Sema: When the Hash Is the Word

Content-addressed semantics for multi-agent coordination.

DOI

Sema is a content-addressed reference system for reasoning and communication. Participants encode and hash information under an agreed representation, then reuse the content address as a verifiable reference, optionally paired with a human-readable handle. Matching full references establishes identity of the resolved hashed content; semantic equivalence, correctness, and enforcement remain separate questions.

Web: semahash.org · Discord: Join

Install

MCP Server (recommended)

Add to any MCP client (Claude Code, Cursor, VS Code, Windsurf, Claude Desktop):

{
  "mcpServers": {
    "sema": {
      "command": "uvx",
      "args": ["--from", "semahash[mcp]", "sema", "mcp"]
    }
  }
}

Or via Claude Code CLI:

claude mcp add sema -- uvx --from "semahash[mcp]" sema mcp

This uses uv to download, install, and run sema in an isolated environment on first invocation, then caches it for subsequent calls.

Claude Code plugin (MCP server + skill)

Sema also ships as a Claude Code plugin — MCP server plus a skill that teaches the agent the search/resolve/mint/handshake workflow:

# One-time: add the Emergent Wisdom marketplace
claude plugin marketplace add emergent-wisdom/marketplace

# Install the plugin
claude plugin install sema

This gives you the MCP server and the sema-usage skill (auto-loaded), which teaches when to search vs mint, how to embed handles in text, and how to verify meaning at boundaries. The skill is a Claude Code convenience — the MCP server works with any client.

For local development:

claude --plugin-dir /path/to/sema

Permanent install (pip)

pip install "semahash[mcp]"

For CLI-only use (no MCP server):

pip install semahash

Quick Start

Use with AI Agents (MCP)

Already covered above via the JSON config or pip install path. For development against this repo:

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

Your agent now has access to sema_search, sema_lookup, sema_handshake, and 9 more tools. Any MCP-compatible client works — Sema exposes a standard stdio server.

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

Sema exposes a standard MCP stdio server — any MCP-compatible client works, including OpenClaw (openclaw mcp set sema '{"command":"uvx","args":["--from","semahash[mcp]","sema","mcp"]}').

Use via CLI

# Search the vocabulary
sema search "coordination"

# Look up a specific pattern
sema resolve StateLock

# Print a pattern's full definition
sema show StateLock

# Browse the graph structure
sema skeleton

# Start local API + web frontend (binds to 127.0.0.1 by default)
sema serve

Bring Your Own Vocabulary

Build a private registry from scratch — no PR or maintainer in the loop:

sema init ./mylib.db
sema use ./mylib.db
sema apply --add path/to/MyPattern.json
sema search "..."

Subsequent sema commands (including sema mcp) read from your private registry. (SEMA_DB_PATH, if set, overrides sema use.) See CONTRIBUTING.md for the canonical contribution path and docs/specification/versioning.md for the refinement and supersession policy.

Package a project database as a verified, standalone library release:

sema package ./mylib.db \
  --name mylib \
  --version 1.0.0 \
  --output-dir dist/mylib-1.0.0 \
  --github-repo acme/sema-mylib

Publish the generated library.json and versioned ZIP as assets on the corresponding published GitHub Release. Consumers install the Release asset URL for library.json—not the repository URL or a branch:

sema install https://github.com/acme/sema-mylib/releases/latest/download/library.json
sema use mylib
sema list
sema root

It installs one verified snapshot at a time rather than merging vocabularies; the bundled vocabulary remains the offline default. Use sema update mylib to follow the installed library's recorded release pointer. See Publishing and Installing Vocabulary Libraries for the complete DeFi authoring, dependency-closure, packaging, GitHub Release, and update workflow.

Use in Python

from sema.core.registry import RegistryManager

registry = RegistryManager()
pattern = registry.get_pattern("StateLock")

# Look up the canonical reference
print(pattern["sema_ref"])  # StateLock#c9c2

# Verify an inline reference before relying on it
assert pattern["sema_ref"] == "StateLock#c9c2"

Try the Protocol (No API Keys Needed)

python experiments/demos/local_handshake.py

See the handshake in action: matching hashes PROCEED, mismatched hashes HALT, and unknown patterns HALT. Cooperative mode accepts short prefixes for drift detection; strict mode requires the full hash. 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.

Cooperative: sema_handshake("StateLock#c9c2")
             -> PROCEED with assurance="prefix", or HALT

Strict:      sema_handshake("StateLock", "<full 64-char hash>", strict=true)
             -> PROCEED with assurance="full_hash", or HALT

This is the Anti-Postel principle: strict mode proceeds only on full-hash identity; cooperative mode uses compact prefixes as a non-adversarial drift signal. Mismatches fail closed in both modes.

The Vocabulary

The bundled vocabulary spans 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 a content-addressed behavioral definition. Concrete cards may add machine-verifiable contracts, invariants, failure modes, parameters, and typed dependencies where those fields are identity-defining.

MCP Tools

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

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 reproducible evaluations of Sema's claims and implementation boundaries.

Delta reconstruction

The public v0.3.0 to v0.4.0 reconstruction experiment checks whether the current graph and hash-cascade algorithm can rebuild a target vocabulary from an earlier release. It compares both aggregate roots, semantic pattern content, the logical dependency graph, unhashed metadata, and the complete normalized read model rather than treating root equality as complete release equality. It also demonstrates the safe reconstruction path: integrate the delta into a complete staged card snapshot, compile a fresh database, verify the result, and only then activate it.

.venv/bin/python experiments/delta_reconstruction/reproduce.py

The experiment and its expected results are documented in experiments/delta_reconstruction/README.md. Small synthetic versions of its addition, removal, rename, cascade, metadata, and failure cases run in the regular test suite.

Multi-agent design challenge

The controlled multi-agent design challenge compares three conditions:

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

Formal-verification pilot

Sema's handshake decision kernel and canonicalization type tags have a small Lean 4 proof suite. The handshake supports cooperative prefix matching for ordinary drift detection and strict full-hash verification for proof-grade identity; the proofs state each guarantee separately. The encoding proof establishes pre-hash domain separation, while Python conformance tests connect the models to production. See verification/README.md for the proven theorems, trusted-computing-base assumptions, and explicit limits of the claim.

Using with understanding-graph

Sema gives your agents shared semantic memory — a vocabulary of cognitive patterns with content-addressed identity. Understanding Graph gives them shared episodic memory — the actual thinking trail behind a decision. They compose:

claude mcp add sema -- uvx --from "semahash[mcp]" sema mcp
claude mcp add ug   -- npx -y understanding-graph mcp

With both installed, an agent can:

1. Anchor an understanding-graph decision node in a sema pattern hash (e.g. StateLock#c9c2) so the meaning of the primitive can never drift.
2. Use graph_semantic_search to find all past graph nodes that reference a given sema pattern — hash-stable history, not keyword matching.
3. Call sema_handshake before writing a decision that depends on a shared concept; if it returns HALT, the agent writes a tension node instead and stops, preventing silent divergence.

Full walkthrough: docs/guides/understanding-graph.md

Repository Structure

sema/
├── src/sema/              Core library (hashing, validation, MCP server, API)
├── data/                  Vocabulary pattern cards + taxonomy databases
├── docs/                  Documentation (philosophy, schema spec, CLI reference)
├── paper/                 Academic paper (sema.tex)
├── web/                   Web frontend (React + Three.js graph visualization)
├── experiments/
│   ├── orchestrator/      Multi-agent engine (bundled for experiment reproduction)
│   ├── delta_reconstruction/  Historical release reconstruction validation
│   ├── sema_design_challenge/  Main experiment (3 conditions, 5 runs, full traces)
│   └── demos/             Standalone demos (local handshake, Babel Test)
└── 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

@misc{westerberg2026sema,
  title        = {Sema: When the Hash Is the Word},
  author       = {Westerberg, Henrik},
  year         = {2026},
  month        = apr,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.19462702},
  url          = {https://doi.org/10.5281/zenodo.19462702}
}

See CITATION.cff for the machine-readable version (GitHub renders a "Cite this repository" button from it).

Safety

Sema ships no executable code — it's a library of pattern definitions (handles, mechanisms, invariants, dependency graphs). The MCP server hands patterns to clients as data; it does not execute the behaviors they describe.

Intended use: reasoning and reference. Patterns are thinking tools — named concepts agents can search, resolve, and handshake on to reason about coordination, risk, and procedure. See docs/manuals/vocabulary-design.md for the intent behind each pattern and the design choices.

Running patterns as executable recipes is untested. Many patterns describe procedures an agent could step through. That path is still a research phase — the mechanism text has not been validated end-to-end, and we make no claims about safety when a pattern is executed rather than referenced. If you go this route, run the agent's execution step in a sandboxed environment. Patterns with known risks carry a caution field in their metadata; absence of that flag means the pattern has not been classified as risky, not that it has been certified safe.

The long-term goal is cryptographically enforced safety constraints on agent-to-agent communication — an active research direction.

License

Sema is dual-licensed:

• Code (everything in src/, web/, experiments/, scripts/, and the package config) — MIT. Self-host it, fork it, build commercial products on top of it.
• Content (the pattern vocabulary in data/, the documentation in docs/, the academic paper in paper/, and the prose displayed on semahash.org) — CC BY 4.0. Reuse the patterns and prose anywhere, for any purpose including commercial, as long as you attribute Henrik Westerberg.

For academic citation, see CITATION.cff. GitHub renders this as a "Cite this repository" button on the project page that generates APA and BibTeX automatically.