By David Speakman ·
SPEAKMAN.AI is the free, local, open source engine behind the same MCP workflows the hosted platform runs. Version 1.2.0 adds a real human-in-the-loop revision gate alongside the existing automated validator, a delegate mode that hands a step to your own connected coding agent instead of asking you to paste data in by hand, a tool that revises a finished session without re-running it from scratch, and a new workflow that turns architecture and requirements into reviewable screen mockups.
Every agent can declare either, both, or neither. When both are set, the automated validator always runs first on every fresh output. A human's time never goes toward catching what a score already could have.
Pausing a workflow to ask a human for data it can't derive on its own was already there. What was missing was the other direction: a human looking at something the engine already produced and either signing off or sending it back with real feedback, then watching the same agent revise in place rather than start over. That gap showed up constantly once workflows started producing anything a person actually needed to weigh in on rather than just supply.
Revising a finished session had the same problem from a different angle. A requirement
changes after the architecture is already done, and the only option was re-running the
whole thing, losing whatever decision trail existed and regenerating parts that never
needed to change. update_session exists because that's a bad tradeoff every
time it comes up.
HITL_VALIDATOR: a real human review-and-revise gate, structurally parallel to the automated validator but scoped to judgment calls a score can't make.
Delegate-first steps hand real work (fetching a URL, reading a file, running a script, or calling your own MCP server) to your connected coding agent instead of pausing for a human to do it by hand.
update_session reopens a completed session and revises one agent's output. The change cascades through everything downstream, and nothing unaffected gets touched.
A new built-in pipeline. Architecture and requirements go in; a screen inventory, flow diagrams, a proposed design direction, and clickable mockups come out. Detailed below.
Seeded from a completed Solution Architecture Document and, where available, its
requirements contracts, MCP_UX_DESIGN_V1 maps logical components to real UI
surfaces and requirements to screens, checks that nothing traces back to a gap, then
proposes a design direction and builds self-contained HTML mockups for the highest-priority
screens. The mockup step runs through your connected coding agent rather than a fixed
background model. Design judgment is exactly where the model behind that connection
matters most.
This is the same workflow the /generate-ux-design skill calls into as Phase 2.5
of the SPEAKMAN.AI SDLC pipeline, shown below.
The hosted SPEAKMAN.AI platform runs a full idea-to-production SDLC pipeline. This repository is the free, local, MIT licensed engine that executes the same MCP workflows, with full functionality for an individual user and no account required.
A raw idea, coached into a structured brief. No SPEAKMAN.AI session, no credits.
Business description in, Solution Architecture Document out.
Domain model, naming dictionary, use cases, API and DB schema.
Screen inventory, flow diagrams, a proposed direction, and clickable mockups.
A working, milestone gated, git committed codebase.
Terraform for GCP, AWS, and Azure, plus pre-pentest hardening.
A gated security engagement, run against the live staging build.
Every workflow needs exactly one planner agent, identified by type rather than a hardcoded ID. A live database check found five separate copies doing that job instead of one shared definition, including a planner with a completely empty prompt, silently live, because a fallback that should have caught it only triggers on a missing key, not an empty one.
planner_agent.get("systemPrompt", DEFAULT) → "" (empty string used as-is)
planner_agent.get("systemPrompt") or DEFAULT → real prompt, every time
| Mode | Command | Database |
|---|---|---|
| Windows exe | Run SpeakmanAI.exe | ~/.speakmanai/speakmanai.db |
| Docker + SQLite | docker-compose up --build | speakmanai_data volume |
| Docker + MongoDB | docker-compose --profile mongo up | MongoDB, port 27017 |
| Dev, no Docker | uvicorn server:app --port 8000 | ~/.speakmanai/speakmanai.db |
There's no account and no cloud dependency. No per-call fee beyond your own provider key, either. Workflows are plain JSON on disk, readable and editable rather than locked inside a hosted black box.
Download the exe, or clone and run with Docker. You get full functionality on your own API keys, with nothing sent anywhere else.
The hosted SPEAKMAN.AI platform runs the same engine with the infrastructure teams need on top of it.
Written by David Speakman. Speakman Consulting designs and builds this kind of system for growing organizations: agent workflows with the governance that keeps humans in the loop.