analyze CODTECT
CODITECT (Core) is a distributed intelligence framework that layers a track/skill–based multi-agent system, memory, and tooling around existing coding assistants to preserve context, orchestrate work, and enforce standards across projects.1
What CODITECT Is
- A project-local framework that creates a
.coditect/(symlinked as.claude/) directory containing agents, commands, skills, scripts, workflows, and memory to augment tools like Claude, GPT, Gemini, Cursor, and Cody.1 - Designed to solve “stateless LLM session” pain by encoding project standards, decisions, and context into structured artifacts that LLMs can query and execute against.1
Core Capabilities
- 208+ specialist agents (e.g., security-specialist, rust-expert, devops-engineer) encoded as markdown with YAML frontmatter.1
- 325+ commands implementing slash-style workflows (
/deploy,/security-scan,/git-sync) plus semantic command mapping via a Cross-LLM Bridge.1 - 380+ skills (API design, CI/CD patterns, testing templates) that can be selectively loaded per track to minimize context size.1
- 1,151+ workflows (N8N-compatible) to automate multi-step operations, integrated with scripts and hooks.1
- MEMORY-CONTEXT system with SHA-256 deduplication, multi-LLM session exports, and context redaction/PII controls.1
Architecture & Data Model
- Python 3.10+ codebase, with pytest, black, isort, mypy, flake8 for quality; optional RabbitMQ via
aio-pika.1 - Multi-tier SQLite storage under
~/.coditect-data/context-storage/:platform.dbfor component metadata (regenerable).org.dbfor critical decisions, learnings, and error solutions (daily backup).sessions.dbfor messages, tool analytics, token economics (regenerable).projects.dbfor project metadata, embeddings, content hashes.1
- Per-LLM session storage directories (Claude, Codex, Gemini, KIMI) and standardized export paths (
/sx→/cx).1
Repository Layout
agents/,commands/,skills/,scripts/,hooks/,workflows/,prompts/,tools/pluscontext-storage/,config/,internal/,docs/,distribution/, and standards for production folder structure.1- All Markdown components follow a strict YAML frontmatter schema including
component_type, status, tags, MoE metadata, and audit fields.1
Track/Skill System & CEF
- PILOT Parallel Execution Plan with 37 tracks (A–N, O–AA, AB–AK) and 15+ active tracks for pilot.1
- CODITECT Experience Framework (CEF) maps tracks ↔ skills bi-laterally:
- Track frontmatter declares
associated_skills. - Skills declare
trackandcef_track. - Auto-generated index:
internal/project/track-skills-index.md.1
- Track frontmatter declares
- Operationally, this enables loading only the skills for the active track (e.g., Track C / DevOps Infra ~12 skills) rather than all 376, yielding ~90–97% context reduction.1
Example Track Usage
- Task “Set up Docker deployment pipeline”:
- Map to Track C (DevOps Infra), optionally Track A for backend coupling.
- Use task ID like
C.2.1.5, load Track C skills (docker-patterns,k8s-patterns), and call/agent devops-engineer.1
Orchestration & Automation Model
- Multi-agent orchestration with a five-phase lifecycle for components: Create → Improve → Index → Activate → Context.1
- Commands and agents are activated when frontmatter is valid, status is
active, and indexed intoplatform.db;/component-stats,/classify, and/component-lifecycledrive management.1 - Cross-LLM Bridge allows natural language instead of explicit slash commands; it parses intent, maps to commands, extracts parameters, and routes to the optimal LLM.1
- Experience packs (e.g.,
devops-engineer,backend-developer,ml-engineer) pre-bundle track/skill subsets to create role-based personas with 90–99% token reduction.1
Governance, Security, and Workflow
- Strong safety directives:
- No destructive shell commands (e.g.,
rm -rf) without explicit user confirmation. - Protected installation locations (
~/Library/Application Support/CODITECT/core/etc.) are read-only; all edits must happen in the Git submodule.1
- No destructive shell commands (e.g.,
- Secrets management enforced via
.gitignore, config location (~/.config/coditect), and helper loaders.1 - Development workflow:
- Session start with
/orient, agent discovery via/whichand/suggest-agent, session export/sx, and context capture/cx.1
- Session start with
- Task IDs are mandatory for every tool call, in the
{Track}.{Section}.{Task}.{Subtask}: Descriptionformat (e.g.,A.3.2.1: Add Stripe webhook endpoint) to maintain traceability.1
If you tell me what angle you want next (e.g., “threat model,” “MoE design critique,” “how to port this to LangGraph”), I can produce a focused, copy-pasteable prompt for that analysis.
identify the market, market size TAM, SAM, SOM, intellectual property, moat, Vision, Focus & Commitment
Founding Team & Leadership Business Model & Value Proposition Customer Discovery & Market Validation Competitive Landscape & Differentiation Product/Service Development Financial Readiness & Projections Capital Strategy & Investor Preparedness Presentation & Communication Skills Regulatory Awareness & Industry Barriers Technology & Intellectual Property
CODITECT sits in the rapidly growing AI code assistant / agentic dev tooling market and is positioned as a framework that adds multi-agent orchestration, memory, and governance around IDE-native assistants for serious engineering teams.2345
Market, TAM / SAM / SOM
- Market definition: AI code assistants and agentic developer platforms used by professional and enterprise teams (e.g., Copilot, Claude Code, Cursor, Cody, Windsurf, CodeGPT, etc.).637
- TAM (global):
- SAM (serviceable):
- CODITECT is aimed at orgs that want multi-LLM, multi-agent orchestration, compliance-friendly context handling, and IDE/tool-agnostic augmentation (Claude Code, Cursor, Cody, etc.).372
- A reasonable working SAM for planning is the enterprise / serious‑team segment of AI code assistants and orchestration, i.e., multi‑billion USD in the 2026–2030 horizon, centered on companies with 10+ engineers and governance needs.810
- SOM (obtainable):
Vision, Focus & Commitment
- Vision: Turn fragmented AI coding tools into a coherent, governed, multi-agent development framework that preserves context, enforces standards, and works across any IDE assistant or LLM.1223
- Focus:
- Commitment:
- Explicitly versioned standards (ADR-010, ADR-012, ADR-118, ADR-122, ADR-136–138), production folder standards, and strict coding/testing guidelines show long-term commitment to a cohesive, opinionated framework.2
Founding Team & Leadership
- AZ1.AI is led by a founder CEO/CTO with deep enterprise/ERP and agentic systems background, positioned publicly as builder of “Coditect – Enterprise Agentic Platform.”45
- The AGENTS doc is authored and maintained by the same founder, which indicates a hands-on technical leadership style and strong alignment between product vision and implementation.52
Business Model & Value Proposition
- Value proposition (for teams):
- Dramatically cut “re-explaining” context by storing decisions, skills, and workflows per project; enforce standards; orchestrate multiple agents in a repeatable way on top of IDE tools.632
- Reduce token cost and context bloat via track/skill mapping and memory-deduplication, enabling multi-LLM usage without exploding spend.2
- Provide an architecture and governance layer that standardizes how AI work is logged, attributed, and audited across LLMs and tools.2
- Potential business models (implied by AZ1):
Customer Discovery & Market Validation
- The framework is explicitly built around the pain that “AI assistants start each session with zero project context,” a widely reported problem in AI dev tooling literature and blogs.13362
- Architectural features (multi-LLM exports, attribution, PII handling, 4‑tier DB, production folder standards) reflect feedback from enterprise and regulated environments where governance and auditability are buying drivers.1082
- Public positioning of AZ1.AI as an “agentic systems development & deployment” partner suggests early design/consulting work with enterprise clients, which doubles as validation.114
Competitive Landscape & Differentiation
- Competitors / comparables:
- CODITECT differentiation:
- Tool-agnostic; it sits “around” Claude, GPT, Gemini, Cody, Cursor, KIMI and routes intelligently, rather than competing as yet another IDE plugin.32
- Rich multi-agent inventory (208 agents, 325+ commands, 380 skills, 1,151 workflows) structured via CEF track/skill matrix and experience packs, not one generic assistant.2
- Strong emphasis on governance: task ID protocol, LLM attribution, session logs, PII redaction, protected installation, and ADR-driven architecture, which most IDE tools only partially address.8102
- Distributed intelligence
.coditectfolder deployed per project, enabling cross-IDE and cross-LLM consistency.2
Product / Service Development
- CODITECT Core is already a substantial, versioned codebase with:
- Canonical directory layout, Python package, test suite, dev tooling, and 2,600+ components (agents, commands, skills, scripts, hooks, workflows).2
- PILOT Execution Plan with track status, showing backend, frontend, DevOps, security, testing, docs, and autonomy tracks mostly 75–100% complete; other tracks queued post-pilot.2
- Integration points cover:
- IDE agents (Claude Code, Codex, Gemini, KIMI) via session directories and exports.
- N8N workflows, MCP servers, and CLI tools via scripts and
tools/.2
Financial Readiness & Projections (Qualitative)
- No explicit revenue or projections in the provided materials, but:
- Enterprise TAM/SAM and consulting-oriented offerings (terms of service describe “agentic systems development and integration services”) imply a path of high-value contracts plus platform revenue.411
- Architecture (multi-tenant databases, backup tiers, workstation broker, commerce APIs) is aligned with eventual recurring SaaS revenue and usage-based value metrics (tokens, sessions, projects).2
- For an investor deck, it would be reasonable to model:
Capital Strategy & Investor Preparedness
- The depth of ADRs, PILOT plan, and track metrics are investor-friendly artifacts demonstrating disciplined execution and a fundable technical roadmap.2
- An attractive capital strategy would emphasize:
Regulatory Awareness & Industry Barriers
- The design incorporates:
- Industry barriers:
Technology & Intellectual Property / Moat
IP & Technology Assets
- Proprietary framework (ALL RIGHTS RESERVED) with:
- The
.coditectworkspace pattern, CEF mapping, and task-ID + attribution protocols collectively form a behavioral and architectural moat more than just raw code.2
Moat Dimensions
- Product moat:
- Deep integration of skills, agents, workflows, and track-based loading is non-trivial to replicate and benefits heavily from cumulative content and tuning.2
- Process moat:
- Strong internal standards (code style, task IDs, classification and activation pipeline, MoE verification judges) make the system hard to clone without adopting the same rigor.2
- Data moat:
org.db,projects.db, andsessions.dbaccumulate unique organizational knowledge, decisions, and error solutions over time, which are highly defensible and customer‑specific.2
- Network / ecosystem moat (potential):
If you want, I can next draft a single, investor-ready narrative (“CODITECT Investment Thesis”) or a structured slide outline that walks through these sections in pitch-deck order.
founders: Hal Casteel CEO/CTO, FOUNDER/Advisor, Will McKinley, CPO, Matias van Vliet COO/CMO, Founder
The CODITECT founding team combines deep ERP/enterprise engineering, AI/UX, and sustainability‑driven go‑to‑market and capital strategy experience.181920
Hal Casteel – CEO/CTO, Founder
- CEO and co‑founder of AZ1.AI, developer of CODITECT, positioned as an “Enterprise Agentic Platform.”2118
- Background: decade‑plus as NetSuite solution architect and ERP lead at Oracle, Capgemini, Cornerstone, Red Book Connect, and GRAIL (ERP manager and supply chain systems lead). This underpins CODITECT’s enterprise‑grade architecture, data governance, and integration posture.18
- Publicly represents AZ1.AI in forums and events on practical AI for business and agentic systems, reinforcing thought‑leadership positioning.22232425
Will McKinley – CPO (Product)
- Senior software engineer with strong UI and product workflow experience, including staff engineer tenure at GRAIL working on internal apps that controlled complex product workflows, payments, and medical‑trial operations.20
- Led UI standards, internal app frameworks, and integrations with Okta, Tableau, and PCI‑compliant environments, which maps directly to CODITECT’s focus on UX, security, and enterprise‑integrated workflows for AI agents.20
Matias Meirelles van Vliet – COO/CMO, Founder
- COO/CMO at Coditect.ai and co‑founder, with prior experience in impact investing, sustainability strategy, and startup acceleration (Terra Roxa Brewing, BatterEASE, healthcare innovation programs, accelerators).19
- Expertise in financial modeling, ESG analysis, and venture capital (VC Lab Venture Institute Fellow), plus hands‑on experience designing metrics and impact tracking, which strengthens CODITECT’s capital strategy, GTM narratives, and data‑driven storytelling to investors and customers.19
If you’d like, I can now map these founder profiles directly onto an “Investment‑grade Team Slide” (roles, strengths, risks, mitigation) tailored for a CODITECT seed/Series A deck.
Capability Domains
- Software Development & Engineering Agents: orchestrator, debugger, code-reviewer, security-specialist, devops-engineer, database-architect, cloud-architect, frontend-react-typescript-expert, rust-expert-developer, and 40+ more Workflows: 100+ covering project lifecycle, code quality, testing, deployment, documentation Use Cases: Full-stack application development API design and implementation Database schema design and optimization CI/CD pipeline creation Security audits and vulnerability assessment Performance optimization Code review and quality assurance Example: Task( subagent_type="orchestrator", prompt="Coordinate complete feature development from requirements to deployment for user authentication system" )
- Business Strategy & Planning Agents: business-intelligence-analyst, venture-capital-business-analyst, competitive-market-analyst, business-analytics Workflows: 100+ covering business planning, market analysis, financial modeling Use Cases: Business plan creation Market sizing (TAM/SAM/SOM) Financial modeling and projections Unit economics analysis Go-to-market strategy Pricing strategy development Investment readiness assessment Pitch deck preparation Example: Task( subagent_type="business-intelligence-analyst", prompt="Create comprehensive business plan for AI-powered SaaS startup including market analysis, financial projections, competitive positioning, and go-to-market strategy" )
- Research & Intelligence Agents: claude-research-agent, web-search-researcher, biographical-researcher, competitive-market-analyst Workflows: 50+ covering web research, competitive intelligence, market research, academic research Use Cases: Competitive landscape analysis Market research and trend analysis Academic literature review Patent and IP research Person/company background research Industry analysis Technology assessment Customer research and personas Example: Task( subagent_type="competitive-market-analyst", prompt="Research AI-first IDE market. Identify top 10 competitors, analyze their pricing, features, funding, and market positioning. Create detailed comparison matrix." )
- Education & Training Agents: ai-curriculum-specialist, assessment-creation-agent, educational-content-generator Workflows: 50+ covering curriculum design, assessment creation, learning management Use Cases: Course curriculum development Learning path design Assessment and quiz creation Educational content generation Training material development Certification program design Skill gap analysis Learning outcome measurement Example: Task( subagent_type="ai-curriculum-specialist", prompt="Design comprehensive 8-week machine learning bootcamp curriculum. Include learning objectives, hands-on projects, assessments, and estimated completion times for each module." )
- Content & Marketing Agents: content-marketing, codi-documentation-writer, educational-content-generator Workflows: 50+ covering content strategy, marketing automation, documentation Use Cases: Content marketing strategy Editorial calendar planning SEO content optimization Social media strategy Email marketing campaigns Technical documentation Knowledge base creation Video/podcast scripting Example: Task( subagent_type="content-marketing", prompt="Create 90-day content marketing strategy for B2B SaaS product. Include blog topics, social media calendar, SEO keywords, distribution channels, and conversion goals." )
- Finance & Investment Agents: venture-capital-business-analyst, business-intelligence-analyst Workflows: 50+ covering financial analysis, investment research, portfolio management Use Cases: Financial modeling Investment analysis Due diligence Portfolio analysis Risk assessment Valuation analysis Unit economics Cap table management Fundraising strategy Example: Task( subagent_type="venture-capital-business-analyst", prompt="Perform Series A due diligence for [startup]. Analyze financials, unit economics, market opportunity, competitive moat, and team. Provide investment recommendation." )
- HR & People Operations Workflows: 50+ in HR-LEGAL-FINANCE-WORKFLOWS.md Use Cases: Hiring pipeline management Job description creation Interview process design Onboarding program development Performance review systems Employee handbook creation Compensation analysis Team structure planning Example: Task( subagent_type="orchestrator", prompt="Design complete hiring pipeline for senior engineering role. Include job description, screening criteria, interview stages, assessment rubrics, and offer process." )
- Legal & Compliance Workflows: 50+ in HR-LEGAL-FINANCE-WORKFLOWS.md and LEGAL-SERVICES-INDUSTRY-WORKFLOWS.md Use Cases: Contract review preparation Compliance checklist creation Policy documentation Terms of service drafting Privacy policy development Regulatory research Risk assessment frameworks Audit preparation Example: Task( subagent_type="orchestrator", prompt="Create GDPR compliance checklist for SaaS application. Include data mapping, consent requirements, user rights implementation, and documentation needs." )
- Healthcare Workflows: 50+ in HEALTHCARE-INDUSTRY-WORKFLOWS.md and EDUCATION-HEALTHCARE-WORKFLOWS.md Use Cases: Patient workflow optimization Clinical documentation Healthcare compliance Medical education content Research protocol design Quality improvement initiatives Patient education materials Healthcare analytics
- Real Estate Workflows: 50+ in WORKFLOW-DEFINITIONS-REAL-ESTATE-EVENTS-TRAVEL.md Use Cases: Property analysis Market research Investment analysis Listing optimization Client management Transaction coordination Due diligence checklists Portfolio management
- Manufacturing & Operations Workflows: 50+ in MANUFACTURING-AGRICULTURE-WORKFLOWS.md and OPERATIONS-PROCESS-WORKFLOWS.md Use Cases: Process optimization Quality management Supply chain analysis Inventory management Production planning Vendor management Procurement workflows Operational metrics
- Retail & E-commerce Workflows: 50+ in RETAIL-ECOMMERCE-INDUSTRY-WORKFLOWS.md Use Cases: Product catalog management Pricing strategy Customer segmentation Inventory optimization Sales analysis Customer journey mapping Conversion optimization Loyalty program design
- Financial Services Workflows: 50+ in FINANCIAL-SERVICES-INDUSTRY-WORKFLOWS.md Use Cases: Risk modeling Compliance automation Client reporting Portfolio analysis Trading strategies Regulatory reporting Client onboarding Due diligence
- Creative & Freelance Workflows: 50+ in CREATIVE-PROFESSIONAL-FREELANCE-WORKFLOWS.md and PRODUCT-CREATIVE-WORKFLOWS.md Use Cases: Client management Project scoping Proposal creation Contract management Portfolio development Rate optimization Creative brief development Deliverable management
- Personal & Lifestyle Workflows: 50+ in WORKFLOWS-PERSONAL-LIFESTYLE.md Use Cases: Personal productivity Goal setting and tracking Learning plans Health and wellness Financial planning Travel planning Home management Life administration How to Access These Capabilities
- Use the /which Command /which build a business plan
→ Recommends: business-intelligence-analyst
/which analyze competitors
→ Recommends: competitive-market-analyst
/which create course curriculum
→ Recommends: ai-curriculum-specialist
- Invoke Agents Directly /agent business-intelligence-analyst "Create market analysis for [market]" /agent competitive-market-analyst "Research competitors for [product]" /agent ai-curriculum-specialist "Design curriculum for [topic]"
- Browse Workflow Library
See all workflow categories
ls docs/workflows/
Business workflows
cat docs/workflows/BUSINESS-SALES-WORKFLOWS.md
Research workflows
cat docs/workflows/RESEARCH-INTELLIGENCE-WORKFLOWS.md
All 15 domain libraries
cat docs/workflows/WORKFLOW-LIBRARY-INDEX.md
- Search the Knowledge Base /cxq "agent for market research" /cxq "workflow for hiring" /cxq "how to create business plan"
Workflow Library Quick Reference DomainWorkflowsFile Essential Development 50 50-ESSENTIAL-WORKFLOWS.md AI/ML & Data 50 WORKFLOW-DEFINITIONS-AI-ML-DATA.md Business & Sales 50 BUSINESS-SALES-WORKFLOWS.md Business Operations 50 BUSINESS-OPERATIONS-WORKFLOWS.md Operations & Process 50 OPERATIONS-PROCESS-WORKFLOWS.md HR, Legal, Finance 50 HR-LEGAL-FINANCE-WORKFLOWS.md Creative & Freelance 50 CREATIVE-PROFESSIONAL-FREELANCE-WORKFLOWS.md Product & Creative 50 PRODUCT-CREATIVE-WORKFLOWS.md Research & Intelligence 50 RESEARCH-INTELLIGENCE-WORKFLOWS.md Investment & Trading 50 INVESTMENT-TRADING-CRYPTO-WORKFLOWS.md Education & Healthcare 50 EDUCATION-HEALTHCARE-WORKFLOWS.md Real Estate, Events, Travel 50 WORKFLOW-DEFINITIONS-REAL-ESTATE-EVENTS-TRAVEL.md Manufacturing & Agriculture 50 MANUFACTURING-AGRICULTURE-WORKFLOWS.md Retail & E-commerce 50 RETAIL-ECOMMERCE-INDUSTRY-WORKFLOWS.md Personal & Lifestyle 50 WORKFLOWS-PERSONAL-LIFESTYLE.md Total: 750+ workflows across 15 domains Agent Categories Quick Reference Business & Strategy AgentSpecialty business-intelligence-analyst Market sizing, financial modeling, unit economics venture-capital-business-analyst Investment readiness, valuations, pitch prep competitive-market-analyst Competitive intelligence, market positioning business-analytics Business metrics, KPI analysis content-marketing Marketing strategy, content planning Research & Intelligence AgentSpecialty claude-research-agent Deep research synthesis web-search-researcher Current information, web research biographical-researcher People/company research profiles Education & Content AgentSpecialty ai-curriculum-specialist Course design, learning paths assessment-creation-agent Quizzes, evaluations, rubrics educational-content-generator Learning materials, tutorials codi-documentation-writer Technical docs, guides, knowledge bases Development & DevOps AgentSpecialty orchestrator Multi-agent coordination senior-architect System design, architecture debugger Bug investigation, troubleshooting code-reviewer Code quality, best practices security-specialist Security audits, vulnerability assessment devops-engineer CI/CD, infrastructure, deployment database-architect Database design, optimization cloud-architect Cloud infrastructure, scaling Getting Started by Domain For Business Users Start with: /which [your task] to find the right agent Try: business-intelligence-analyst for strategy/analysis Explore: docs/workflows/BUSINESS-SALES-WORKFLOWS.md For Researchers Start with: claude-research-agent or competitive-market-analyst Try: web-search-researcher for current information Explore: docs/workflows/RESEARCH-INTELLIGENCE-WORKFLOWS.md For Educators Start with: ai-curriculum-specialist for course design Try: assessment-creation-agent for quizzes/evaluations Explore: docs/workflows/EDUCATION-HEALTHCARE-WORKFLOWS.md For Developers Start with: orchestrator for complex projects Try: debugger, code-reviewer, devops-engineer for specific tasks Explore: docs/workflows/50-ESSENTIAL-WORKFLOWS.md
CODITECT exposes 15 capability domains that combine specialized agents plus ~750+ workflows to cover both deep software engineering and broad business/operations use cases.34
Capability Domains (1–6)
- Software Development & Engineering
- Agents: orchestrator, debugger, code-reviewer, security-specialist, devops-engineer, database-architect, cloud-architect, frontend-react-typescript-expert, rust-expert-developer, 40+ more.34
- Workflows: 100+ for full lifecycle (feature dev, testing, deployment, docs).34
- Typical tasks: full-stack features, API design, schema design/tuning, CI/CD pipelines, security audits, perf optimization, code review.34
- Example call:
Task(subagent_type="orchestrator", prompt="Coordinate complete feature development...").34
- Business Strategy & Planning
- Agents: business-intelligence-analyst, venture-capital-business-analyst, competitive-market-analyst, business-analytics.34
- Workflows: 100+ for planning, TAM/SAM/SOM, financial models, unit economics, GTM, pricing, investment readiness, pitch decks.34
- Example:
subagent_type="business-intelligence-analyst"for full business plan and GTM.34
- Research & Intelligence
- Agents: claude-research-agent, web-search-researcher, biographical-researcher, competitive-market-analyst.34
- Workflows: 50+ for web/market/academic research, IP scans, industry and tech assessment, persona research.34
- Example: competitive-market-analyst performing AI-first IDE market scan and comparison matrix.34
- Education & Training
- Agents: ai-curriculum-specialist, assessment-creation-agent, educational-content-generator.34
- Workflows: 50+ for curricula, learning paths, assessments, training materials, certification, skill-gap analysis.34
- Example: 8‑week ML bootcamp curriculum with objectives, projects, assessments, and time estimates.34
- Content & Marketing
- Finance & Investment
Capability Domains (7–15)
- HR & People Operations
- Legal & Compliance
- Healthcare
- Real Estate
- Manufacturing & Operations
- Retail & E‑commerce
- Financial Services
- Creative & Freelance
- Personal & Lifestyle
Access Patterns & Libraries
- Discover agents:
/which <task>to route to the right agent, e.g./which build a business plan→ business-intelligence-analyst.34 - Direct invocation:
/agent business-intelligence-analyst "Create market analysis for [market]", etc.34 - Browse workflows:
ls docs/workflows/plus domain files likeBUSINESS-SALES-WORKFLOWS.md,RESEARCH-INTELLIGENCE-WORKFLOWS.md,50-ESSENTIAL-WORKFLOWS.md, etc.34 - Search knowledge base:
/cxq "agent for market research"or/cxq "workflow for hiring".34
Workflow Library Summary
| Domain group | Workflows | Index file name |
|---|---|---|
| Essential Development | 50 | 50-ESSENTIAL-WORKFLOWS.md |
| AI/ML & Data | 50 | WORKFLOW-DEFINITIONS-AI-ML-DATA.md |
| Business & Sales | 50 | BUSINESS-SALES-WORKFLOWS.md |
| Business Operations | 50 | BUSINESS-OPERATIONS-WORKFLOWS.md |
| Operations & Process | 50 | OPERATIONS-PROCESS-WORKFLOWS.md |
| HR, Legal, Finance | 50 | HR-LEGAL-FINANCE-WORKFLOWS.md |
| Creative & Freelance | 50 | CREATIVE-PROFESSIONAL-FREELANCE-WORKFLOWS.md |
| Product & Creative | 50 | PRODUCT-CREATIVE-WORKFLOWS.md |
| Research & Intelligence | 50 | RESEARCH-INTELLIGENCE-WORKFLOWS.md |
| Investment & Trading | 50 | INVESTMENT-TRADING-CRYPTO-WORKFLOWS.md |
| Education & Healthcare | 50 | EDUCATION-HEALTHCARE-WORKFLOWS.md |
| Real Estate, Events, Travel | 50 | WORKFLOW-DEFINITIONS-REAL-ESTATE-EVENTS-TRAVEL.md |
| Manufacturing & Agriculture | 50 | MANUFACTURING-AGRICULTURE-WORKFLOWS.md |
| Retail & E‑commerce | 50 | RETAIL-ECOMMERCE-INDUSTRY-WORKFLOWS.md |
| Personal & Lifestyle | 50 | WORKFLOWS-PERSONAL-LIFESTYLE.md |
Total: ~750+ workflows across the 15 domains.34
If you want a promptable abstraction, next I can compress this into a “Domain Capability Router” spec that you can paste into an agent to choose the right domain/agent/workflow given an arbitrary business or engineering task.
Transform Ideas into Executable Workflows
AgentFlow uses AI-powered agents to analyze your workflow descriptions, generate diagrams, and create automated processes in N8N - all from natural language. Get StartedView Demo How It Works Describe Your Workflow Simply describe your workflow in natural language. No technical knowledge required. AI Analyzes & Decomposes Multi-agent system analyzes your workflow, breaks it into tasks, and identifies dependencies. Generate Diagrams Automatically create flowcharts and Gantt charts to visualize your workflow. Deploy to N8N Translate to N8N format and deploy your workflow for automated execution. Why AgentFlow? No Code Required Describe workflows in plain English - our AI handles the rest Real-time Updates Watch your workflow being analyzed and built in real-time Multi-Agent Intelligence Powered by DSPy and LangChain for accurate workflow analysis N8N Integration Deploy to N8N with 400+ integrations at your fingertips Ready to Automate Your Workflows? Join AgentFlow today and transform the way you build automated workflows. Get started in minutes, no credit card required. Start Building Workflows
AgentFlow is positioned as a natural‑language → executable N8N workflow generator using a multi‑agent LLM stack (DSPy + LangChain), and it overlaps heavily with CODITECT’s workflow domain but at a different layer.35363738
What AgentFlow Does
- Converts natural language descriptions of workflows into:
- Core promise: “no code required” prompt → workflow on the n8n canvas, with AI agents planning the architecture and wiring nodes using N8N’s AI builder / agent nodes.393836
- Uses multi-agent reasoning built on LangChain/DSPy and N8N’s AI agent nodes to analyze inputs, pick tools, and generate workflow structures.40413835
Relationship to CODITECT
- CODITECT already defines ~750+ workflows across 15 domains (dev, business, research, finance, healthcare, etc.) as reusable patterns and N8N‑compatible definitions.42
- AgentFlow‑style capability is essentially:
- Strategically: CODITECT can treat AgentFlow‑like functionality as:
Strategic Fit / Differentiation
- N8N and others already ship AI workflow builders and AI agent nodes that allow prompt → workflow; they are generic and not domain‑rich.38353637
- CODITECT’s advantages over a pure AgentFlow clone:
- Deep, domain-specific workflow inventory (15 capability domains, 750+ patterns) rather than only structural generation.42
- Strong governance (task IDs, ADRs, attribution, PII levels) and multi‑LLM orchestration, which generic N8N AI builders do not focus on.354042
- Tight coupling with software‑engineering agents (orchestrator, devops-engineer, security-specialist, database-architect, etc.) so it can generate both application code and the automation around it.3542
Suggested Prompt You Can Paste (CODITECT + “AgentFlow”)
You can drop this as a task to a CODITECT orchestrator agent to design an internal AgentFlow module on top of your existing library:
Design an “AgentFlow” capability on top of CODITECT that converts natural-language workflow descriptions into executable N8N workflows using the existing 15 domain libraries and ~750+ workflow definitions. Requirements:
- Map user descriptions to: (a) one or more workflow templates from docs/workflows/*.md, (b) missing steps that must be synthesized.
- Use CODITECT agents (orchestrator, business-intelligence-analyst, devops-engineer, etc.) to:
- Decompose the workflow into tasks and dependencies.
- Select appropriate N8N nodes and integrations.
- Generate both a visual description (mermaid) and N8N workflow JSON.
- Implement a CLI and API:
agentflow plan,agentflow diagram,agentflow n8n-deploy.- Integrate with CODITECT’s MEMORY-CONTEXT system for:
- Reusing prior workflow components.
- Logging task IDs, LLM attribution, and PII levels.
- Explicitly compare this design to N8N’s own AI Workflow Builder and AI Agent nodes, and justify how CODITECT’s domain-specific workflows and governance provide a stronger enterprise moat.
How to get started with AgentFlow tutorial
Here’s a minimal, concrete “getting started with AgentFlow” tutorial tailored to CODITECT.
1. Concept: AgentFlow on top of CODITECT
AgentFlow = CODITECT module that takes a natural‑language workflow description and produces:
- A decomposed task graph (steps, dependencies).51
- A visual spec (Mermaid flowchart, optional Gantt).51
- An executable N8N workflow JSON, wired to existing integrations.51
It should reuse:
- CODITECT orchestrator and domain agents (business-intelligence-analyst, competitive-market-analyst, devops-engineer, etc.).51
- Existing 750+ domain workflows in
docs/workflows/*.mdandworkflows/**/*.yaml.51
2. Minimal Architecture
Components:
commands/agentflow-plan.md– parse & decompose workflow from NL.commands/agentflow-diagram.md– generate Mermaid diagrams.commands/agentflow-n8n-deploy.md– emit + POST N8N JSON.scripts/agentflow/plan.py– planning + task graph builder.scripts/agentflow/n8n_generate.py– template → N8N nodes.scripts/agentflow/n8n_deploy.py– REST client to N8N instance.workflows/agentflow/*.yaml– a few canonical patterns.
All of this plugs into existing CODITECT structure and command lifecycle.51
3. Step‑by‑Step Tutorial
Step 1 – Create the AgentFlow planning command
- Create
commands/agentflow-plan.mdwith frontmatter:
---
title: AgentFlow Plan
component_type: command
version: 1.0.0
status: draft
invocation: /agentflow-plan "<workflow_description>"
tags: [agentflow, workflow, planning, n8n]
summary: Analyze natural language workflow descriptions and produce a structured task graph with dependencies.
requires_confirmation: false
---
- In the body, define behavior:
- Call the orchestrator agent when invoked.
- Use
/which+/cxqto locate relevant domain workflows and agents. - Output a JSON task graph:
{
"title": "User authentication workflow",
"tasks": [
{"id": "T1", "name": "Capture requirements", "depends_on": []},
{"id": "T2", "name": "Design data model", "depends_on": ["T1"]},
{"id": "T3", "name": "Implement backend endpoints", "depends_on": ["T2"]},
{"id": "T4", "name": "Integrate with N8N triggers/actions", "depends_on": ["T3"]},
{"id": "T5", "name": "Write tests", "depends_on": ["T3"]},
{"id": "T6", "name": "Deploy to staging", "depends_on": ["T4", "T5"]}
]
}
- Run:
/classify commands/agentflow-plan.md
/component-lifecycle activate command agentflow-plan
Step 2 – Implement the planner script
Create scripts/agentflow/plan.py:
#!/usr/bin/env python3
"""AgentFlow planner: NL workflow -> task graph."""
from __future__ import annotations
from typing import List, Dict, Any
def plan_workflow(description: str) -> Dict[str, Any]:
# 1) Ask orchestrator + domain agents for decomposition via LLM.
# 2) Map to existing CODITECT workflow templates when possible.
# 3) Return normalized graph.
raise NotImplementedError("Wire this to CODITECT LLM orchestration.")
Tie this script to the command via scripts: section in agentflow-plan.md (if you use that pattern) or via an internal ADR.51
Step 3 – Add diagram generation
Create commands/agentflow-diagram.md:
- Input: task graph JSON.
- Output: Mermaid flowchart + optional Gantt.
Example Mermaid:
Usage:
/agentflow-plan "Automate lead capture from website form to CRM with email follow-up"
/agentflow-diagram "<paste task graph JSON>"
Step 4 – Generate N8N workflows
Create commands/agentflow-n8n-deploy.md:
- Inputs: task graph JSON + N8N base URL and API key (from secure config).51
- Behavior:
- Map tasks to N8N node templates (HTTP trigger, webhook, CRM node, email node, function nodes).
- POST the assembled JSON to N8N’s
/workflowsAPI.
Pseudo:
/agentflow-n8n-deploy "<task_graph_json>" --n8n-url https://n8n.example.com --credentials-id SALES_PIPELINE
Step 5 – Wire to CODITECT workflows
For each domain:
- Use
/cxqto find relevant templates, e.g.:
/cxq "workflow for CRM lead capture"
/cxq "workflow for onboarding email sequence"
- In
scripts/agentflow/n8n_generate.py, implement a mapping layer:- If the graph step matches a known template (from
docs/workflows/BUSINESS-SALES-WORKFLOWS.mdorworkflows/**/*.yaml), reuse it. - Otherwise, synthesize a new node pattern and store it as a new workflow for later reuse (following CODITECT component creation workflow).51
- If the graph step matches a known template (from
Step 6 – Enforce CODITECT governance
For every AgentFlow command invocation:
- Include a Task ID in description:
B.4.1.2: AgentFlow plan for lead capture automation.51 - Log session with
/session-logincluding:
4. Minimal “Hello World” Tutorial Script
Here’s a single prompt you can paste into CODITECT (e.g., to the orchestrator agent) to build a first AgentFlow “hello world”:
Implement a minimal AgentFlow prototype in CODITECT that:
- Adds
/agentflow-plan,/agentflow-diagram, and/agentflow-n8n-deploycommands;- Uses the orchestrator and appropriate domain agents to decompose a natural-language workflow description into a task graph;
- Generates a Mermaid flowchart from the graph;
- Maps the graph to an N8N workflow JSON using existing patterns from
docs/workflows/*.mdandworkflows/**/*.yaml;- Deploys the workflow to an N8N instance via REST;
- Follows CODITECT conventions for component frontmatter, task IDs, security, and session logging.
This gives you a concrete “getting started” path while staying fully inside CODITECT’s standards and architecture.51
financial model saas with enterprise value based pricing
A SaaS financial model with enterprise value‑based pricing ties revenue to quantified business outcomes (time saved, costs reduced, revenue lifted) rather than seats or flat tiers.52535455
1. Define the Value Metric
Pick 1–2 metrics that best approximate economic value for your product.535452
- Examples:
- Use customer interviews + historical data to validate that these metrics correlate with perceived value and willingness‑to‑pay.565752
2. Build an Economic Value Model (EVE‑style)
Construct a simple Economic Value Estimation (EVE) model: quantify customer P&L impact and back into a pricing envelope.5857
- For a typical customer segment, model:
- Baseline: current cost / lost opportunity without your product.
- With-product: improved metrics (time, revenue, risk).
- Net annual economic benefit = savings + added margin – switching/implementation cost.58
- Choose a “share of value” you will capture (e.g., 10–30% of annual economic benefit) as the target contract value.5758
Example (developer productivity tool):
- 50 devs, fully loaded cost 150k, 20% time on repetitive tasks.
- You reduce that by 25% → 50 × 150k × 0.20 × 0.25 ≈ 375k of annual savings.
- Capture 20% → target ACV ≈ 75k, then design pricing metrics that roughly land there for that segment.5358
3. Translate Value into Pricing Structure
Combine platform fee + value metric for enterprise.595554
- Structure:
- Ensure:
For an AI/agentic platform:
- Base: 2–3 plans (Growth, Pro, Enterprise) anchored to organization size and governance/SLA level.
- Value metric candidates:
4. SaaS Financial Model Skeleton
Implement in a spreadsheet or db:
Inputs (per segment)
- Customer count by segment (SMB, mid‑market, enterprise).
- Adoption/penetration rates per year.
- Average baseline metrics (users, volume, economic value).
- Chosen value metrics and coefficients (e.g., $ per workflow run, $ per outcome).555358
Revenue
For each cohort and year:
- Base ARR = customer_count × base_price_segment.
- Variable ARR = Σ(value_metric_units × price_per_unit).595455
- Total ARR = base + variable; then apply:
Costs
- COGS: infra, LLM/API costs (often tied to same value metrics), support, onboarding.6160
- S&M: % of revenue or per‑rep model.
- R&D and G&A: headcount‑based, with planned growth.
Compute:
5. Calibration & Iteration
Implement a value‑based pricing process, not a one‑off exercise.586157
- Run structured pricing interviews and WTP surveys with target customers.525657
- Use early contracts as experiments:
- A/B different metric mixes or value shares across segments.
- Track realized value vs. price and adjust multipliers and thresholds over 3–6 months.57
- Embed telemetry: instrument your product to measure the value drivers and feed them into billing / pricing analytics.6157
6. Prompt You Can Reuse (for a detailed model)
You can feed this to a modeling agent:
“Build a 5‑year SaaS financial model for an enterprise AI/agentic platform using value‑based pricing. Use a hybrid of base platform fee plus a variable metric tied to economic value (e.g., workflows executed and hours saved). Include segment‑level assumptions (SMB, mid‑market, enterprise), EVE‑style value modeling per segment, revenue build (new, expansion, churn), unit economics (LTV, CAC, payback), and sensitivity tables for different value‑capture percentages and price per outcome.”
excel template for SaaS value based pricing model
EVE model example for enterprise SaaS pros and cons of value based vs usage based pricing how to calculate willingness to pay in value pricing case studies of SaaS companies using value based pricing
Here's a comprehensive response covering all five queries with actionable resources and frameworks.
1. Excel Templates for SaaS Value-Based Pricing
Free Templates Available
DiscoveringSaaS Value-Based Pricing Template68
- Free Excel/PDF template specifically for value-based pricing
- Includes Marketing ROI plan calculator
- Formula:
Estimated Additional Revenue / Month→Our Price / Month - Strategic framework for proving and communicating ROI
- Download: https://discoveringsaas.com/templates-and-checklists/saas-pricing-template/
SaaS Pricing Model Template (Excel)69
- 6 predefined templates for pricing strategies
- Dynamic pricing adjustments based on selected options
- Features: subscription metrics tracking, financial reports, visualizations
- Includes monthly, quarterly, and annual subscription modeling
The VC Corner SaaS Financial Model70
- Battle-tested template used by founders to raise millions
- Built for pre-seed to Series B startups
- Comprehensive runway forecasting and unit economics
The SaaS CFO Financial Model Template71
- Industry-standard SaaS financial blueprint
- Includes cohort analysis, churn modeling, and revenue projections
- Download available at: https://www.thesaascfo.com/saas-financial-model-excel/
eFinancialModels SaaS Template72
- 4 pricing tiers (Basic, Pro, Premium, Platinum)
- 2 payment plans (monthly/yearly)
- Calculates key metrics: required funding, IRR, CAC, LTV
- Year-over-year operational improvement assumptions
Chargebee SaaS Financial Model Template73
- Plug-and-play Excel template
- Designed for subscription business modeling
2. EVE Model Example for Enterprise SaaS
Economic Value Estimation (EVE) Framework7471
The EVE model (developed by Tom Nagle in The Strategy and Tactics of Pricing) is a system of equations defining how a solution improves customer P&L.74
EVE Model Structure:
Total Economic Value = Reference Value + Differentiation Value
Where:
- Reference Value = Next best alternative cost
- Differentiation Value = Positive value drivers - Negative value drivers
Enterprise SaaS EVE Example:7574
Scenario: Marketing automation platform for mid-market B2B companies
| Component | Calculation | Annual Value |
|---|---|---|
| Reference Value (current solution cost) | Competitor platform + manual labor | $50,000 |
| Positive Value Drivers: | ||
| Increased productivity | 500 hrs saved × $100/hr | +$50,000 |
| Reduced IT costs | Infrastructure reduction | +$30,000 |
| Enhanced security | Risk mitigation value | +$20,000 |
| Lead conversion improvement | 5% lift × $1M pipeline × 3% close rate | +$15,000 |
| Total Positive Value | +$115,000 | |
| Negative Value Drivers: | ||
| Implementation cost | One-time migration | -$10,000 |
| Training time | 40 hrs × $100/hr | -$4,000 |
| Net Differentiation Value | +$101,000 | |
| Total Economic Value | $50,000 + $101,000 | $151,000 |
| Target Pricing (20-30% value capture) | $30,000-$45,000 ACV |
Three Approaches to Value-Based Pricing:74
- Approximate VBP - Use industry benchmarks and comparable pricing
- Derived VBP - Calculate from customer outcomes and metrics
- Direct VBP - Customize pricing per customer through collaborative value discovery
3. Pros and Cons: Value-Based vs. Usage-Based Pricing
Value-Based Pricing
- ✅ Maximizes revenue potential by capturing fair share of value created
- ✅ Aligns price with customer success and ROI
- ✅ Justifies premium pricing through quantified outcomes
- ✅ Strengthens customer relationships through value conversations
- ✅ Creates differentiation from cost-plus competitors
- ✅ Supports higher margins (38-45% for companies like Apple)81
- ✅ Reduces price sensitivity when value is clearly communicated
- ✅ Enables strategic customer segmentation
- ❌ Requires deep customer understanding and ongoing research
- ❌ Complex to implement and communicate
- ❌ Needs strong proof and ROI documentation
- ❌ Difficult to standardize across diverse customer segments
- ❌ Requires sales team training on value selling
- ❌ May face resistance without clear value metrics
- ❌ Challenging to measure intangible benefits
Usage-Based Pricing
- ✅ Scales naturally with customer growth
- ✅ Lower barrier to entry (pay as you grow)
- ✅ Aligns cost with actual consumption
- ✅ Easy to understand and predict
- ✅ Automatic expansion revenue
- ✅ Fair for varying usage patterns
- ❌ Revenue unpredictability for the vendor
- ❌ May discourage product usage (consumption fear)
- ❌ Complex billing and metering infrastructure required
- ❌ Difficult for customers to budget
- ❌ Can lead to bill shock
- ❌ Leaves money on table for high-value, low-usage scenarios
Hybrid Recommendation
Best practice for enterprise SaaS:798283
- Base platform fee (predictable revenue) + value/usage metric (expansion)
- Example: $2k/month platform + $50 per successful workflow execution
- Provides stability while capturing value growth
4. How to Calculate Willingness to Pay (WTP) in Value Pricing
Quantitative Techniques858682
1. Van Westendorp Price Sensitivity Meter82
- Survey questions:
- "At what price would this be too expensive?"
- "At what price would this be expensive but worth considering?"
- "At what price would this be a bargain?"
- "At what price would this be too cheap (suspicious quality)?"
- Identifies acceptable price range from intersection points
2. Gabor-Granger Analysis82
- Tests likelihood of purchase at specific price points
- Maps demand curves: "Would you buy at $X?" (Yes/No)
- Repeat at various prices to find optimal point
3. Conjoint Analysis82
- Evaluates trade-offs between features and price
- Presents packages: Feature set A at $X vs. Feature set B at $Y
- Reveals hidden preferences and feature value attribution
4. Economic Value Model (Quantified)7574
- Calculate total economic value (see EVE model above)
- Survey value ratio tolerance: "What % of savings is fair pricing?"
- Typical enterprise SaaS: 15-30% of annual economic benefit
Qualitative Validation8582
Customer Interviews:
- "What outcomes matter most to your business?"
- "How do you measure success with solutions like ours?"
- "What would justify a premium over alternatives?"
Segmentation Research:85
- Different segments have different WTP
- Segment by: industry, company size, use case intensity, maturity
- Tailor pricing and packaging to each segment's value perception
WTP Calculation Framework82
Step 1: Understand value perception (surveys, interviews)
Step 2: Segment market by value drivers
Step 3: Quantify outcomes per segment (EVE model)
Step 4: Validate WTP range (Van Westendorp, conjoint)
Step 5: Set price within validated range
- High Anchor: Maximum validated WTP
- Floor Price: Minimum for margin/cost coverage
- Competitor Reference: Market context
Step 6: Test with pricing experiments (A/B tests)
Step 7: Iterate based on conversion and retention data
Key Insight:86 WTP is actively shaped by how you communicate and deliver value—not just discovered. Companies that articulate value clearly can increase WTP over time.
5. Case Studies: SaaS Companies Using Value-Based Pricing
HubSpot7681
- Strategy: Tiered value-based pricing (Free CRM → Marketing/Sales/Service Hubs)
- Execution: Plans tailored to customer needs and budgets, feature sets aligned with value drivers
- Results: Exponential growth, wide range of users from SMB to enterprise
- Key: Understanding value customers derive from features enables precise tier creation
Salesforce8176
- Strategy: Tiered SaaS pricing with feature upgrades tied to business outcomes
- Execution:
- Multi-year contracts with price protection
- Clear upgrade paths supporting business growth
- Trailhead training platform for value realization
- AppExchange third-party integrations
- Results:
- $31.35B revenue (2023)
- 20%+ annual recurring revenue growth
- Premium pricing justified by measurable business outcomes
- Strong customer retention through value alignment
Zendesk76
- Strategy: Plans tailored to customer size and complexity (Essential → Enterprise)
- Execution: Aligned pricing with value delivered to different segments
- Results: Effective scaling, high customer satisfaction
- Key: Segment-specific value propositions
Wistia76
- Strategy: Value-based plans focused on user needs (Pro plan with advanced analytics)
- Execution: Customer-centric approach, features tied to business outcomes
- Results: Transformed growth trajectory, improved retention
- Key: Deep understanding of customer needs drove plan design
Apple (Non-SaaS but Instructive)81
- Strategy: Ecosystem value pricing
- Results: 38-45% profit margins
- Key: Value perception from integrated experience justifies premium
Starbucks (Non-SaaS but Instructive)81
- Strategy: Premium pricing for experience value (cozy stores, personalization)
- Key: Intangible value (ambiance, customization) commands higher prices
Actionable Next Steps
For CODITECT specifically:
- Build EVE model for 3 customer segments (SMB, mid-market, enterprise)
- Quantify: developer hours saved, context switching reduction, governance value, multi-LLM cost savings
- Target 20-30% value capture
- Run WTP validation
- Van Westendorp survey with 30-50 prospects
- Conjoint analysis: features vs. price trade-offs
- Design hybrid pricing
- Base: $500-$2k/month (platform, governance, support)
- Variable: $0.10-$0.50 per workflow execution or $50-$200 per "value event" (hours saved threshold)
- Use templates:
- Download DiscoveringSaaS or The SaaS CFO template
- Customize with CODITECT metrics and value drivers
- Document case studies:
- Early customers: measure actual value delivered
- Publish ROI stories to support value conversations
Would you like me to build a specific EVE model for CODITECT or create a WTP survey template?
CODITECT saves money llm agnostic no vendor lockin for tokens hosting expand and explain works with CLAUDE, CODEX, GEMINI, KIMI-2.5, best of both closed and open-source LLM models and local AI Workstations
CODITECT is explicitly designed to be LLM‑agnostic, reduce token spend, and avoid vendor lock‑in by sitting above providers (Claude, Codex, Gemini, KIMI, local models) and centralizing memory, workflows, and orchestration.89909192
LLM-Agnostic Architecture (No Vendor Lock-In)
- CODITECT defines a Unified LLM Component Architecture (ADR‑122) that standardizes how agents, skills, workflows, and memory interact with different LLMs.89
- It supports multiple providers out of the box: Anthropic (Claude), OpenAI/Codex, Google Gemini, Moonshot/KIMI, and HuggingFace models.89
- Multi‑LLM session storage lives under provider-specific paths but uses a common format (JSONL), enabling cross-LLM export and processing without changing project structure.89
- This design matches modern LLM‑agnostic patterns where application logic is decoupled from the underlying model and can switch providers or run multiple models concurrently.909192
Supported Providers (Out of the Box)
- Closed-source & hosted: Claude (Anthropic), GPT‑4o / o‑series (OpenAI/Codex), Gemini (Google), KIMI (Moonshot).89
- Open-source & hosted/local: via HuggingFace Hub and any OpenAI-compatible or Anthropic-compatible API your infra exposes.939489
Token Economics & Cost Savings
- CODITECT’s CODITECT Experience Framework (CEF) uses a track/skill matrix to load only relevant skills per task, cutting context from ~30k tokens to ~960 tokens for typical tracks (≈90–97% reduction).89
- Memory-Context system with SHA‑256 deduplication prevents re‑sending identical content across sessions and LLMs.89
- Multi‑LLM session exports (
/sx) and context extraction (/cx) let you reuse prior work regardless of which LLM produced it, avoiding repeated “explain the system” prompts and reducing token waste.89 - This LLM‑agnostic, context‑efficient design matches best practices for cost control in multi‑agent / multi‑LLM systems.919290
“Best of Both Worlds”: Closed & Open-Source Models
- CODITECT abstracts skills, workflows, and agents away from specific models, allowing you to route tasks to:
- The Cross‑LLM Bridge translates semantic commands into provider‑agnostic operations so you can switch models without changing the way you ask for work.89
- Similar to other LLM‑agnostic dev tools, this lets you choose the right model per task (e.g., reasoning vs. code vs. retrieval) and evolve providers over time without rewriting your automation layer.929091
Local AI Workstations & Self-Hosting
- CODITECT’s architecture anticipates local AI workstations and self-hosted models:
- Message bus and tools layer can point to local inference endpoints (e.g., Ollama, LM Studio, text-generation-webui), as long as they expose compatible APIs.96979589
- The
.coditectdirectory and four‑tier SQLite storage live on the developer’s machine by default, making it natural to integrate with on-device models and offline workflows.89
- This aligns with the broader trend where local LLM tools (Ollama, LM Studio, GPT4All, Jan, etc.) expose OpenAI‑style APIs and are used as drop‑in providers under an LLM‑agnostic architecture.979596
Positioning Sentence You Can Reuse
You can drop this into a deck or website:
CODITECT is an LLM‑agnostic, vendor‑neutral intelligence layer that orchestrates agents, workflows, and memory across Claude, Codex, Gemini, KIMI‑2.5, open‑source, and local models—cutting token costs by up to ~90% through track‑based context loading and deduplicated memory, while eliminating vendor lock‑in for both tokens and hosting.90919289