pydantic-ai-pixeltable

Give a Pydantic AI agent read access to your Pixeltable tables, and optionally keep its Harness Memory in the same catalog.

• Pixeltable: read-only tools list_tables, describe_table, query_table, and similarity_search over tables and views you already have.
• PixeltableMemoryStore: a Harness MemoryStore, used as Memory(store) in place of FileStore.

Each works without the other. Requires Python 3.11+, pydantic-ai-slim 2.38+, pydantic-ai-harness 0.29.0+, and Pixeltable 0.7.8+.

Quick start

pip install pydantic-ai-pixeltable "pydantic-ai-slim[openai]"

import pixeltable as pxt
from pixeltable.functions.openai import embeddings
from pydantic_ai import Agent
from pydantic_ai_harness import Memory
from pydantic_ai_pixeltable import Pixeltable, PixeltableMemoryStore

handbook = pxt.create_table("handbook", {"topic": pxt.String, "text": pxt.String})
handbook.insert([{"topic": "expenses", "text": "Meals during business travel are reimbursed up to 60 EUR per day."}])
handbook.add_embedding_index("text", embedding=embeddings.using(model="text-embedding-3-small"))

agent = Agent(
    "openai:gpt-5.6-sol",
    capabilities=[
        Pixeltable(["handbook"]),  # list_tables, describe_table, query_table, similarity_search
        Memory(PixeltableMemoryStore(table_name="memory")),  # notes kept across runs
    ],
)
print(agent.run_sync("Can I expense a 75 EUR dinner? Remember that I travel monthly.").output)
# The answer cites the 60 EUR daily limit; the note lands in the memory table.

quickstart.py is the full runnable version: a two-run HR assistant whose second run answers from what the first one remembered. In an app, declare the table on a TableModel and create it with pxt schema update.

Catalog tools

• tables is a required allowlist of table paths or directory prefixes. ["*"] allows the whole catalog, including a memory table. A view inside an allowed directory exposes its base table's columns. Version handles (tbl:3) are refused, since old versions keep deleted rows and dropped columns.
• similarity_search needs an embedding index on the column. query_table filters by equality only; timestamp, date, and UUID values are ISO strings.
• Default columns skip media, array, binary, and unstored computed columns, which recompute on every read (possibly a model call) and also reject filters. A named media column returns a file URL.
• max_rows (20) and max_chars (8000) bound every result. An oversized string is cut to end in ... and any other oversized value becomes null; the {"table", "rows", "truncated"} envelope is always returned.
• Two instances on one agent share id="pixeltable" and merge by intersecting their allowlists; a disjoint merge raises. A capability passed to a single run replaces the agent's, so it can widen access.
• pydantic-ai-chdb registers the same list_tables and describe_table names; wrap one in PrefixTools to use both.
• From a spec: Agent.from_spec({"model": ..., "capabilities": [{"Pixeltable": ["handbook"]}]}, custom_capability_types=[Pixeltable]).

Memory store

• The table is created on first use. Each memory path is a kind == "file" row, and operation receipts are __op__/ rows, so the roots __op__ and __meta__ are reserved. Paths are at most 255 characters.
• Writes are compare-and-set on a UUID version. The intent is journaled before the write, so a crash rolls forward on replay instead of applying twice.
• store.table is an ordinary table: query it, join it, or add an embedding index on content. search_memory stays lexical, as in every Harness store. A direct t.update is not a Memory write and leaves the version unchanged.
• Pixeltable keeps every row version and receipts are never pruned, so the table grows with history.
• Python only: Harness YAML backends are memory, file, and sqlite. The package emits no telemetry; memory.* spans come from Harness Memory.

On Pixeltable 0.7.8 with embedded Postgres, 20k rows, laptop (pytest tests/test_stress.py -m expensive): list_paths and search take ~115 and ~125 ms with 18k files under the prefix, a contended CAS write ~5 ms per attempt, query_table ~10 ms, similarity_search ~8 ms. list_paths and search sort in Python because database ordering depends on collation, so they scale with the files under the prefix, not with the table.

Development

pip install -e ".[dev]"
pytest tests/            # add -m expensive for the 20k-row volume tests
ruff check . && ruff format --check . && mypy pydantic_ai_pixeltable

License

Apache 2.0