langgraph-store-pixeltable

LangGraph BaseStore backend for Pixeltable — persistent, versioned, multimodal agent memory.

Installation

pip install langgraph-store-pixeltable

Quick Start

from langgraph.store.pixeltable import PixeltableStore

store = PixeltableStore(table_name="agent_memory.items")
store.setup()

# Store a memory
store.put(("users", "alice"), "prefs", {"color": "blue", "language": "Python"})

# Retrieve it
item = store.get(("users", "alice"), "prefs")
print(item.value)  # {"color": "blue", "language": "Python"}

# List namespaces
namespaces = store.list_namespaces(prefix=("users",))

Filtered Search

The filter parameter maps to Pixeltable's .where() clause — predicates are evaluated server-side, not in Python:

store.put(("team", "eng"), "alice", {"role": "senior", "level": 5, "lang": "Python"})
store.put(("team", "eng"), "bob", {"role": "junior", "level": 2, "lang": "Go"})
store.put(("team", "eng"), "carol", {"role": "senior", "level": 4, "lang": "Python"})

# Exact match (shorthand)
results = store.search(("team",), filter={"role": "senior"})

# Comparison operators
results = store.search(("team",), filter={"level": {"$gt": 3}})
results = store.search(("team",), filter={"role": {"$ne": "junior"}})

# Multiple conditions (AND)
results = store.search(("team", "eng"), filter={"role": "senior", "lang": "Python"})

Supported operators: $eq, $ne, $gt, $gte, $lt, $lte.

Semantic Search

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

store = PixeltableStore(
    table_name="agent_memory.semantic",
    index={
        "dims": 384,
        "embed": lambda texts: model.encode(texts).tolist(),
        "fields": ["text"],
    },
)
store.setup()

store.put(("docs",), "d1", {"text": "Pixeltable handles multimodal data", "domain": "infra"})
store.put(("docs",), "d2", {"text": "LangGraph builds stateful agents", "domain": "framework"})
store.put(("docs",), "d3", {"text": "React is a frontend library", "domain": "frontend"})

# Semantic search
results = store.search(("docs",), query="multimodal AI pipelines", limit=2)
for r in results:
    print(f"{r.key}: {r.value['text']} (score={r.score:.3f})")

# Semantic search + filter (narrowed by domain)
results = store.search(
    ("docs",), query="data infrastructure", limit=3, filter={"domain": "infra"},
)

With LangGraph Agents

from langgraph.prebuilt import create_react_agent
from langgraph.store.pixeltable import PixeltableStore

store = PixeltableStore(table_name="agent_memory.items")
store.setup()

agent = create_react_agent(model, tools=tools, store=store)

Access the Underlying Pixeltable Table

The .table property gives direct access to the Pixeltable table for operations beyond the Store interface — computed columns, lineage, version history, and arbitrary predicates:

import pixeltable as pxt

t = store.table

# Inspect all data
t.select(t.key, t.value, t.created_at).collect()

# Add a computed column — auto-backfills all existing rows
t.add_computed_column(word_count=my_word_counter(t.value['text'].astype(pxt.String)))

# Add a classification UDF — runs on every new insert
t.add_computed_column(auto_domain=classify_domain(t.value['text'].astype(pxt.String)))

# WHERE on computed columns
results = t.where(t.auto_domain == 'infra').select(t.key, t.value).collect()

# Compound WHERE on multiple computed columns
results = (
    t.where((t.word_count > 5) & (t.auto_domain == 'infra'))
    .select(t.key, t.value, t.word_count, t.auto_domain)
    .collect()
)

# New inserts via the store auto-compute all lineage columns
store.put(("docs",), "d4", {"text": "Kubernetes orchestrates containers"})
# word_count and auto_domain are already computed for d4

Why Pixeltable for LangGraph Memory?

• Metadata filtering via .where(): Filter on value fields with $eq/$ne/$gt/$gte/$lt/$lte, evaluated server-side
• Computed column lineage: Add derived columns that auto-backfill and auto-compute on new inserts
• Persistent and versioned: Data survives restarts; every update is tracked with full version history
• Incremental: Only new/changed rows get re-embedded
• Multimodal native: Images, video, audio, and documents alongside text via .table
• Any embedding model: Works with sentence-transformers, OpenAI, or any callable
• No external services: Embedded PostgreSQL, no Docker required

Links

• Pixeltable Docs
• LangGraph Integration Docs
• GitHub
• Discord