attestor

Attestor

Cut your agent's token burn 21×. Two API calls.

Full-context replay re-reads the whole conversation every turn — input tokens that grow O(n²) and a bill that compounds with every session. Attestor retrieves only what's needed: flat ~200 tokens per call, 21× fewer input tokens by turn 100, 100% recall — measured across six models, open and closed.

await attestor.add(namespace, content)          # when new information arrives
facts = await attestor.recall(namespace, query) # ~200 flat tokens, always

Self-hosted, deterministic retrieval, zero LLM in the critical path. The memory layer for agent teams that need shared, tenant-isolated memory with bi-temporal replay and an auditable supersession chain.

pip install attestor

Using Claude Code? pipx install attestor then attestor quickstart — one command, zero questions: it brings up the local backends (Postgres + Pinecone Local + Neo4j), uses a local Ollama embedder (no cloud key), and wires the MCP server + hooks. Reverse it with attestor teardown. Or drive it from inside Claude Code via the plugin (/plugin install attestor → /attestor:install-attestor). See Install for Claude Code.

pipx install attestor && attestor quickstart

Version	4.1.6 (stable; greenfield rebuild — no v3 migration path)
PyPI	attestor
Import	attestor
Live site	https://attestor.dev/
Repo	https://github.com/bolnet/attestor
License	MIT

Designed and built by Surendra Singh — 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.

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What it is

Attestor is a memory store for agent teams that need a shared, tenant-isolated memory with bi-temporal replay, deterministic retrieval, and an auditable supersession chain. It runs as a Python library, a Starlette REST service, or an MCP server — same API in all three.

The token math: Full-context replay is O(n²) — every turn re-reads the whole history. Attestor replaces that with O(n) targeted retrieval. Per-call context stays flat at ~200 tokens whether the agent is on turn 1 or turn 100. One Claude Opus 4 session at 100 turns: $24.15 → $1.24. Verify it yourself with context-clock.

Turn	Full-context replay	Attestor	Reduction
t24	growing	~200 tok	5.6×
t50	growing	~200 tok	11×
t100	8,709 tok/call	~200 tok	21.5×

It is built around three claims, each grounded in code:

1. Bi-temporal — replay any past state. Every memory has both event time (valid_from / valid_until) and transaction time (t_created / t_expired). Nothing is deleted; everything is queryable forever (attestor/temporal/manager.py:43-73, core.py:888-890).
2. Semantic-first retrieval, no LLM in the hot path. A six-step deterministic pipeline. Same query → same ranking. Unit-testable (attestor/retrieval/orchestrator.py:1-14).
3. Conversation ingest with auditable conflict resolution. Two-pass speaker-locked extraction, then a four-decision (ADD / UPDATE / INVALIDATE / NOOP) resolver per fact. Every supersession carries an evidence_episode_id (attestor/extraction/conflict_resolver.py:98).

Designed for

• Multi-agent products where many LLMs write to the same memory store
• Regulated chat systems that need point-in-time reconstruction (compliance, audit, FOIA-style queries)
• Self-hosted deployments — your VPC, your Postgres, your Neo4j

Not designed for