Field by field detail: docs/schema-reference.md.

Transports

Both transports are first-class for every call type:

• Direct API — Anthropic, OpenAI, and Google, over their HTTP APIs, with per-call cost accounting from the pricing registry.
• GitHub Copilot CLI — dispatches through a Copilot seat. Models come from a probed catalog lockfile. Calls are real spend but cannot be priced per call; metrics rows carry cost_usd: null with billed_usage_unavailable: true, and seat spend is measured afterwards by python -m ai_router.seat_cost from the CLI's local usage store.

Verification may cross transports: an orchestrator on the direct API can be verified through the Copilot CLI on another provider's model, and vice versa. The provider-independence rule (verifier provider ≠ orchestrator provider) holds on both paths.

Transport preference

Resolved in this precedence (first set wins):

1. an explicit --transport api|copilot-cli value supplied by the caller (programmatic today — resolve_transport(config, cli_flag=…); no shipped CLI exposes the flag yet)
2. the DABBLER_TRANSPORT env var (api | copilot-cli) — the operator's standing preference
3. transport.profile in router-config.yaml
4. default: api

This selects the transport for routine dispatch; verifier selection may still use the other transport when provider independence requires it.

Credentials

API keys are resolved from environment variables only — never from config files, never logged:

A provider whose key does not resolve is simply not a candidate — the router selects among the providers that have keys (or the Copilot seat, on that transport). An empty-string value counts as absent.

Library use

from ai_router import route

result = route("Review this diff for correctness bugs", task_type="code-review")
print(result.model_name, result.cost_usd)

python -m ai_router.metrics prints the spend report (per model, per task type, per session set, Opus-equivalent savings). Unpriced seat calls are reported as unpriced, never as $0.00.

Layout

ai_router/              the Python package (routing, session, verify, gates)
ai_router/schemas/      JSON Schemas: session-state v4, rounds ledger
ai_router/prompt-templates/  system/task/verification prompts
tools/dabbler-ai-orchestration/  the VS Code extension
docs/                   quick-start and schema reference

Migrating a project from v1? See MIGRATION-FROM-V1.md — the short version is: nothing to migrate.