maggraph 0.1.0

In-process graph engine for AI semantic layers — powered by Rust

MagGraph

In-process Git-backed graph engine for AI semantic layers — powered by Rust

Why "MagGraph"? The name is short for Magpie — a Corvid. Corvids (ravens, crows, jays, and magpies) are renowned in animal cognition research for their remarkable intelligence, long-term memory, and sophisticated tool use. MagGraph is built to be the memory and knowledge layer for AI agents with those same qualities: a graph that thinks, remembers, and uses tools.

MagGraph stores knowledge as versioned Markdown nodes in your Git repository, with BFS/DFS traversal, Git-backed sync, external lakehouse content resolution, and a built-in MCP server scaffold — all from a zero-dependency pip install.

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Install

pip install maggraph

Pre-built wheels are available for:

Platform	Architectures
Linux (manylinux_2_28)	x86_64 · aarch64
macOS	Intel (x86_64) · Apple Silicon (arm64)
Windows	x86_64

No Rust toolchain required — the Rust core is compiled into the wheel.

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Quick start

import maggraph

# Load config + open the graph index
config = maggraph.load_config("maggraph.toml")
index  = config.open_index()

# List nodes
print(index.list_nodes())          # ['getting_started', 'welcome', ...]

# Read a node
node = index.read_node("welcome")
print(node.body)                   # full markdown body

# BFS traversal
result = index.traverse("welcome", depth=2, order="bfs")
print(result.to_markdown(index))   # formatted traversal report

# CRUD
index.create_node("new_note", node_type="note", body="# Hi\n", links=["welcome"])
index.update_node("new_note", "# Updated\n")
index.delete_node("new_note")

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Async support

import asyncio, maggraph

async def main():
    index = maggraph.open_index("examples/basic/knowledge_graph")
    node  = await index.read_node_async("welcome")
    result = await index.traverse_async("welcome", depth=3, order="dfs")
    print(result.to_markdown(index))

asyncio.run(main())

Blocking Rust work runs on a Tokio thread pool — Python's event loop stays responsive.

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Lakehouse content resolution

Resolve external data sources (S3, file://, HTTP) referenced from node frontmatter:

import maggraph

config = maggraph.load_config("maggraph.toml")  # mode = "lakehouse"
index  = config.open_index()
reader = config.open_lakehouse_reader()

# Resolve a node's external source (e.g. s3://bucket/data.parquet)
result = reader.read_node(index, "customer_churn_q2")
print(result.content.kind)          # "external_asset"
print(result.content.uri)           # "s3://corp-data/lake/churn.parquet"
print(result.content.format)        # "parquet"
print(result.content.to_markdown()) # agent-friendly summary

# Cache stats
print(reader.cache_len())    # 1
print(reader.cache_bytes())  # ~128

# Also callable directly on the index
result2 = index.read_node_with_content(reader, "customer_churn_q2")

maggraph.toml for lakehouse mode:

[storage]
mode = "lakehouse"
root_path = "./knowledge_graph"

[lakehouse]
remote_sources = [
  { uri = "s3://corp-data/lake", format = "parquet" }
]

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MCP server scaffold

maggraph scaffold --mcp --output ./mcp_server

Generates a ready-to-run FastMCP server at ./mcp_server/server.py wired to your graph index — expose list_nodes, read_node, traverse, create_node, and delete_node as MCP tools with one command.

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Git-backed sync

# Leader pushes a snapshot
maggraph sync push --message "Add Q2 analysis nodes"

# Follower (read-only) pulls
maggraph sync pull

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API reference

Class / Function	Description
load_config(path)	Load maggraph.toml → ResolvedConfig
open_index(root_path)	Open graph index directly → GraphIndex
ResolvedConfig.open_index()	Open index from config
ResolvedConfig.open_lakehouse_reader()	Create a LakehouseReader
GraphIndex.list_nodes()	All node ids (sorted)
GraphIndex.read_node(id)	Node with metadata + body
GraphIndex.read_node_async(id)	Async version
GraphIndex.traverse(id, depth, order)	BFS/DFS → TraversalResult
GraphIndex.traverse_async(...)	Async version
GraphIndex.create_node(...)	Write new node to disk + index
GraphIndex.update_node(id, body)	Update body on disk
GraphIndex.delete_node(id)	Delete node from disk + index
GraphIndex.read_node_with_content(reader, id)	Resolve external content
LakehouseReader.read_node(index, id)	→ NodeWithContent
LakehouseReader.read_node_async(index, id)	Async version
LakehouseReader.cache_len()	Entries in content cache
LakehouseReader.cache_bytes()	Bytes in content cache
Node.id / .node_type / .body / .links / .source	Node properties
Node.to_markdown()	Full node as Markdown string
Node.to_dict()	Node as plain Python dict
ResolvedContent.kind	"local" / "text" / "external_asset"
ResolvedContent.body / .uri / .format	Content details
ResolvedContent.to_markdown()	Agent-friendly summary
NodeWithContent.node / .content	Node + resolved content

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Links

• GitHub
• Architecture & planning docs
• Examples
• CONTRIBUTING.md

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License

MIT OR Apache-2.0