semahash 0.1.1

Content-addressed semantics for multi-agent coordination. When the hash is the word.

Sema: When the Hash Is the Word

Content-addressed semantics for multi-agent coordination.

Sema is a semantic commons that content-addresses meaning itself: the definition is the identifier. By deriving identifiers from the cryptographic hash of a pattern's definition, any divergence in meaning produces a distinct hash, guaranteeing that misaligned agents halt rather than fail silently.

Web: semahash.org

Install

pip install semahash

For MCP server support (recommended for AI agents):

pip install "semahash[mcp]"

Quick Start

Use with AI Agents (MCP)

Add Sema to Claude Code in one command:

claude mcp add sema -- sema mcp

Add Sema to OpenClaw via mcporter:

npm install -g mcporter
mcporter config add sema --command sema --arg mcp --scope home

Or clone and install locally:

git clone https://github.com/emergent-wisdom/sema.git
pip install -e "./sema[mcp]"
claude mcp add sema -- sema mcp

Your agent now has access to sema_search, sema_lookup, sema_handshake, and 5 more tools. Any MCP-compatible framework works — Sema exposes a standard stdio server.

Verify it works — ask your agent: "Search sema for coordination patterns and handshake on StateLock"

Full integration guides: Claude Code | OpenClaw

Use via CLI

# Search the vocabulary
sema search "coordination"

# Look up a specific pattern
sema resolve StateLock

# Browse the graph structure
sema skeleton

# Start local API + web frontend
sema serve

Use in Python

from sema.core.actions import sema_handshake
import json

# Look up the canonical hash
result = json.loads(sema_handshake("StateLock"))
print(result["canonical_ref"])  # StateLock#a375

# Verify alignment
result = json.loads(sema_handshake("StateLock#a375"))
print(result["verdict"])  # PROCEED

Try the Protocol (No API Keys Needed)

python experiments/demos/local_handshake.py

See the handshake in action: matching hashes PROCEED, mismatched hashes HALT, unknown patterns HALT. Takes 2 seconds.

How It Works

word = hash(canonical(definition))

Take any concept (a coordination protocol, a reasoning pattern, a trust mechanism), express it in canonical form, hash it. That hash IS the word. Change one byte in the definition, get a different word.

Agent A: "Let's use StateLock#a375"
Agent B: sema_handshake("StateLock#a375")
         -> PROCEED (hashes match) or HALT (drift detected)

This is the Anti-Postel principle: same bytes = PROCEED, different bytes = HALT. No ambiguity, no silent failures.

The Vocabulary

450 patterns across 13 categories and 4 layers:

• Physics — Immutable substrate (locks, entropy, causality)
• Mind — Hybrid cognition (reasoning, inference, strategy)
• Society — Multi-agent coordination (economics, governance, protocols)
• Infrastructure — Operational constraints (data structures, verification)

Each pattern is an executable specification containing machine-verifiable contracts, invariants, failure modes, and typed dependencies.

MCP Tools

When running as an MCP server (sema mcp), these tools are available:

Tool	Description
sema_search	Search patterns by name, description, or meaning
sema_lookup	Get a pattern by its reference (e.g., StateLock#a375)
sema_resolve	Get a pattern with dependencies expanded
sema_handshake	Fail-closed semantic verification between agents
sema_mint	Create a new pattern (validate, hash, add to vocabulary)
sema_propose_context	Generate a Merkle root for a multi-agent context set
sema_verify_context	Verify a context proposal from another agent
sema_tree	Browse vocabulary by layer and category
sema_validate	Validate a pattern JSON for correctness
sema_stats	Vocabulary statistics
sema_graph_skeleton	Ultra-minimal graph overview (~150 tokens)

Web Frontend

pip install "semahash[api]"
sema serve
# Open http://localhost:3000

Interactive 3D graph visualization, pattern browser, and search. Built with React + Three.js.

Experiments

The experiments/ directory contains a controlled multi-agent design challenge comparing three conditions:

Condition	Sema	Turns	Outcome
A: Natural language only	No	4	Design rejected
B: Sema vocabulary	Yes	11	SAD Engine approved
C: Sema + protocol	Yes	25	SAD Engine with exhaustive vetting

Agents with Sema patterns produced physics-grounded designs that survived adversarial scrutiny. Agents without Sema produced shallow designs that failed safety review.

To reproduce:

cd experiments/sema_design_challenge
export GOOGLE_API_KEY=your_key
./reproduce.sh

See experiments/sema_design_challenge/README.md for details.

Key Properties

• Zero semantic collisions across the full vocabulary
• 16.9x average token compression via content-addressed stubs
• Fail-closed architecture — mismatches halt, never fail silently
• Mean embedding similarity of 0.21 — high structural distinctness

Repository Structure

sema/
├── src/sema/              Core library (hashing, validation, MCP server, API)
├── data/                  Vocabulary (450 pattern cards + taxonomy database)
├── docs/                  Documentation (philosophy, schema spec, CLI reference)
├── paper/                 Academic paper (sema.tex)
├── web/                   Web frontend (React + Three.js graph visualization)
├── experiments/
│   ├── emergent-swarm/    Multi-agent engine (bundled for experiment reproduction)
│   ├── sema_design_challenge/  Main experiment (3 conditions, 5 runs, full traces)
│   └── demos/             Standalone demos (local handshake, Babel Test)
├── integrations/          Integration guides (Claude Code, OpenClaw, any MCP client)
└── pyproject.toml         Package config (extras: [mcp], [api], [full])

Contributing

Want to add patterns, improve existing ones, or host the frontend locally? See CONTRIBUTING.md.

Citing

@unpublished{westerberg2026sema,
  title={Sema: Content-Addressing Meaning for Safe Multi-Agent Coordination},
  author={Westerberg, Henrik},
  year={2026},
  note={Preprint}
}

License

MIT