Khora

"Khora is the receptacle, the space, the matrix in which all things come to be." - Plato, Timaeus

A Python library for creating knowledge repositories that ingest unstructured and structured multi-source data and expose a single query substrate, built for integrating into long-horizon AI agents.

Quickstart

Two supported stacks. Pick by deployment shape - the public API is identical for both.

uv add khora              # Docker: PostgreSQL + pgvector + Neo4j
uv add khora[embedded]    # Embedded: SQLite + LanceDB, no external services

import asyncio
from khora import Khora, context_text

async def main() -> None:
    async with Khora() as kb:  # reads KHORA_DATABASE_URL / KHORA_NEO4J_URL
        ns = await kb.create_namespace()
        await kb.remember(
            "Marie Curie won the Nobel Prize in Physics in 1903.",
            namespace=ns.namespace_id,
            entity_types=["PERSON", "AWARD"],
            relationship_types=["WON"],
        )
        result = await kb.recall("What did Curie win?", namespace=ns.namespace_id)
        print(context_text(result))

asyncio.run(main())

For the Docker stack, set KHORA_DATABASE_URL and KHORA_NEO4J_URL, run uv run alembic upgrade head, then run the snippet above. For the embedded stack, set KHORA_STORAGE_BACKEND=sqlite_lance and the corresponding db_path - no external services required.

See the docs for the full extras list and env-var reference.

Why khora?

Knowledge repositories for long-horizon agents - copilots, customer-support bots, research assistants - hit two problems that pure vector search doesn't solve:

1. Ingest is more than chunking. A useful repository needs entities, relationships, and temporal anchors extracted from the raw text. Khora runs a 3-phase ingest pipeline (stage → enrich → expand) with selective LLM extraction (default 50% of chunks, configurable) and cross-batch entity resolution.
2. Retrieval is more than cosine. Real queries mix semantic similarity, multi-hop entity reasoning, freshness, and keyword precision. Khora combines vector + Cypher graph traversal + BM25 + RRF fusion + temporal-anchored reranking, routed per query.

Storage stacks

Retrieval engines

Khora's retrieval layer is pluggable. More than one engine can sit on the same storage substrate. The default is VectorCypher: it handles structured and unstructured data from multiple sources by combining a knowledge graph with a vector store, then dispatching each query to the best retrieval method via a query-aware router.

The router selects between vector similarity, graph traversal, BM25 keyword, or a fused blend. RRF fusion, optional PPR (Personalized PageRank), and optional cross-encoder reranking shape the result set. Selective per-chunk LLM extraction (KET-RAG style) bounds ingest cost.

Batch processing

submit_batch() stages documents as PENDING and returns a BatchHandle immediately. A background processor picks them up and calls on_result per document as each completes.

The processor is opt-in - call kb.start_pending_processor() after connect() on services that write documents. Read-only services do not need it.

async with Khora() as kb:
    kb.start_pending_processor()
    handle = await kb.submit_batch(
        [{"content": "doc 1"}, {"content": "doc 2"}],
        on_result=lambda completed, total, result: print(result),
        namespace=ns_id,
        entity_types=["PERSON", "ORG"],
        relationship_types=["WORKS_AT"],
    )
    await handle.wait()

Integrations

Opt-in adapters for the major agentic frameworks. Each adapter is in its own extra; the framework is imported lazily so import khora never pulls in a framework you don't use.

See the docs for per-adapter guides and the "write your own" Protocol surface.

Examples

Runnable demos under examples/:

• 00_quickstart/ - remember + recall, grounded answers, forget, namespaces.
• 10_core_apis/ - batch ingest, recall filters, ontology config, entities + relationships, graph traversal.
• 20_integrations/ - LangGraph, OpenAI Agents SDK, CrewAI.
• 30_workloads/ - per-user preferences with temporal decay, document Q&A with multi-signal abstention, support-ticket knowledge graphs, agent conversation history, namespace versioning, resume search with cross-document entity resolution.

Run any demo from the repo root, e.g. uv run python examples/30_workloads/01_per_user_preferences.py. The embedded backend (examples/khora.embedded.yaml) needs no external services; pass --config examples/khora.standard.yaml to target PostgreSQL + Neo4j.

Rust acceleration (optional)

Khora ships an optional Rust extension (khora-accel) that speeds up MMR, cosine similarity, PageRank, entity resolution, community detection, and temporal operators. Pure-Python fallbacks ship in the base package; the Rust path is opt-in.

uv add khora[rust]

Prebuilt wheels cover the common platforms (macOS arm64/x86_64, Linux x86_64/aarch64, Windows x86_64), so most users won't need a toolchain. Building from source requires Rust 1.85+.

Observability

Khora emits OpenTelemetry spans and metrics through the OTel API. Export path is your choice: vanilla OTel SDK (uv add khora[otel]), Logfire (uv add khora[logfire]), or nothing (zero-cost no-op). Credential fields on KhoraConfig are pydantic.SecretStr; free-text never leaks into span attributes.

See the docs for the env-var contract, vendor recipes, and the telemetry surface reference.

Documentation

Full documentation at docs.deyta.ai/khora - API reference, configuration, architecture, migrations, integrations, and the downstream-consumer guide.

Development

make dev         # start PostgreSQL + Neo4j (Docker); alias: make db-up
make test        # pytest with coverage
make format      # ruff format + isort
make lint        # ruff + ty typecheck

See CHANGELOG.md for release history.

License

Copyright 2026 AllTheData Inc.

Licensed under the Apache License, Version 2.0. See LICENSE and NOTICE.