Engrava

Engrava

The memory database for AI agents.

A queryable memory graph with bi-temporal valid-time predicates — in an in-process Python library over one SQLite file, with no generative-model call in the core write path.

Image: CI Image: PyPI Image: Python Image: License: MIT

Engrava is a standalone, in-process database for AI agent memory. Built on SQLite, it provides thought CRUD, edge-based knowledge graphs, embedding-based similarity search, full-text search (FTS5/BM25), and a declarative extension system — all in a single package with zero external service dependencies.

Benchmark results are published, and the runs are reproducible from a separate repository. Every published run is Group A — memory_pipeline_llms: [], no language model anywhere in the memory layer's built-in path. That is a property of the architecture's default signals and hooks rather than a measurement, unless a custom Dreaming signal or Memory Hygiene hook is configured to call one.

• Benchmark results — the live table: every published row with the comparability segment it belongs to.

• engrava-benchmark — the runner. Clone it and reproduce a result against the package from PyPI. MIT.

• Discussions — questions; reproduction questions belong in the Q&A category.

Use Cases

• AI agent persistent memory

• Personal knowledge base

• Conversation storage with semantic search

• Research notes with associative linking

• Any application that needs a thought-graph with embeddings

Quick Start

Installation

pip install engrava

Optional extras:

pip install 'engrava[vec]'                # sqlite-vec vector search backend
pip install 'engrava[embeddings-local]'   # sentence-transformers embeddings (local model)
pip install 'engrava[embeddings-openai]'  # OpenAI-compatible embeddings API
pip install 'engrava[embeddings-ollama]'  # Ollama local embeddings server
pip install 'engrava[embeddings-hf]'      # HuggingFace Inference API embeddings

Dreaming/consolidation and the knowledge graph need no extra — they are part of the base install.

embeddings-local carries a large first-run download. It pulls sentence-transformers and torch, plus a further, separate model download (~88 MB for all-MiniLM-L6-v2, cached under ~/.cache/huggingface/hub) on first use. On Linux, the PyPI build of torch also pulls the NVIDIA CUDA libraries as dependencies: several gigabytes, most of it usable only with an NVIDIA GPU. Without one, install the CPU-only build first (see Configuration: No NVIDIA GPU for the exact command and numbers). engrava-mcp[local] is the exact same download, reached through the server package instead of this one. If you do not need semantic search in-process, pip install engrava alone (no extra, no download, no model) already gives you keyword search, the graph, and MindQL — see Configuration → Quick-start profiles for that and the Ollama-backed alternative that keeps the model out of this process entirely.

Basic Usage

Store a memory and search for it in two calls — no IDs to generate, no record to assemble:

import asyncio

import aiosqlite

from engrava import SqliteEngravaCore


async def main() -> None:
    # SqliteEngravaCore wraps an open aiosqlite connection.
    async with aiosqlite.connect(":memory:") as conn:
        conn.row_factory = aiosqlite.Row
        store = SqliteEngravaCore(conn)
        await store.ensure_schema()

        await store.remember("Python is great for AI agents")
        await store.remember("SQLite needs no server")

        result = await store.recall("what language is good for agents?")
        for thought_id, score in result.results:
            thought = await store.get_thought(thought_id)
            if thought is not None:
                print(f"{thought.essence}  (score: {score:.3f})")


asyncio.run(main())

remember() stores the text as a thought (generating its ID for you) and returns the stored ThoughtRecord; recall() runs the same hybrid search as search_hybrid() and returns the ranked results. This store has no SearchConfig of its own, and it still ranks with the same default weights — FTS 0.30, vector 0.55, recency 0.10, priority 0.05, graph 0.00 (opt-in) — as a store built with one; see Hybrid Search. For full control — setting priority, thought type, metadata, or the cognitive cycle on a write — build a ThoughtRecord yourself and call create_thought().

From here, link thoughts with typed edges, query them with MindQL, or run the full ingest → dream → search tour in the Quick Start guide.

Configuration-Driven Setup

from engrava import SqliteEngravaCore

# from_config opens and OWNS the connection — use it as an async context manager.
async with await SqliteEngravaCore.from_config("engrava.yaml") as store:
    # The schema is already applied by from_config.
    thought = await store.get_thought("some-id")

See docs/configuration.md for the full YAML schema.

Upgrading

Automatic schema migration runs on first connection. See the upgrade guide for compatibility notes, backup guidance, and troubleshooting steps.

Features

Thought CRUD

Create, read, update, and archive thoughts with full lifecycle management. All models are frozen Pydantic objects — mutations happen via evolve().

Edge-Based Knowledge Graph

Link thoughts with typed, weighted edges. Edge types include ASSOCIATED, DEPENDS_ON, DERIVED_FROM, CONSOLIDATED_FROM (created by dreaming), and CONTESTED_BY.

Embedding Search

Store embeddings alongside thoughts and search them with the built-in NumPy cosine backend or the optional sqlite-vec backend (pip install 'engrava[vec]'). Pluggable embedding providers:

Full-Text Search (FTS5)

SQLite FTS5 virtual table with BM25 ranking. Hybrid search combines vector similarity, text relevance, recency, priority, and graph connectivity. Signals that cannot run for a query are skipped and the remaining weights are redistributed.

MindQL Query Language

Declarative query language for the thought-graph:

FIND thoughts WHERE thought_type = 'OBSERVATION' AND priority = 'P1' LIMIT 10
COUNT thoughts WHERE lifecycle_status = 'ACTIVE'
SELECT thought_id, essence FROM thought WHERE thought_type = 'BELIEF'

Extensible with custom commands through MindQLExecutor or an ExtensionManifest; the lifecycle hook registry is reserved and is not consulted by the core executor.

Extension System

Plug into the thought lifecycle via EngravaHooksProtocol. Subclass DefaultEngravaHooks when you only need selected active methods:

from engrava import DefaultEngravaHooks, ThoughtRecord

class MyHooks(DefaultEngravaHooks):
    async def on_store(self, thought: ThoughtRecord) -> ThoughtRecord:
        # Observe or enrich the object returned after persistence.
        return thought

    async def decay_function(
        self, thought: ThoughtRecord, elapsed_cycles: int
    ) -> float:
        # Supply a decay multiplier to an enabled Memory Hygiene pass.
        return 1.0

Core currently invokes on_store, on_retrieve, and decay_function. on_store runs after the source thought's row is inserted; changing its return value does not rewrite the persisted row. score_function and mindql_extension_registry() remain reserved protocol methods and are not called by core.

Dreaming / Memory Consolidation

Built-in DreamingExtension for periodic memory consolidation — scores thoughts via its default signals (no LLM calls), promotes high-value entries, and creates REFLECTION thoughts by clustering semantically related thoughts and computing centroid embeddings through a deterministic structural function, not an LLM. A custom signal you register with DreamingExtension runs whatever code it contains. Available since 0.3.0.

→ See docs/benchmarks.md for reproducible evidence (synthetic benchmark suite runnable in ~5 minutes).

Forgetting / Memory Hygiene

The subtractive half of memory maintenance, paired with Dreaming: an opt-in loop whose built-in scoring makes no LLM calls, that archives cold, low-signal thoughts — the default action, reversible via restore_thought — and, as a separately opted-in step, garbage-collects them (not reversible) once both restore windows have elapsed under their non-zero defaults — a cycle count and a wall-clock duration, either of which can be configured to 0 to disable that window. OFF by default; once enabled, archived thoughts drop out of default retrieval and can be restored (restore_thought / include_archived).

→ See docs/memory-hygiene.md for the loop, protection, restore windows, and the honest deletion posture.

Tamper-Evident Thought/Edge Journal

Opt-in hash-chain journal that records thought and edge mutations (plus action status/verification_status transitions) as SHA-256-linked, before/after entries — a tamper-evident thought/edge journal, not a whole-database audit (embeddings and action creation are not covered). Off by default, one config flag to enable. Query history with store.journal.get_entries(...) and validate the chain with store.verify_journal(), which audits whatever chain is on disk independent of the current journal.enabled state — the store.journal writer handle does not exist while journaling is off, but the chain earlier sessions wrote is still in journal_entry and still needs verifying.

→ See docs/audit-trail.md for enabling, querying, verification, and the security model (what "tamper-evident" does and does not guarantee).

Multi-Service Isolation

Run multiple independent databases under one EngravaManager:

from pathlib import Path

from engrava import EngravaManager

async with EngravaManager(data_dir=Path("./data")) as mgr:
    agent_a = await mgr.get_store("agent-a")
    agent_b = await mgr.get_store("agent-b")
    # Completely isolated databases

MCP Server

Want Engrava as a memory server for your agent? The MCP server ships as its own package, engrava-mcp — a native stdio server (no HTTP shim) with read tools, optional write tools, attachable engrava:// resources, and guided prompts, for any MCP client (Claude Desktop, Claude Code, Cursor, Windsurf, VS Code):

uvx engrava-mcp        # or: pip install engrava-mcp

engrava-mcp pulls engrava in transitively, so installing it also gives you the import engrava library. See the engrava-mcp package for install, client configuration, the full tool/resource/prompt reference, and read-only mode.

uvx vs. a persistent install. uvx engrava-mcp (equivalently uv tool run engrava-mcp) installs into "an ephemeral virtual environment in the uv cache directory" per uv's own --help text — fine for the lexical/network profiles above, which add nothing heavier than httpx. Once you are on the local profile (engrava-mcp[local], the same sentence-transformers + torch download as engrava[embeddings-local]), uv tool install 'engrava-mcp[local]' is the better fit: it installs once into a persistent environment — the same uvx engrava-mcp invocation then reuses that installed environment instead of resolving a fresh ephemeral one — and a later uv tool upgrade engrava-mcp re-pays only the changed packages, not the whole dependency tree.

CLI

engrava --db mydata.db info          # Database stats
engrava --db mydata.db query "FIND thoughts WHERE thought_type = 'OBSERVATION' LIMIT 5"
engrava --db mydata.db snapshot -o backup.jsonl
engrava --db mydata.db restore -i backup.jsonl
engrava --db mydata.db gc            # Collect ARCHIVED thoughts + their edges/embeddings/actions
engrava --db mydata.db migrate       # Ensure schema is up-to-date
engrava --db mydata.db export -o portable.json

gc physically deletes ARCHIVED thoughts together with every edge touching one on either end — including edges whose other end is still live — their embeddings and the actions sourced from them, then reconciles the vector index by removing every vec0 row no embedding row owns; on a vec0-indexed store where sqlite-vec cannot be loaded — most commonly because engrava[vec] is not installed — a pass that is about to delete stops before deleting anything and exits 1 rather than stranding those vectors in an index nothing can then reach them through.

engrava info's --format json output carries every field of the metrics snapshot exposed by await store.metrics(), with the snapshot's own schema version renamed metrics_schema_version, plus two fields the snapshot itself does not carry, db_path and database_schema_version — see Upgrade Guide. The default text output is a shorter summary of that same data, not the full snapshot.

See the CLI reference for every command and option.

Architecture

• SQLite with WAL mode for concurrent reads

• Frozen Pydantic models — immutable domain objects

• Async-first — all I/O via aiosqlite

• Hook-based extension — zero monkey-patching

• Template method pattern — subclass SqliteEngravaCore for extended schemas

• Zero external services — everything runs locally in-process

Documentation

• Documentation Index — choose a path by task: learn, build, operate, extend, or migrate

• Core Concepts — the mental model (thought, edge, reflection, cycle, …) — start here

• The Bi-temporal Model — the optional valid-time axis: query a fact as of any instant, invalidate without deleting

• Positioning — when Engrava is (and isn't) the right tool, and how it compares

• Quick Start — 5-minute setup guide

• Tutorial — build a small notes memory end to end

• Recipes — copy-paste snippets for common tasks (store a turn, retrieve context, TTL, dedup, …)

• Building a memory-backed agent — the end-to-end agent turn loop (ingest → retrieve → generate → consolidate)

• Migrating from another memory system — concept mapping, porting calls, bulk import, and scoping/multi-tenancy

• Embeddings — wiring a real embedding provider (local / OpenAI / Ollama / HuggingFace / custom)

• MCP server (engrava-mcp) — expose a store to MCP clients (Claude Desktop, Claude Code, Cursor, Windsurf, VS Code) via the standalone server package: install, run, client config, tools/resources/prompts

• Configuration — YAML config format and options

• Upgrade Guide — compatibility matrix, backups, and troubleshooting

• Extensions — Writing custom extensions and hooks

• Observability — Metrics snapshot API

• Audit Trail — Tamper-evident hash-chain journal (enabling, querying, verifying, security model)

• Evidence and Conflicts — model claims, provenance, contested facts, and caller-owned clarification

• Security — trust boundaries, data egress, tenant isolation, encryption posture, and journal limits

• Error Handling and Recovery — decide when to retry, repair input, or replace a store

• Benchmarks — release gates, versioned observed values, and LongMemEval

• API Reference — Full protocol and class reference

• CLI Reference — every engrava command and option

• Glossary — quick definitions of every Engrava term

• MindQL — Query language syntax and examples

• Troubleshooting — symptom → cause → fix for common errors

• FAQ — quick answers (LLM/keys, embeddings-optional, scale, concurrency, backups, …)

• Performance & Scaling — the vector-backend switch, bulk-ingest, and dreaming cost at scale

• Data Lifecycle & Retention — lifecycle states, TTL, archive-vs-delete, GDPR erasure, disk reclamation

• Deployment — process model, database files on disk, containers, graceful shutdown

• Concurrency — the WAL single-writer model, busy timeout, and per-service isolation

• Backup & Recovery — WAL-safe backups, snapshot vs file copy, restore verification

• Known Limitations — Platform notes and constraints

Development

make install   # deps + dev extras + local git hooks -- from the primary checkout
make gate      # fast: hooks-active + lint + format check + type check + goldens drift, ~70s
make check     # lint + format check + type check + the full test suite with coverage (unchanged; no hooks-active, no goldens drift)

make install also wires a commit-msg hook that lints the message of the commit a squash merge creates — including on a local branch that never opens a pull request, where nothing else does. It refuses to install from a linked worktree (core.hooksPath is shared across all of them). See CONTRIBUTING.md for what it covers and what it does not.

License

MIT — see LICENSE for details.