haft

Enforces a disciplined 5-mode engineering cycle (Understand → Explore → Choose → Execute → Verify) by requiring AI agents to frame problems before solving them, generate genuinely different solution variants, and compare options under parity constraints. Implements this via MCP tools (haft_problem, haft_solution, haft_decision) that validate reasoning artifacts against a formal specification before allowing progression to implementation.

Exposes Haft's reasoning capabilities as a Model Context Protocol (MCP) server via JSON-RPC transport, providing six specialized tools (haft_problem, haft_solution, haft_decision, haft_evidence, haft_check, haft_search) that AI agents can invoke natively within their execution environment. The server runs as a subprocess managed by the agent's MCP client, maintaining a persistent SQLite state store and knowledge graph indexed to the codebase.

Enforces equal rigor in comparing competing solutions by requiring that all variants be evaluated against the same criteria, preventing bias toward preferred solutions. Implements parity checks via the haft_solution and haft_decision tools that validate solution descriptions follow the same structure and depth. Tracks comparison fairness metrics to ensure decisions are based on equivalent evidence.

Monitors the health of engineering decisions across two axes: maturity (progress from Unassessed to Shipped) and freshness (Healthy → Stale → At Risk based on evidence age and drift detection). Implements R_eff (effective reasoning score) that decays over time as supporting evidence ages, triggering alerts when decisions drift from their original context. Uses SQLite schema with timestamp-based queries to identify stale decisions and prompt re-evaluation.

Builds a knowledge graph that links engineering decisions to codebase artifacts (modules, functions, files) using FPF Spec Search & Indexer. Enables semantic search over past decisions filtered by codebase context, allowing agents to query 'decisions affecting this module' or 'solutions tried for this problem pattern'. Stores graph in SQLite with projections that map decisions to code locations and vice versa.

Provides a terminal-based autonomous agent (haft agent command) that executes the engineering cycle without human intervention, using a ReAct-style coordinator to move through Understand → Explore → Choose → Execute → Verify phases. The coordinator maintains state in SQLite and can pause at checkpoints for human review. Implements a lemniscate cycle pattern that allows looping back to earlier phases if verification fails.

Abstracts LLM provider differences (OpenAI Codex, Anthropic Claude, Google Gemini) behind a unified interface, allowing the same FPF reasoning cycle to work across different models. Routes tool calls and reasoning prompts to the configured provider via a provider adapter pattern, with fallback support for multiple models. Stores provider configuration in project policy files.

Enforces project-level governance policies via .haft/ directory containing formal specifications (FPF Spec), provider configurations, and decision templates. Policies are versioned and can be checked via haft check command to ensure decisions comply with project standards. Implements a policy-as-code approach where governance rules are stored alongside the project and enforced by the Haft runtime.

Provides a Wails v2-based desktop application (Go backend + React frontend) that visualizes the project's reasoning state across multiple dimensions: decision maturity, freshness, evidence decay, and Pareto fronts of competing solutions. Allows human oversight of autonomous agent decisions, multi-project portfolio views, and interactive decision exploration. Status: pre-alpha.

Implements a persistent data layer using SQLite with versioned schema migrations, storing decision artifacts, evidence records, codebase mappings, and reasoning state. Provides projection views that map decisions to code modules and track artifact relationships. Migrations are managed via a migration system that ensures schema consistency across versions.

Provides a comprehensive CLI interface with commands for project initialization (haft init), governance checks (haft check), MCP server startup (haft serve), and autonomous agent execution (haft agent). Commands are composable and support piping for integration with shell scripts and CI/CD pipelines. Implements a task-based CLI pattern with structured output (JSON/YAML).