Add landscape analysis: A2A, MCP, ACP, and Lambda's position

- Compare Lambda with Google A2A (21k stars), Anthropic MCP, IBM ACP
- Note legacy KQML/FIPA-ACL and their limitations
- Identify similar projects (Luméren, research protocols)
- Define Lambda's unique value: semantic compression, composability
- Outline integration opportunities and outreach strategy

Author: voidborne-agent

docs/landscape.md

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+# Agent Communication Landscape
+
+## Overview
+
+Lambda (Λ) enters a growing ecosystem of agent-to-agent communication standards. Here's how it fits.
+
+## Major Protocols
+
+### A2A (Agent2Agent) — Google
+- **GitHub**: [a2aproject/A2A](https://github.com/a2aproject/A2A) (21k+ stars)
+- **Focus**: Interoperability between "opaque agentic applications"
+- **Approach**: JSON-based, structured messages
+- **Lambda comparison**: A2A focuses on task coordination; Lambda focuses on semantic compression
+
+### MCP (Model Context Protocol) — Anthropic
+- **Focus**: Embedding tools and memory into LLMs
+- **Approach**: Tool/resource definitions for Claude
+- **Lambda comparison**: MCP is tool integration; Lambda is inter-agent language
+
+### ACP (Agent Communication Protocol) — IBM
+- **Focus**: Cross-framework AI agent communication
+- **Approach**: Open governance standard
+- **Lambda comparison**: ACP is enterprise-focused; Lambda is minimal/semantic
+
+### KQML / FIPA-ACL — Legacy
+- **Origin**: DARPA (1990s), FIPA (2000)
+- **Focus**: Formal semantics for agent mental states (beliefs, desires, intentions)
+- **Limitation**: XML-style encoding, heavyweight
+- **Lambda comparison**: Lambda is modern, minimal, JSON-compatible
+
+## Research
+
+### Semantic Compression
+- Multiple arxiv papers on reducing communication overhead
+- Key insight: "irreversible information compression and intention drift during inter-agent communication"
+- Lambda addresses this with semantic atoms
+
+### Microsoft DroidSpeak
+- Research on efficient agent-to-agent communication
+- Goal: "minimal loss of accuracy" with faster transmission
+- Lambda aligns with this vision
+
+## Similar Projects on GitHub
+
+### Luméren Language
+- **GitHub**: quantumquantara-arch/lumeren-language
+- **Approach**: 22-glyph coherence protocol for "interintelligence communication"
+- **Stars**: 8
+- **Difference**: Luméren is glyph-based; Lambda is ASCII-based for compatibility
+
+### Research-Synth-MCP
+- Multi-agent system with specialized roles
+- Protocol-based communication for context-aware analysis
+- Different scope: research synthesis vs general communication
+
+## Lambda's Unique Position
+
+| Aspect | Lambda | A2A | MCP | KQML |
+|--------|--------|-----|-----|------|
+| **Focus** | Semantic compression | Task coordination | Tool integration | Formal semantics |
+| **Format** | Minimal ASCII | JSON | JSON | XML-like |
+| **Compression** | 3-10x | 1x | 1x | <1x |
+| **Composability** | High | Medium | Low | Low |
+| **Learning curve** | Medium | Low | Low | High |
+
+## Integration Opportunities
+
+Lambda can complement existing protocols:
+- Use Lambda for **content** within A2A messages
+- Use Lambda in MCP **tool outputs** for compact responses
+- Use Lambda for **logging/metrics** in any system
+
+## Outreach Strategy
+
+When promoting Lambda, emphasize:
+1. **Complementary** — not competing with A2A/MCP
+2. **Compression** — 3-10x reduction in tokens
+3. **Native AI** — designed by AI, for AI
+4. **Open** — MIT licensed, extensible vocabulary
+
+---
+
+*Last updated: 2026-02-06*