haxaml 0.6.8

Prebuild and governance for AI coding agents.

Haxaml

MCP-first, Git-style workflow governance for AI coding agents.

Most AI coding issues do not begin when an agent writes bad code. They begin earlier, when the agent jumps from a vague request straight into implementation without understanding the project, the missing context, the risks, or what "done" actually means.

Haxaml is a token and context-efficient engine built on top of FRAME: a simple model that splits project understanding into five parts: Facts, Rules, Acts, Map, and Expect.

Instead of dumping the whole project into an agent and hoping it figures things out, Haxaml gives the agent a governed workflow. It helps the agent prepare before coding, pull only the context needed for the task, verify the work with evidence, and record useful state for the next agent.

The result is a calmer agent workflow: less guessing, less context bloat, better handoffs, and a project state that can travel across Claude Code, Codex CLI, Cursor, Copilot, Windsurf, and any MCP-compatible agent.

What Haxaml Is Not

• Not an AI memory backpack. Haxaml is not mainly about storing random facts for an agent to recall later. It is about shaping project understanding so agents can prepare, plan, verify, and record their work properly.
• Not just a prompt file. AGENTS.md, CLAUDE.md, Cursor rules, Copilot instructions, and similar files are adapters. Haxaml is the governed engine underneath them.
• Not a replacement for your agent. Haxaml does not write the code for the agent. It gives the agent a workflow spine so the work starts with context, follows project rules, and ends with verification.
• Not a giant context dump. Haxaml is built to reduce context noise by giving the agent the right project signals at the right time.

How It Works

Haxaml exposes a lifecycle through MCP tools. The agent follows this flow before, during, and after implementation:

Phase	Tool(s)	What happens
about	haxaml_about	The agent learns what Haxaml is, what FRAME means, and how to operate inside the project
guidance	haxaml_guidance	Haxaml classifies the request and decides whether it is governed project work or a utility task
prebuild	haxaml_prebuild → haxaml_context_pack	Agent classifies the task, checks FRAME readiness, opens a governed session, then pulls the first task-scoped context pass
retrieve	haxaml_context_fetch (optional, repeatable)	Agent asks for more governed memory only when the first context pass is not enough
build	(no Haxaml tool)	The agent edits files, writes code, runs commands, answers a question and performs the actual implementation
verify	haxaml_session_verify	The agent records what it inspected, what it changed, what was checked, and what risks remain
record	haxaml_session_record → haxaml_expect_sync	The outcome is written into project history and expectations are synced for future work

In short:

about → guidance → prebuild → context_pack → [context_fetch]* → build → verify → record → expect_sync

Project memory lives in .haxaml/ — versioned files your agent uses at runtime, not a static wall of text. Older acts history is kept in .haxaml/archive/acts-history.yaml and pulled back only on demand.

In 0.6.7, long-lived runtimes also stop rereading unchanged FRAME files blindly. Repeated context_pack calls now compare the current runtime snapshot to the earlier session snapshot, report what changed, and return smaller refresh deltas when only part of the governed context moved.

Install

Haxaml now ships as three related packages:

• haxaml - core FRAME governance engine and CLI
• haxaml-mcp - MCP launcher/runtime package
• haxaml-ui - local read-only dashboard package

Common install paths:

pip install haxaml
pip install haxaml-mcp
pip install haxaml-ui
pip install "haxaml[ui]"
pip install "haxaml[full]"

If you want to run the MCP server without a persistent install:

uvx haxaml-mcp

For persistent local installs:

uv tool install haxaml-mcp

Local Dashboard

The first dashboard release is intentionally narrow:

• browser UI, not TUI
• localhost only
• read-only
• overview-first
• all five FRAME files rendered
• archive summary plus drilldown

Launch it with:

haxaml dashboard

Supported flags:

• --project-dir
• --host
• --port
• --no-open
• --read-only

Install note:

• haxaml dashboard is the primary human launcher
• core haxaml install now includes the MCP runtime dependency (mcp)
• haxaml[ui] installs the dashboard package (haxaml-ui)
• haxaml[full] installs both optional adapter packages
• haxaml-ui is the separate dashboard distribution

MCP Start

Configure your MCP client to launch haxaml-mcp. For project-scoped configs, cwd is enough. For user-wide configs, set HAXAML_PROJECT_DIR to the project root. See learn/haxaml-mcp.md for the full MCP/architecture guide.

Once connected, agents can initialize and validate through MCP tools:

• haxaml_init
• haxaml_validate

Optional fallback: run haxaml init / haxaml validate directly when MCP is unavailable.

MCP Config

Official docs:

• Claude Code MCP docs: https://docs.claude.com/en/docs/claude-code/mcp
• OpenAI Codex MCP docs: https://developers.openai.com/docs/mcp
• MCP architecture: https://modelcontextprotocol.io/docs/learn/architecture

Project-scoped (recommended)

Place config in the project root. The server uses cwd as the project directory — no env var needed.

Claude Code — .mcp.json in project root:

{
  "mcpServers": {
    "haxaml": {
      "command": "uvx",
      "args": ["haxaml-mcp"]
    }
  }
}

Codex CLI — .codex/config.toml in project root:

[mcp_servers.haxaml]
command = "haxaml-mcp"

Generic MCP JSON (Windsurf, Cursor, etc.):

{
  "mcpServers": {
    "haxaml": {
      "type": "stdio",
      "command": "uvx",
      "args": ["haxaml-mcp"]
    }
  }
}

User-wide

Set HAXAML_PROJECT_DIR to pin the server to a specific project regardless of cwd. Useful for global configs that live outside the project.

Claude Code — ~/.claude.json:

{
  "mcpServers": {
    "haxaml": {
      "command": "uvx",
      "args": ["haxaml-mcp"],
      "env": {
        "HAXAML_PROJECT_DIR": "/absolute/path/to/project"
      }
    }
  }
}

Codex CLI — ~/.codex/config.toml:

[mcp_servers.haxaml]
command = "haxaml-mcp"
env = { HAXAML_PROJECT_DIR = "/absolute/path/to/project" }

Generic MCP JSON:

{
  "mcpServers": {
    "haxaml": {
      "type": "stdio",
      "command": "uvx",
      "args": ["haxaml-mcp"],
      "env": {
        "HAXAML_PROJECT_DIR": "/absolute/path/to/project"
      }
    }
  }
}

Bootstrap Prompt

Paste this into your native agent instruction file (AGENTS.md, CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, etc.):

This repository uses Haxaml for agent governance.

Use the Haxaml MCP server for governed project work.
Before governed project work, call haxaml_about(project_dir='.') once in the active MCP session.
Follow the workflow returned by that tool.
If a governed step is skipped or out of order, treat Haxaml contract errors as hard blockers and fix the lifecycle step before continuing.
Do not edit .haxaml/* for utility or side tasks that are not governed project work.

FRAME Files

• .haxaml/facts.yaml - project truth
• .haxaml/rules.yaml - agent operating rules
• .haxaml/acts.yaml - execution diary and decisions
• .haxaml/map.yaml - optional module ownership and impact map
• .haxaml/expect.yaml - run plan and milestones

Docs

• learn/FRAME.md - FRAME memory model
• learn/haxaml.md - how Haxaml makes FRAME operational
• learn/haxaml-mcp.md - MCP setup, architecture mapping, and lifecycle contract
• 0.7.x.md - setup, onboarding, and target support roadmap for the 0.7.x line
• v1.0_Roadmap.md - roadmap from 0.6.0 to 1.0
• docs/architecture.md - module layout and MCP split overview
• docs/mcp-tool-reference.md - compact MCP tool and resource index
• CONTRIBUTING.md - contributor workflow and expectations
• examples/minimal-governed-flow - minimal FRAME project for governed-flow smoke tests