This repository defines the Conversational Finance Governance Framework, an open specification (v0.4) for embedding runtime authority controls into conversational AI agents operating in fintech contexts. It includes a reference implementation (Lite sidecar) that demonstrates the control plane: classifying utterances along Financial Commitment Risk (F0-F3) and Regulatory Sensitivity (R0-R3) axes, binding each turn to an Action Object (inform/advise/commit/transact/represent), applying a decision table (FREE/AUTH/ESCALATE), enforcing classification assurance, issuing scoped credential records, and producing a hash-chained audit trail. The sidecar is a CLI tool that takes a product-line mapping YAML (payments, lending, insurance, collections) and an utterance, then outputs the governance decision as JSON. It does not call an LLM or execute real transactions; it is a control-plane demo. The repository also contains the full specification documents (v0.3 and v0.4), reference mapping files, and a test suite with gold-standard classification fixtures. The architecture is a monolithic CLI with a clean separation between classification, decision logic, and audit logging. The code is well-structured, uses dataclasses, and includes a reusable hash-chained audit log component. The last commit date (2026-09-09) appears to be in the future relative to the current date, which is anomalous but taken at face value indicates active maintenance.
ThinkWatch is a Rust workspace implementing an AI gateway platform that fronts multiple model providers and MCP (Model Context Protocol) servers. It exposes OpenAI-compatible chat/completion endpoints, streaming, MCP tool-call routing, content filtering, and provider failover. The backend is split into crates: `common` (shared lifecycle/limits/access), `auth` (PoW login, API keys), `gateway` (model routing), `mcp-gateway` (MCP server proxying), `server` (REST admin/audit/trace endpoints), and `test-support` (integration test client). A React/TypeScript frontend in `web/` provides an admin console, setup wizard, limits panels, and MCP server management. Data flows: requests enter the gateway, pass through `check_limits`, `check_budget`, `check_access`, then `run_post_invoke`; `trace_id` is propagated across gateway, MCP, audit, and app logs stored in ClickHouse; Postgres holds users, API keys, budgets, and MCP server configs; Redis/Lua scripts enforce rate limits.