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.

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 — torch's current PyPI Linux/x86_64 wheel for Python 3.11 alone measures 554.6 MB — plus a further, separate model download (~88 MB for all-MiniLM-L6-v2, cached under ~/.cache/huggingface/hub) on first use. 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:

| Provider | Extra | Backend | |----------|-------|---------| | SentenceTransformerProvider | embeddings-local | Local model via sentence-transformers | | OpenAICompatibleProvider | embeddings-openai | Any OpenAI-compatible API | | OllamaProvider | embeddings-ollama | Local Ollama server | | HuggingFaceProvider | embeddings-hf | HuggingFace Inference API | | CallbackProvider | (built-in) | Custom callable |

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](https://github.com/sovanti