Memwright

Attestor

Governed memory for multi-agent production meshes.

Attestor is no longer "memory for your coding assistant." Three things happened: the editor vendors shipped memory for free, the benchmark race became a race about which LLM answers the question, and every serious conversation about a memory layer was about something else entirely: when many agents share one memory, who is allowed to write, who is allowed to forget, and can you prove it afterwards. So that is what Attestor is now.

When many agents share one memory, recall is the easy part. The hard part is who may write, who may forget, what an answer was built from, and whether you can prove all of it later. Attestor is a self-hosted memory service that makes those properties enforceable in code rather than in policy documents: role-based access at the AgentContext layer, provenance on every memory (optionally Ed25519-signed), per-agent token budgets and write quotas, a deterministic ranking path with no LLM in it, temporal supersession you can query as of any past moment, hard tenant isolation across all three storage roles, an auditable, audit-first forget-user path with declarative retention, and governance jobs that run as durable Temporal workflows: retried by policy, resumed after a crash, visible in an operator UI, and never on the read path. It runs as a Python library, a REST sidecar, or an MCP server with the same API. Role enforcement is an AgentContext guarantee and runs in process. The REST and MCP surfaces call the store directly and rely on their own auth.

ctx = AgentContext(agent_id="reviewer-1", role=AgentRole.REVIEWER, namespace="acme-prod")

ctx.add_memory("...") # PermissionError: REVIEWER lacks 'write' mem.recall("...", context=ctx, as_of=last_friday) # what the mesh believed then — nothing is deleted

pip install attestor

Designed and built by [Surendra Singh](https://www.linkedin.com/in/singhsurendra/) — building auditable infrastructure for multi-agent AI, with fifteen years of production-systems discipline brought to the memory layer. Companion projects: claude-finance (Claude-powered financial analytics) · private-equity (PE × AI workshop). Reach out if you're hiring senior IC for AI infrastructure.

What it is

Attestor is the memory layer for an agent mesh that has to answer to someone — a security review, a regulator, a customer, or the engineer paged at 3 a.m. Every memory is stored once in Postgres (the source of truth) and derived into Pinecone (vectors) and Neo4j (graph). Every read and write goes through an AgentContext that carries identity, role, namespace, and budget, and the store keeps enough history that any past state can be reconstructed.

What it enforces

Each guarantee below is a property of the code, not a roadmap item. File references point at the enforcing implementation.

How to evaluate it

The nearest neighbours are the temporal-knowledge-graph memory layers (Zep, Graphiti). For a production mesh the useful comparison is not benchmark accuracy — it is whether the memory layer *itself* enforces roles, budgets, tenancy, provenance, and auditable deletion, or leaves each of those to the calling application. The table above is Attestor's answer; hold any alternative to the same checklist, with file references.

Designed for

• Multi-agent products where many LLM-driven agents with different trust levels write to the same memory
• Regulated systems that need point-in-time reconstruction and a defensible deletion story
• Self-hosted deployments — your VPC, your Postgres, your Pinecone, your Neo4j

*Not* designed for

• A personal memory for a single coding assistant — the Claude Code plugin below is an on-ramp demo of the same engine, not the product
• A general-purpose vector database or a RAG framework with chunking / reranking / orchestration
• An LLM agent runtime — Attestor is the memory backend; the agent loop is yours

Try it locally in one command. pipx install attestor && attestor quickstart brings up Postgres + Pinecone Local + Neo4j in Docker and wires the Claude Code MCP server + hooks so you can watch the governance layer work on a real project. Details in Quick start and Install for Claude Code below.

Quick start

1. Install

pip install attestor                 # or: pipx install attestor

Or pull the container (introspection-grade image, single layer over python:3.12-slim, currently linux/amd64):

docker pull ghcr.io/bolnet/attestor:latest      # recommended — anonymous pull, mirrored to all registries below

Same image is mirrored to:

(An internal Azure ACR mirror exists at memwright.azurecr.io/attestor but is private — Azure customers should use az acr import from one of the public registries above.)

The image's default entrypoint is attestor mcp (MCP server over stdio). For full production use, point the container at an external Postgres + Neo4j via env vars (or compose them with attestor/infra/local/docker-compose.yml); override the entrypoint to run attestor doctor, attestor api, etc.

2. Stand up the local stack — one command, zero questions

attestor quickstart

attestor quickstart does the whole local install non-interactively and prints every step: it writes ~/.attestor/{config.toml,attestor.yaml,.env}, brings up the three-role local stack in Docker, uses a local Ollama `bge-m3` embedder (no cloud key), wires the Claude Code MCP server (./.mcp.json) + lifecycle hooks, and runs attestor doctor.

Prerequisites: Docker running, and Ollama serving bge-m3 (ollama pull bge-m3). quickstart runs a preflight that scans the ports/tools and tells you if anything is missing — it never prompts.

| Container | Ro