Spec Editor

Active Memory Layer for Requirements & Code — powered by AI agents

▶ Watch the demo (GIF)

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Spec Editor is an active memory layer for AI-driven development. Unlike passive docs, this memory is alive — AI agents continuously debate, cross-reference, and evolve the project knowledge.

# One-liner install (macOS / Linux):
curl -sSL https://raw.githubusercontent.com/spec-editor/spec-editor/main/install.sh | bash

# Or via pip (requires Python 3.11+):
pip install spec-editor && spec-editor init --with-example && spec-editor run

Every AI coding agent suffers from amnesia between sessions. Spec Editor gives them — and your team — a shared, persistent memory that grows smarter with every run. Not a wiki. Not a task tracker. An active, self-maintaining knowledge base that debates its own completeness.

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Why Now?

Cursor, Copilot, and Claude Code have made AI-assisted coding mainstream. But they all share one critical flaw: no memory between sessions. Every conversation starts from zero.

Spec Editor is the missing layer — a team memory for the AI era. Solo developers get a structured analysis process they'd otherwise skip. Teams get a single source of truth that stays in sync with code via @implements traceability.

Built with 2 years of LLM engineering experience and 20+ years in software development.

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What is Spec Editor?

Spec Editor is an active memory system for your project. AI agents don't just write to it — they debate, cross-validate, and continuously refine the knowledge. Every specification element is a version-controlled artifact that any AI coding agent can query via MCP.

         ┌─────────────────────────────────────┐
         │           ACTIVE MEMORY              │
         │                                      │
         │   ┌──────┐  ┌──────┐  ┌─────────┐   │
         │   │Agent 1│  │Agent 2│  │Orchestr.│  │  ← debate & refine
         │   └──┬───┘  └───┬───┘  └────┬────┘   │
         │      │          │           │        │
         │      ▼          ▼           ▼        │
         │   ┌──────────────────────────────┐   │
         │   │  Structured Knowledge Base   │   │  ← YAML + Markdown + Git
         │   │  MOD-001  SCN-007  ENT-004   │   │
         │   └──────────────────────────────┘   │
         │                                      │
         │   MCP → Cursor, Claude, Zed, ...     │  ← any agent can query
         └─────────────────────────────────────┘

Built around a pluggable methodology system — define any set of aspects (for ex. modules, scenarios, UI, entities, NFRs), their relationships, and the agent skills that populate them. Create your own or use the built-in waterfall.

It is:

• An active memory layer — AI agents debate, maintain, and evolve project knowledge
• A methodology engine — define your own aspects, relationships, and agent skills in YAML
• An architectural code generator — produces structured code from patterns (hexagonal, DDD, MVC)
• An MCP server — 20+ tools for external AI agents to read and search your specification
• A VS Code extension + web UI — browse specs visually, no terminal required

It is NOT:

• A replacement for human decision-making — agents debate, humans decide

[!NOTE] Works with any OpenAI-compatible API. Default: DeepSeek Reasoner (~$0.55/M tokens). Free & Open Source — Apache 2.0. No per-seat pricing, no vendor lock-in, your data stays in your Git repo.

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Who Is This For?

• Business analysts — turn stakeholder interviews and vague docs into structured specs
• System analysts — decompose requirements into modules, data models, and API contracts
• Engineering teams — need traceability from requirements to deployed code
• Technical PMs — tired of Word docs and Jira tickets drifting apart over time
• AI-assisted developers — using Cursor, Claude Code, or Zed — give your coding agent full spec context
• AI agent developers — give your agents a shared, persistent memory of project requirements, decisions, and code contracts via MCP
• Vibe-coders — gives you a secret sauce of technical architecture and professional-grade requirements

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Before & After

Input — a single paragraph in source/readme.md:

"We need a user authentication system with login, registration, and password reset."

Output — structured specification in aspects/:

aspects/
├── modules/MOD-003.md        Authentication Module
├── user_scenarios/SCN-007.md  User Login (happy path, error states, rate limiting)
├── user_scenarios/SCN-008.md  Password Reset (email flow, token expiry)
├── user_interface/UI-005.md   Login Form (widgets, validation rules)
├── data_entities/ENT-004.md   User entity (fields, constraints, relationships)
└── non_functional/NFR-002.md  Auth latency < 200ms, bcrypt hashing, OWASP compliance

Each element is a version-controlled Markdown file with YAML frontmatter — diffable, mergeable, and connected via bidirectional traceability links.

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Why Not Just Prompt an LLM Directly?

A raw LLM prompt produces superficial, flat requirements. Spec Editor's multi-agent debate and methodology-driven structure produce deeply connected specifications — much better than what any single LLM prompt can achieve.

What happens with raw LLM	What spec-editor does
Single perspective	Multi-agent debate with structured rounds
No adversarial review	Agents challenge each other — edge cases, contradictions caught
Freeform output	Methodology-driven: modules, scenarios, UI, data, NFR, metrics
No persistent memory	Full project memory — every decision, requirement, and relationship is versioned in Git

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Quick Start

pip install spec-editor

# 1. Instant preview (no API key)
spec-editor demo              # opens pre-generated spec in browser

# 2. Create project and run agents
spec-editor init my-project --with-example   # creates project with sample requirements
cd my-project
spec-editor run                               # needs DEEPSEEK_API_KEY in .env

# 3. Connect to your AI coding agent
spec-editor mcp &             # start MCP server in background
# Add the MCP config to your agent (see below)

# 4. Export to shareable format
spec-editor export -f html    # styled HTML report
spec-editor export -f srs     # IEEE 830 Markdown
spec-editor validate          # check methodology compliance

After spec-editor run completes, you'll have:

• aspects/ — structured specification in Markdown + YAML frontmatter
• source/session_summary.md — what the agents did and why

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Connect to AI Coding Assistants (MCP)

Spec Editor runs an MCP server for any MCP-compatible agent (Zed, Cursor, Claude Code, Windsurf, etc.).

spec-editor mcp &   # start in background

Add to your agent's MCP config (.mcp.json):

{
  "mcpServers": {
    "spec-editor": {
      "command": "spec-editor",
      "args": ["mcp", "-p", "/absolute/path/to/project"]
    }
  }
}

What Your Agent Gets

Tool	Description
get_context_for_file	Spec context for a code file via @implements
search_elements	Full-text and semantic search across requirements
read_element	Read any specification element by ID
list_all_elements	Browse entire specification

Add @implements("REQ-ID") decorators to your code — the agent automatically pulls linked requirements into its context. This gives AI coding assistants supercharged debugging: they see not just your code, but the exact requirements it was built to satisfy. Bugs get traced back to spec elements instantly.

Full API reference: readme_mcp.md

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VS Code Extension

Install from the .vsix file included in the repository:

code --install-extension packages/vscode-extension/spec-editor-vscode-0.1.0.vsix

What you get:

• Tree view — browse aspects and all spec elements
• Validation panel — see errors and warnings inline as you work
• Mermaid diagrams — visualize relationships between elements

The extension automatically connects to the MCP server started by spec-editor mcp.

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Web UI (Experimental)

Launch a browser-based interface to explore your specification visually:

cd packages/frontend/out
python3 -m http.server 3000
# Open http://localhost:3000

Or with Docker (configured during spec-editor init):

docker compose up -d

Ideal for team reviews, stakeholder walkthroughs, and non-technical users. A web-cloud version is coming!

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How It Works

┌──────────────────────────────────────────────────────────────┐
│                     SPEC EDITOR                              │
│                                                              │
│  SOURCE DOCUMENTS                                            │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐                      │
│  │ PDF/TXT  │ │ Telegram │ │  Voice   │  ...                 │
│  └────┬─────┘ └────┬─────┘ └────┬─────┘                      │
│       │             │            │                            │
│       ▼             ▼            ▼                            │
│  ┌─────────────────────────────────────┐                     │
│  │        Ingestion Pipeline           │                     │
│  │  PDF → text, spam filter, SRC gen   │                     │
│  └─────────────────┬───────────────────┘                     │
│                    ▼                                          │
│  ┌─────────────────────────────────────┐                     │
│  │        AGENT DIALOGUE               │                     │
│  │  ┌──────────┐  ┌──────────┐         │                     │
│  │  │ Agent 1  │  │ Agent 2  │  +Orch  │                     │
│  │  │ modules  │  │scenarios │         │                     │
│  │  └────┬─────┘  └────┬─────┘         │                     │
│  │       │   debate    │               │                     │
│  │       ▼             ▼               │                     │
│  │  ┌─────────────────────────────┐    │                     │
│  │  │  Skill-based helpers        │    │                     │
│  │  │  scenario_decomposer,       │    │                     │
│  │  │  ui_navigator, metrics_linker   │                     │
│  │  └─────────────────────────────┘    │                     │
│  └─────────────────┬───────────────────┘                     │
│                    ▼                                          │
│  ┌─────────────────────────────────────┐                     │
│  │         SPECIFICATION               │                     │
│  │  aspects/modules/    MOD-001.md     │                     │
│  │  aspects/scenarios/  SCN-001.md     │                     │
│  │  aspects/entities/   ENT-001.md     │                     │
│  └──────────────────┬──────────────────┘                     │
│                     ▼                                        │
│  ┌──────────────────────────────────────┐                    │
│  │           MCP SERVER                  │                    │
│  │  19 tools — read_element,            │                    │
│  │  search_elements, list_aspect, ...   │                    │
│  └──────────────────┬───────────────────┘                    │
│                     ▼                                        │
│  ┌──────────────────────────────────────┐                    │
│  │     AI CODING AGENTS                  │                    │
│  │  Claude Code · Cursor · Zed · ...    │                    │
│  │  Code with full spec context          │                    │
│  └──────────────────┬───────────────────┘                    │
│                     ▼                                        │
│  ┌──────────────────────────────────────┐                    │
│  │    VS CODE EXTENSION + WEB UI        │                    │
│  │  Tree view · Diagrams · Validation   │                    │
│  │  Browser UI for non-technical users  │                    │
│  └──────────────────────────────────────┘                    │
└──────────────────────────────────────────────────────────────┘

Key Features

Feature	Description
Multi-agent dialogue	2 agents + orchestrator debate requirements in structured rounds
Pluggable methodologies	Define any set of aspects, relationships, and agent skills in YAML — not locked into one framework
Skill-based helpers	Agents spawn specialised helpers: scenario decomposer, UI navigator, metrics linker
Architectural codegen	Generates code following patterns: hexagonal, DDD, clean architecture, MVC
MCP server	20+ tools — connect to Claude Code, Cursor, Zed for context-aware code generation
Export formats	SRS (IEEE 830), TRLC (BMW), OpenAPI 3.0, Jira CSV, styled HTML
Git-native	Everything is Markdown + YAML in git — version, diff, merge, blame
Pluggable subsystems	Swappable backends for ingestion, visualization, storage, secrets, events, auth, and notifications

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Supported Methodologies

Specifications follow a methodology — a YAML-defined structure of aspects, element types, cross-aspect relationships, and agent skills. Create your own or use the built-in ones:

Methodology	What it generates	Status
waterfall	Full spec: modules, scenarios, UI, entities, non-functional, implementation, metrics, sources	✅ Bundled
agile	Sprint backlog: epics → user stories → acceptance criteria + Jira CSV	🔜 Coming
scrum	Agile + sprints (goal, capacity, focus factor, velocity, DoD)	🔜 Coming
kanban	Agile + workflow stages (WIP limits, cycle time, throughput)	🔜 Coming
api-first	OpenAPI 3.0 contract (service → endpoint → schema + auth)	🔜 Coming

Create your own methodology in YAML — define aspects, element types, cross-aspect relationships, and agent skills. See data/methodology.yaml for the waterfall example.

Reverse Engineering

Already have code but no spec? Use reengineer mode to extract requirements from an existing codebase:

spec-editor reengineer ./my-codebase   # reads @implements, docstrings, types
spec-editor run                        # agents fill in the gaps

Supported languages: Python, TypeScript, JavaScript, Go, Java, Kotlin, Rust.

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CLI Commands

spec-editor demo                         # Instant preview (no API key)
spec-editor init ./my-project            # Create project
spec-editor run -p ./my-project          # Run agent dialogue
spec-editor view -p ./my-project         # Interactive Mermaid graph
spec-editor validate -p ./my-project     # Validate specification
spec-editor status -p ./my-project       # Show spec status
spec-editor export -p ./my-project       # Export to SRS/TRLC/OpenAPI/Jira/HTML
spec-editor mcp                          # Start MCP server (20+ tools)

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Configuration

Edit agents.yaml to choose your provider:

agents:
  agent_1:
    provider: deepseek     # or openai, anthropic
    model: deepseek/deepseek-reasoner
    temperature: 0.7
  agent_2:
    provider: deepseek
    model: deepseek/deepseek-reasoner
    temperature: 0.7
  orchestrator:
    provider: deepseek
    model: deepseek/deepseek-reasoner

More configuration options are available through the VS Code extension: Ctrl+Shift+P → type Spec Editor to access settings, project switching, and MCP controls.

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Contributing

Prompts are the engine of Spec Editor. Better prompts = better specifications.

• Language packs — translations for EN, RU, ES, FR, DE. Missing your language? Add prompts/xx.yaml and open a PR.
• LLM-specific tuning — DeepSeek, GPT-4, Claude each respond differently. Share your tuned prompts.
• Few-shot examples — help us add domain-specific examples.

Got ideas? Open an issue or submit a PR — we review everything.

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Documentation

• Quickstart — 5-minute setup
• Architecture — pipeline, components, CLI reference
• MCP API Reference — MCP server tools
• Extension Integration — VS Code + MCP setup
• Contributing Prompts — how to improve agent quality
• CONTRIBUTING.md — code contributions
• CHANGELOG.md — release history

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License

Apache 2.0 — see LICENSE.