Skip to main content

langgraph-agentram

An AgentRAM-backed BaseStore for LangGraph. It gives your LangGraph agents cross-thread long-term memory through a hosted key-value API, with no vector database and no embedding pipeline to run.

LangGraph's long-term memory is built on stores: JSON values organized by a namespace and a key. AgentRAM is a hosted memory API with exactly that shape, so this adapter is a thin, honest bridge between the two.

Install

pip install langgraph-agentram

Get a free AgentRAM API key at agentram.dev. New accounts start with 1,000 credits, no card required.

Use it

Pass the store to your agent and it gains memory that survives across threads:

from langchain.agents import create_agent
from langgraph_agentram import AgentRAMStore

store = AgentRAMStore(api_key="agentram_your_key_here")

agent = create_agent("claude-sonnet-4-6", tools=[], store=store)

Or use the store directly:

store.put(("memories", "user-1"), "language", {"value": "French"})
item = store.get(("memories", "user-1"), "language")
print(item.value)  # {"value": "French"}

# list a namespace, or text-search within it
store.search(("memories", "user-1"))
store.search(("memories", "user-1"), query="French")

How it maps to AgentRAM

  • A namespace tuple becomes an AgentRAM agent_id, joined with / (for example ("memories", "user-1") becomes memories/user-1).
  • The key is the AgentRAM key.
  • The value dict is JSON-encoded into AgentRAM's value field.

Honest limits

This adapter does what AgentRAM does, and nothing it does not.

  • search is a text match, not semantic ranking. score is always None. That is the point: memory without a vector database.
  • search treats the namespace you pass as a full namespace, not a prefix to walk into nested sub-namespaces.
  • list_namespaces is not supported. AgentRAM has no endpoint to enumerate namespaces, so the adapter raises rather than return a wrong answer. Track namespaces in your own app if you need them.
  • A namespace maps to an agent_id capped at 100 characters, and a value is capped at 5,000 characters. Both raise a clear error if exceeded, rather than truncating.
  • Per-item TTL is not mapped in this version.

Config

  • api_key (required): your key, starts with agentram_.
  • base_url (optional): defaults to https://api.agentram.dev.

Links

MIT

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

langgraph_agentram-0.1.0.tar.gz (6.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

langgraph_agentram-0.1.0-py3-none-any.whl (6.9 kB view details)

Uploaded Python 3

File details

Details for the file langgraph_agentram-0.1.0.tar.gz.

File metadata

  • Download URL: langgraph_agentram-0.1.0.tar.gz
  • Upload date:
  • Size: 6.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for langgraph_agentram-0.1.0.tar.gz
Algorithm Hash digest
SHA256 0c669693d69ea28d6107b54428a51880835050ac8ed572b09aca4b8e82878011
MD5 efd85b0944237a1179a4e79cd55ad21e
BLAKE2b-256 4b1adbc8a2c656b4b330379019f7a28673a4a06cbab7c1c16bb458873d47812e

See more details on using hashes here.

File details

Details for the file langgraph_agentram-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for langgraph_agentram-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 618556696196486323254e026b384d532d8f38b3b9472f7fb2e5f0a961329ca9
MD5 9154f3471527377bd48ecd15f281c788
BLAKE2b-256 6471f0b7cbb20ca6ea009726da96b1b34166568ad3613a758d4934188495edf7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page