deepagents-graph-memory
Give your agent a record of what it tried and what worked.
For developers building LangChain Deep Agents that handle long-running tasks and need to remember a lot of context. The graph connects attempts, evidence, and results so agents can find earlier work before deciding what to do next.
Works alongside the virtual filesystem (VFS): files hold code, logs, and tool dumps; the graph links findings to their evidence. For example: failing test → suspected cause → code change → test result.
Inspired by Niels de Jong's Neo4j article, From recall to reasoning: How context graphs upgrade an agent’s brain. Built by Pranav Bedi and Taha Khan, using LadybugDB. Experimental.
Install
pip install --upgrade deepagents-graph-memory
Requires Python 3.11–3.14 on Linux or Apple Silicon Mac; see supported platforms. The graph database, OpenSSL, and search extension come bundled. No database server or separate search setup.
Quick start
Set your model provider's API key, then add the graph tools and guidance to your agent:
from deepagents import create_deep_agent
from deepagents_graph_memory import (
GraphMemoryBackend,
graph_context_middleware,
graph_memory_tools,
)
graph = GraphMemoryBackend.create()
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
tools=graph_memory_tools(graph),
middleware=[graph_context_middleware()],
)
result = agent.invoke({"messages": [{"role": "user", "content": "Investigate the parser failure and record what you find."}]})
print(result["messages"][-1].content)
graph.close()
Storage options
GraphMemoryBackend.create() keeps the graph temporarily while the backend is
open. Use GraphMemoryBackend.create(path="project.lbdb") to keep it on a
persistent disk across runs.
The full guide covers deployment, shared agents, graph-only mode, and debugging. Agents choose what to record; the graph doesn't automatically verify their claims.
1 maintainer
Metadata
1 maintainer
Release files for deepagents-graph-memory 0.1.8
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