# 001 — AbstractFramework Documentation Overhaul

## Task
Comprehensive review and rewrite of all AbstractFramework documentation to ensure it is up-to-date, professionally engaging, and fully communicates the capabilities and possibilities of the framework.

## Summary
AbstractFramework's docs are structurally solid (README, docs/README, architecture, getting-started, api, faq, guides, scenarios) but fail to communicate the **full breadth of what's possible** with the framework. Many powerful capabilities are buried in individual repos but never surfaced at the ecosystem level. The tone is competent but could be more inspiring and inviting.

## Why
AbstractFramework is the **single entry point** to the ecosystem. Users landing here must:
1. Immediately understand what they can build (vision, not just components)
2. Feel confident this is a professional, well-maintained project
3. Discover capabilities they didn't know existed (MCP, glyph compression, voice cloning, evidence capture, scheduled workflows, etc.)
4. Navigate efficiently to the right starting point for their use case

## Scope

### What We Do
- Rewrite `README.md` — more engaging, highlights all capabilities, "What can you build?" section
- Rewrite `docs/README.md` — welcoming hub that highlights what's possible
- Update `docs/architecture.md` — add MCP, evidence/provenance, scheduled workflows
- Update `docs/getting-started.md` — richer intro, add MCP path
- Update `docs/faq.md` — add missing topics (MCP, structured output, streaming, embeddings, evidence)
- Update `docs/api.md` — minor enrichment

### What We Don't
- Do not rewrite individual repo documentation (each repo owns its docs)
- Do not change code or package versions
- Do not create new guides/scenarios (existing ones are adequate)

## Dependencies
- All repos present in workspace for reference

## Expected Outcomes
- A user landing on the README immediately understands the vision and breadth
- All framework capabilities are discoverable from the docs hub
- Professional, positive, engaging tone throughout
- Cross-references are accurate and complete
- No feature is hidden — everything possible is at least mentioned

---

## Completion Report

### What Was Done

**README.md** — Complete rewrite:
- Added "What Can You Build?" section with 12 concrete use cases (coding assistant, visual workflows, voice, image gen, scheduling, KG, observability, bridges, MCP, glyph compression, server, custom UIs)
- Added "Key Capabilities in Depth" section with code examples for: tool calling + MCP, structured output, streaming + async, voice I/O, image generation, glyph compression, embeddings, OpenAI-compatible server, scheduled workflows, event bridges, CLI apps, evidence & provenance
- Enhanced "Why AbstractFramework?" with 9 bullet points (added: Visual, Multimodal, Interoperable, Production-Ready)
- Enhanced "Create More Solutions" with concrete use cases (code reviewers, researchers, analysts, moderators, support agents, DevOps monitors)
- Updated ecosystem tables with richer descriptions (e.g., AbstractCore now mentions MCP/embeddings/server; AbstractVoice mentions cloning/multilingual)
- Updated architecture diagram to include MCP and scheduled workflows
- Enhanced Philosophy section with richer explanations

**docs/README.md** — Complete rewrite:
- Added welcoming intro with role-based "Start Here" navigation table
- Added comprehensive "What's Possible" section covering: Build & Deploy AI Agents, Use Any LLM Anywhere, Go Multimodal, Ensure Reliability, Observe & Debug Everything, Connect to the Outside World, Build Your Own UI
- Enhanced "Find What You Need" tables with richer descriptions and new entries (OpenAI-compatible server, CLI apps)
- Updated architecture diagram to include scheduling and event bridges

**docs/architecture.md** — Significant update:
- Added MCP Integration section with architecture diagram
- Added Evidence & Provenance Architecture section (tamper-evident ledger, artifacts, snapshots, interaction tracing)
- Added Scheduled Workflows section
- Added Split API/Runner (Production) architecture with diagram
- Added Media Input Architecture section with policy-driven pipeline
- Enhanced Gateway architecture with scheduling and event bridges
- Enhanced Foundation diagram with snapshots, provenance, MCP, server
- Added Production (Split API + Runner) deployment pattern
- Enhanced capability plugin discovery description

**docs/getting-started.md** — Updated:
- Added richer intro paragraph with guidance for new users
- Added Path 13: MCP Integration (with code example)
- Added Path 14: Structured Output (with Pydantic example)
- Added Path 15: OpenAI-Compatible Server (with usage example)

**docs/faq.md** — Significant update:
- Added new "Advanced Capabilities" section with 13 new Q&As:
  - MCP (Model Context Protocol)
  - Structured output
  - Streaming
  - Async
  - Glyph visual-text compression
  - Embeddings
  - OpenAI-compatible server
  - Built-in CLI apps
  - Snapshots and history bundles
  - Interaction tracing
  - Voice cloning
  - GGUF local image generation
- Added new Gateway & Deployment Q&As:
  - Scheduled workflows
  - Event bridges (Telegram, email)
  - Split API/Runner
  - SQLite storage backend

**docs/api.md** — Enhanced:
- Added richer `create_llm` description mentioning all providers and features
- Added `GenerateResponse` field descriptions
- Added "Where to Find Specific APIs" navigation table (14 entries linking to repo-specific docs)

**docs/glossary.md** — Updated:
- Added "Durability & Provenance" section: Snapshot, History bundle, Provenance, Evidence
- Added "Integrations" section: MCP, Event bridge, Structured output, Glyph compression
- Added: Interaction trace

**llms.txt** — Updated:
- Reflects new doc structure and richer descriptions
- Added all guides and reference entries
- Added all ecosystem repos (was missing 7 repos)

### Features Now Surfaced (Previously Hidden)
1. MCP (Model Context Protocol) integration
2. Glyph visual-text compression
3. Structured output (Pydantic)
4. Streaming + async support
5. Built-in CLI apps (summarizer, extractor, judge, intent, deepsearch)
6. Evidence capture and tamper-evident provenance
7. Snapshots and history bundles
8. Voice cloning
9. GGUF model support for image generation
10. OpenAI-compatible server mode
11. Embeddings and semantic search
12. Token budget management
13. Interaction tracing / observability events
14. Scheduled workflows (cron-style)
15. Split API/runner production architecture
16. SQLite storage backend
17. Event bridges (Telegram, email)
18. Media input pipeline (policy-driven)
19. BasicSession for conversation state
20. Plan + Review modes in AbstractCode
21. Mindmap/KG query UI in AbstractObserver
