walrus-verity 0.3.0

Proof-chain registry for AI agents — backed by Walrus for persistent, portable, verifiable memory

verity

A proof-chain registry for AI agents, backed by Walrus for persistent, portable, verifiable memory.

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What it does

verity gives AI agents structured, portable memory:

feature → claim → test → evidence → release
                                       │
                              verity push ──► Walrus blob ID (immutable)
                              verity pull ◄── restore anywhere, any agent

1. Proof-chain registry: track what an agent claimed, what it tested, and what it proved, all in a single verity.json file.
2. Agent memory layer: push the registry to Walrus (or MemWal) and pull it back in any future session, on any machine, by any agent.

Built for the Sui Overflow hackathon, Walrus track.

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Install

pip install walrus-verity

# With MemWal support
pip install "walrus-verity[memwal]"

AI coding assistant integration

Context skill: teach your AI tool the verity proof chain model, CLI, and API:

verity install-skill                    # Claude Code (global)
verity install-skill --tool cursor      # Cursor → .cursorrules
verity install-skill --tool windsurf    # Windsurf → .windsurfrules
verity install-skill --tool codex       # OpenAI Codex → AGENTS.md
verity install-skill --tool aider       # Aider → CONVENTIONS.md

MCP server: expose all verity tools natively to any MCP-compatible editor:

pip install "walrus-verity[mcp]"

Add to your claude_mcp_config.json (or equivalent):

{
  "mcpServers": {
    "verity": {
      "command": "verity-mcp",
      "env": {
        "WALRUS_PUBLISHER_URL": "https://publisher.walrus-testnet.walrus.space",
        "WALRUS_AGGREGATOR_URL": "https://aggregator.walrus-testnet.walrus.space"
      }
    }
  }
}

Available MCP tools: verity_init, verity_add_feature, verity_add_claim, verity_add_test, verity_add_evidence, verity_set_status, verity_validate, verity_release, verity_push, verity_pull, verity_log, verity_status.

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Quick start

The CLI validates on every write, so build the chain with neutral statuses first, then promote them once everything is linked.

verity init --repo-id repo:my-project

# Phase 1 — build the chain (neutral statuses)
verity add feature feat:auth "User authentication"
verity add claim   clm:auth.t1 "Login succeeds"  --feature feat:auth
verity add test    tst:auth.unit "Unit test"      --claim clm:auth.t1 --kind unit --path tests/test_auth.py
verity add evidence evd:auth.ci "CI run"          --test tst:auth.unit --artifact artifacts/ci.json --status passed

# Phase 2 — promote statuses (edit verity.json: set claim → verified, test → passing)
verity validate      # → OK
verity release 1.0.0
verity push          # → blob: AbCdEfGh…

# Any agent, any machine, any future session:
verity pull AbCdEfGh…

Why two phases? verity validates after every add command. Setting --status verified on a claim before its test exists will fail. Build the full chain first, then mark statuses.

Using the Python API instead? Statuses can be set at add time; validation is deferred until you call validate() or push(). See Python API.

$ verity validate
OK

$ verity release 1.0.0
Released rel:1.0.0 at 2026-05-23T19:08:25Z
  claims: clm:auth.t1

$ verity push
blob: AbCdEfGhIjKlMnOpQrStUvWxYz0123456789

$ verity log
  1.  [walrus]  2026-05-23T19:08:25Z  AbCdEfGhIjKlMnOpQrStUvWxYz0123456789

$ verity status
repo:my-project  schema 0.1.0
features   1   claims     1  (1 verified, 0 open)
tests      1  (1 passing)
evidence   1  (1 passed)
releases   1   latest: rel:1.0.0  blob: AbCdEfGh…
valid      ✓

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Documentation

Topic
CLI Reference	All commands: init, add, validate, release, push, pull, log, site, context, install-skill
MCP Server	verity-mcp: expose verity tools to any MCP-compatible editor
Python API	VeritySession, low-level functions, custom backends
Schema Reference	verity.json fields, ID prefixes, status values, validation rules
Walrus Setup	Testnet, mainnet, custom endpoints
MemWal Setup	Env vars, delegate keys, namespace isolation
Multi-Agent Patterns	Handoff pattern, audit trail, dry-run
Scripts	store_project_context.py: seed MemWal with verity project knowledge

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Acknowledgements

The proof-chain model (feature → claim → test → evidence → release) is directly inspired by the ssot-registry project, licensed under Apache 2.0. verity adapts that model for AI agents and Walrus-backed persistence.

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Contributing

See CONTRIBUTING.md. Run tests with uv run pytest.

License

MIT. See LICENSE.