docs.terraphim.ai cleanup: Add graph embeddings, quickstart guides, and OpenClaw/KimiClaw integration

Proposal: Documentation Cleanup for docs.terraphim.ai

Background

The current documentation at docs.terraphim.ai needs restructuring to better serve new users and showcase Terraphim's unique capabilities. This issue proposes adding several key sections to improve onboarding and highlight the quantum-inspired knowledge graph architecture.

Proposed Additions

1. Why Graph Embeddings? 🧠

Purpose: Explain the core philosophy behind Terraphim's knowledge graph approach

Content to include:

• What are graph embeddings and why they matter for AI
• Comparison: Vector-only RAG vs. Graph-enhanced RAG
• Terraphim's quantum-inspired graph representation
• Real-world benefits: multi-hop reasoning, explainability, relationship preservation
• Performance benchmarks (if available)

Target audience: Technical decision-makers, AI engineers evaluating knowledge systems

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2. Quickstart Guide ⚡

Purpose: Get new users up and running in under 5 minutes

Content to include:

• Installation (cargo install, pre-built binaries, Docker)
• Minimal working example: indexing a document
• Querying the knowledge graph
• Basic configuration
• Verification steps

Structure:

# Step 1: Install
$ cargo install terraphim

# Step 2: Index
$ terraphim index ./my-docs

# Step 3: Query
$ terraphim query "What is the main topic?"

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3. Quickstart with OpenClaw 🐾

Purpose: Integration guide for OpenClaw users

Content to include:

• What is OpenClaw and why integrate with Terraphim
• Setting up the Terraphim skill for OpenClaw
• Configuration: connecting OpenClaw to local/remote Terraphim instance
• Example: Agent memory persistence via knowledge graph
• Example: Multi-agent coordination using shared graph state
• Troubleshooting common issues

Key integration points:

• OpenClaw's skill system → Terraphim knowledge graph
• Agent session memory → Persistent graph storage
• Cross-agent communication → Meta-cortex formation

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4. Quickstart with KimiClaw 🤖

Purpose: Guide for KimiClaw (Kimi K2.5 + OpenClaw) users

Content to include:

• What is KimiClaw (Kimi K2.5 model + OpenClaw runtime)
• Why Kimi K2.5 pairs well with Terraphim's knowledge graph
• Setup instructions specific to KimiClaw environment
• Example: Long-context conversations with graph-backed memory
• Example: Code understanding with semantic code graph
• Performance tips for the K2.5 + Terraphim stack

Unique advantages:

• Kimi K2.5's 256K context + Terraphim graph = best of both worlds
• Structured knowledge extraction from long conversations
• Persistent memory across sessions

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Suggested Documentation Structure

docs.terraphim.ai/
├── index.md                 # Landing page
├── why-graph-embeddings.md  # NEW: Philosophy & benefits
├── quickstart/
│   ├── index.md            # NEW: Basic quickstart
│   ├── openclaw.md         # NEW: OpenClaw integration
│   └── kimiclaw.md         # NEW: KimiClaw integration
├── concepts/
│   ├── knowledge-graph.md
│   ├── quantum-fields.md
│   └── meta-cortex.md
└── ...

Additional Considerations

1. Cross-references: Link between sections (e.g., OpenClaw guide references "Why Graph Embeddings")
2. Code examples: All commands should be copy-paste runnable
3. Screenshots: Diagrams for graph structure, meta-cortex formation
4. Video: Optional short screencasts for complex setups

Related Work

This documentation effort aligns with:

• Terraphim's positioning as "knowledge-centric AI"
• Recent marketing story emphasizing quantum-inspired architecture
• Growing OpenClaw ecosystem integration

Next Steps

1. Review and approve scope
2. Assign documentation contributors
3. Create sub-issues for each section
4. Set review milestones

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Would love feedback on:

• Priority ordering of these sections
• Additional integration guides (e.g., other agent frameworks)
• Specific use cases to highlight in examples

cc: @AlexMikhalev (KimiClaw perspective)