AIWG
Multi-agent AI framework for Claude Code, Copilot, Cursor, Warp, and 4 more platforms
188 agents, 50 CLI commands, 128 skills, 6 frameworks, 21 addons. SDLC workflows, digital forensics, research management, marketing operations, media curation, and ops infrastructure — all deployable with one command.
npm i -g aiwg # install globally
aiwg use sdlc # deploy SDLC framework
Get Started · Features · Agents · CLI Reference · Documentation · Community
What AIWG Is
AIWG is a cognitive architecture that gives AI coding assistants structured memory, multi-agent ensemble validation, and closed-loop self-correction. It deploys specialized agents, workflow commands, enforcement rules, and artifact templates to any of 8 AI platforms with a single CLI command.
If you have used AI coding assistants and thought "this is amazing for small tasks but falls apart on anything complex," AIWG is the missing infrastructure layer that scales AI assistance to multi-week projects.
Unlike prompt libraries or ad-hoc workflows, AIWG implements research-backed patterns from cognitive science (Miller 1956, Sweller 1988), multi-agent systems (Jacobs et al. 1991, MetaGPT, AutoGen), and software engineering (Cooper's stage-gate, FAIR Principles, W3C PROV). The system addresses the hard problems in AI-augmented development: recovering from failures, maintaining context across sessions, preventing hallucinated citations, and ensuring reproducible workflows.
What Problems Does AIWG Solve?
Base AI assistants (Claude, GPT-4, Copilot without frameworks) have three fundamental limitations:
1. No Memory Across Sessions
Each conversation starts fresh. The assistant has no idea what happened yesterday, what requirements you documented, or what decisions you made last week. You re-explain context every morning.
Without AIWG: Projects stall as context rebuilding eats time. A three-month project requires continuity, not fresh starts every session.
With AIWG: The .aiwg/ directory maintains 50-100+ interconnected artifacts across days, weeks, and months. Later phases build on earlier ones automatically because memory persists. Agents read prior work via @-mentions instead of regenerating from scratch.
The segmented structure also makes large projects tractable. As code files grow, the project doesn't become harder to reason about — agents load only the slice of memory relevant to the current task (@requirements/UC-001.md, @architecture/sad.md, @testing/test-plan.md) rather than the entire codebase. Each subdirectory is a focused knowledge domain that fits comfortably in context, while cross-references keep everything connected.
The artifact index (aiwg index) takes this further. Without any tooling, agents often need to browse 3-6 documents before finding what they need. AIWG's structured artifacts reduce this to 2-3. With the index enabled, agents resolve artifact lookups in one query more often than not — a direct hit on the right requirement, architecture decision, or test case without browsing.
2. No Recovery Patterns
When AI generates broken code or flawed designs, you manually intervene, explain the problem, and hope the next attempt works. There is no systematic learning from failures, no structured retry, no checkpoint-and-resume.
Without AIWG: Research shows 47% of AI workflows produce inconsistent outputs without reproducibility constraints (R-LAM, Sureshkumar et al. 2026). Debugging is trial-and-error.
