deepagents-graph-memory
Give your Deep Agents a shared record of what they tried, what happened, and how the findings connect. Built on LadybugDB. Experimental.
Created by Pranav Bedi and Taha Khan.
Why use this?
An agent can find an old answer and still be missing why that answer made sense. Neo4j's From recall to reasoning inspired this project: connect what happened, why an action was chosen, and what followed, so the agent can retrieve the reasoning behind earlier work.
This library brings that pattern to Deep Agents. For example, an agent can record: a parser test failed → it suspected a missing empty field → it changed the parser → the test passed, with links to the file and test output. Later, an agent sharing that graph can look up what was tried and why, with the evidence to check it.
Use it for long tasks or shared agent work where those connections matter. Keep full files and logs in your filesystem. The agent chooses what to record and does the reasoning; the graph stores and retrieves the links. It doesn't automatically learn rules or verify that a recorded explanation is true.
Installation
Use Python 3.11–3.14 on macOS 15+ (Apple Silicon or Intel), Windows x64, or Linux x64/ARM64 with glibc 2.28+.
pip install --upgrade deepagents-graph-memory
The platform packages include LadybugDB, OpenSSL, and the search extension. The graph needs no database server, separate OpenSSL install, or search setup command. On Intel Macs, a separate Deep Agents dependency currently requires a source build. See platform and development details.
Quick Start
This gives your agent tools to save and look up findings while keeping its normal file tools. Set your model provider's API key first.
from deepagents import create_deep_agent
from deepagents_graph_memory import (
GraphMemoryBackend,
graph_context_middleware,
graph_memory_tools,
)
MODEL = "google_genai:gemini-3.5-flash"
# Temporary graph; add path="project.lbdb" to save it to disk.
graph_backend = GraphMemoryBackend.create()
graph_tools = graph_memory_tools(graph_backend)
agent = create_deep_agent(
model=MODEL,
tools=graph_tools,
middleware=[graph_context_middleware()],
)
The agent can spawn workers as needed. Default workers inherit the graph tools, and the middleware passes the evidence and disagreement instructions with each task. Seven workers get seven automatic IDs, and all their findings stay in the same graph for the parent to read. You don't need to define or name each worker.
Call graph_backend.close() after all workers finish. See the
full example
for custom subagents and browsing the graph through file tools.
Storage and limits
By default, the graph disappears when you close it or stop the program. Use
GraphMemoryBackend.create(path="project.lbdb") to keep it on disk. On a server,
keep that file on storage that survives restarts and let only one process open it
for writing. Save your evidence files separately. See storage and deployment.
Search returns a limited amount of related context, so it may leave things out. Agents still need to check evidence and resolve disagreements. Namespaces separate project data but don't enforce access permissions. Keep personal preferences and ordinary notes in your existing memory backend.
Documentation
- Usage guide: shared agents, evidence, conflicts, retries, tools, and deployment.
- Inspecting the graph: debug through Python without a model or provider key.
- Design and rationale: the context-graph model and implementation boundaries.
- Evaluations: offline workflow scenarios and an optional model comparison; offline passes don't establish better model decisions.
For source development, follow the development setup,
then run python -m pytest and python -m ruff check ..
Report issues.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for deepagents-graph-memory 0.1.3
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