langgraph-velesdb

Drop VelesDB's local-first agent memory — the why() knowledge-graph wedge — into any LangGraph agent in three lines.

Most agent memory is vector recall: it finds text that looks like the query. VelesDB connects memories with typed links, so why() answers with the best-matching memory plus the connected subgraph — context that shares no words with the question, which a plain vector recall is blind to. The store is on disk, so memory persists across agent runs.

Install

pip install langgraph-velesdb        # pulls langchain-core + velesdb

Use

from langgraph.prebuilt import create_react_agent
from langgraph_velesdb import make_memory_tools

# Offline by default (hash embedder); pass a configured MemoryService for Ollama.
agent = create_react_agent(llm, make_memory_tools("./agent_memory"))

make_memory_tools returns ten tools the agent can call:

For a pre-configured backend (e.g. Ollama embeddings), build the service yourself and pass it through:

from velesdb import MemoryService
tools = make_memory_tools(service=MemoryService("./agent_memory"))

Cross-run resumption

save_working_context / load_working_context let an agent pick up a task across separate runs instead of restarting from scratch:

make_memory_tools returns a list of tools, so index them by name first (the same pattern LangGraph's ToolNode/create_react_agent use internally):

tools = {t.name: t for t in make_memory_tools("./agent_memory")}

tools["save_working_context"].invoke({
    "project": "veles",
    "session": "issue-1546",
    "working": {
        "goal": "ship the langgraph tool set",
        "pending_actions": ["open the PR"],
    },
})

# ... next run, same project + session:
tools["load_working_context"].invoke({"project": "veles", "session": "issue-1546"})

Dated recall

Every remember-ed fact is auto-stamped with _veles_date (today, as a YYYYMMDD int) unless you set that metadata key yourself. Point recall_fused at it to get a chronological timeline instead of a ranked list:

tools = {t.name: t for t in make_memory_tools("./agent_memory")}

tools["recall_fused"].invoke({"query": "what changed this week", "date_field": "_veles_date"})
# -> {"memories": [...], "dated_context": "...", "now": "..."}

Compatibility

This package requires velesdb>=3.12.0, the highest version published to PyPI at the time of writing. feedback, save_working_context, load_working_context, and the automatic _veles_date metadata stamp landed in velesdb/velesdb-memory after the 3.12.0 release cut and are not yet in a published wheel. On a plain 3.12.0 install those three tools detect the missing binding method at call time and return an error payload instead of raising, e.g.:

{"error": "feedback requires velesdb > 3.12.0 — upgrade with `pip install -U velesdb`"}

so a single unsupported call surfaces to the agent as a normal tool result it can react to, instead of an uncaught AttributeError killing the whole graph run. recall_where/recall_fused still work, but their metadata stays empty for auto-dating until you upgrade. forget works on 3.12.0 too, but returns None instead of a True/False existed-or-not signal. The floor will be bumped again once a velesdb release past 3.12.0 ships.

list_working_contexts (browse saved sessions for a project) is not exposed here: it exists on the WASM and MCP surfaces but not yet on the velesdb Python binding (MemoryService), and this package only ever calls the binding — no memory logic is reimplemented in this MIT-licensed integration. It will be added once the Python binding grows the method.

License

MIT — see LICENSE. Wraps velesdb, which is under the VelesDB Core License 1.0.