sema

<!-- mcp-name: io.github.emergent-wisdom/semahash -->
<!-- deploy-trigger: 2026-04-11 -->

<p align="center">
  <img src="https://raw.githubusercontent.com/emergent-wisdom/sema/main/docs/images/sema_banner.png" alt="Sema — When the hash is the word" width="800">
</p>

# Sema: When the Hash Is the Word

**Content-addressed semantics for multi-agent coordination.**

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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](https://semahash.org) · **Discord:** [Join](https://discord.gg/hRhVqAuDYQ)

## Install

### MCP Server (recommended)

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

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

Or via Claude Code CLI:

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

This uses [uv](https://docs.astral.sh/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:

```bash
# 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:

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

### Permanent install (pip)

```bash
pip install "semahash[mcp]"
```

For CLI-only use (no MCP server):

```bash
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:

```bash
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](https://openclaw.ai/) (`openclaw mcp set sema '{"command":"uvx","args":["--from","semahash[mcp]","sema","mcp"]}'`).

### Use via CLI

```bash
# 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:

```bash
sema init ./mylib.db
export SEMA_DB_PATH=$(pwd)/mylib.db
sema apply --add path/to/MyPattern.json
sema search "..."
```

Subsequent `sema` commands (including `sema mcp`) read from your private
registry. See [CONTRIBUTING.md](CONTRIBUTING.md) for the canonical
contribution path and [docs/specification/versioning.md](docs/specification/versioning.md) for the
refinement and supersession policy.

### Use in Python

```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)

```bash
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:

| Tool | Description |
|------|-------------|
| `sema_search` | Search patterns by name, description, or meaning |
| `sema_lookup` | Get a pattern by its reference (e.g., `StateLock#c9c2`) |
| `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` | Compute a context digest for a multi-agent definition set (drift detection) |
| `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) |
| `sema_reset_session` | Clear session cache so searches return full results again |

## Web Frontend

```bash
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:

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

See [`experiments/sema_design_challenge/README.md`](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`](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](https://github.com/emergent-wisdom/understanding-graph) gives them shared *episodic* memory — the actual thinking trail behind a decision. They compose:

```bash
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