actions-graph 0.1.1

Store and track LLM actions, tool calls, and sessions in Memgraph

Actions Graph

Store and track LLM actions, tool calls, and sessions in Memgraph.

Actions Graph provides a graph-based storage system for tracking all LLM interactions, including:

• Tool Calls: Function/tool invocations by the LLM
• Tool Results: Outputs from tool executions
• Messages: User, assistant, and system messages
• Structured Outputs: Validated JSON outputs from the LLM
• Subagent Events: Subagent lifecycle tracking
• Sessions: Conversation session management

Features

• 📊 Graph-based Storage: Store actions as nodes with relationships in Memgraph
• 🔗 Temporal Sequences: Track action order with FOLLOWED_BY relationships
• 🌳 Nested Actions: Support for parent-child action relationships (e.g., subagents)
• 🏷️ Session Management: Create, track, and query sessions with tags
• 📈 Analytics: Built-in queries for tool usage stats and session summaries
• 🔌 Agent Context Graph Integration: Consume normalized runtime events through ActionsGraphConnector

Installation

pip install actions-graph

For Agent Context Graph integration:

pip install "actions-graph[agent-context-graph]"

Quick Start

Basic Usage

from actions_graph import ActionsGraph, Session, ToolCall

# Initialize the graph
graph = ActionsGraph()
graph.setup()  # Create indexes and constraints

# Create a session
session = Session(
    session_id="session-123",
    model="claude-sonnet-4-20250514",
    working_directory="/path/to/project",
)
graph.create_session(session)

# Record a tool call
tool_call = graph.record_tool_call(
    session_id="session-123",
    tool_name="Read",
    tool_input={"file_path": "/path/to/file.py"},
    tool_use_id="tool-use-001",
)

# Record the result
tool_result = graph.record_tool_result(
    session_id="session-123",
    tool_use_id="tool-use-001",
    tool_name="Read",
    content="def hello():\n    print('Hello, World!')",
)

# Get session summary
summary = graph.get_session_summary("session-123")
print(f"Actions: {summary['action_count']}, Tools: {summary['tool_call_count']}")

Agent Context Graph Integration

from actions_graph import ActionsGraph
from actions_graph.connector import ActionsGraphConnector
from agent_context_graph import AgentLink
from agent_context_graph.adapters.claude import ClaudeAdapter

graph = ActionsGraph()
graph.setup()

link = AgentLink()
link.add_connector(ActionsGraphConnector(graph))

adapter = ClaudeAdapter(link, session_id="my-session-123")
hooks = adapter.get_runtime_hooks()

Actions Graph should consume runtime activity through Agent Context Graph when possible. Runtime adapters normalize SDK callbacks and command hooks into the shared Event Protocol; ActionsGraphConnector decides which events become session and action nodes.

Graph Schema

Nodes

• Session: LLM conversation sessions

  • Properties: session_id, started_at, ended_at, status, model, total_cost_usd, etc.

• Action: Individual actions with type-specific labels

  • Labels: ToolCall, ToolResult, Message, StructuredOutput, SubagentEvent, etc.
  • Properties: action_id, session_id, action_type, timestamp, status, etc.

• Tool: Tool definitions

  • Properties: name, is_mcp, mcp_server

• Tag: Session/action tags

  • Properties: name

Relationships

(:Session)-[:HAS_ACTION]->(:Action)
(:Action)-[:FOLLOWED_BY]->(:Action)
(:Action)-[:PARENT_OF]->(:Action)
(:Session)-[:FORKED_FROM]->(:Session)
(:Action)-[:USED_TOOL]->(:Tool)
(:Session)-[:HAS_TAG]->(:Tag)

API Reference

ActionsGraph

Main class for interacting with the graph.

graph = ActionsGraph()

# Setup
graph.setup()      # Create indexes
graph.drop()       # Remove indexes
graph.clear()      # Clear all data

# Sessions
graph.create_session(session)
graph.get_session(session_id)
graph.end_session(session_id, status=ActionStatus.COMPLETED)
graph.list_sessions(limit=100, status=None, tag=None)

# Actions
graph.record_action(action)
graph.record_tool_call(session_id, tool_name, tool_input, ...)
graph.record_tool_result(session_id, tool_use_id, tool_name, content, ...)
graph.record_message(session_id, role, content, ...)
graph.get_action(action_id)
graph.get_session_actions(session_id, action_type=None, limit=1000)

# Analytics
graph.get_tool_usage_stats(session_id=None)
graph.get_action_sequence(session_id, include_content=False)
graph.get_session_summary(session_id)

Action Types

Type	Model Class	Description
tool_call	ToolCall	Tool/function invocation
tool_result	ToolResult	Tool execution result
user_message	Message	User input
assistant_message	Message	LLM response
system_message	Message	System messages
structured_output	StructuredOutput	Validated JSON output
subagent_start	SubagentEvent	Subagent started
subagent_stop	SubagentEvent	Subagent completed
error	ErrorEvent	Error occurred
permission_request	PermissionRequest	Permission requested
rate_limit	RateLimitEvent	Rate limit event

Agent Context Graph Connector

from actions_graph.connector import ActionsGraphConnector

connector = ActionsGraphConnector(graph)
link.add_connector(connector)

The connector records:

• SessionStartEvent and SessionEndEvent as session lifecycle data
• ToolStartEvent as ToolCall action nodes
• ToolEndEvent as ToolResult action nodes
• MessageEvent, AgentStartEvent, AgentEndEvent, and ErrorOccurredEvent as action nodes

Claude Agent SDK Hooks

Direct Claude Agent SDK hooks remain available for standalone use, but Agent Context Graph is the preferred integration path when multiple graph components need the same runtime event stream.

For Claude Agent SDK integration:

from actions_graph.hooks import create_tracking_hooks, ActionTracker

# Simple usage
hooks = create_tracking_hooks(graph, session_id)

# Advanced usage with custom tracker
tracker = ActionTracker(
    graph,
    session_id,
    track_tool_calls=True,
    track_tool_results=True,
    track_messages=True,
    track_subagents=True,
    track_permissions=True,
    track_errors=True,
)

Example Queries

Find sessions with errors

sessions = graph.list_sessions(status=ActionStatus.FAILED)

Get all tool calls in a session

from actions_graph import ActionType

tool_calls = graph.get_session_actions(
    session_id,
    action_type=ActionType.TOOL_CALL,
)

Custom Cypher queries

rows = graph._db.query("""
    MATCH (s:Session {session_id: $session_id})-[:HAS_ACTION]->(a:ToolCall)
    RETURN a.tool_name AS tool, count(*) AS count
    ORDER BY count DESC
""", params={"session_id": "my-session"})

License

MIT