tracemem-core 0.1.2

Knowledge graph memory system for AI agents

TraceMem Core

Knowledge graph memory system for AI agents using Neo4j and LanceDB.

Installation

# Using UV (recommended)
uv add tracemem-core

# Using pip
pip install tracemem-core

Requirements

• Python 3.12+
• Neo4j 5.x instance (local or cloud)
• OpenAI API key (for embeddings, or provide a custom embedder)

Quick Start

With LangChain Adapter

from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from tracemem_core import TraceMem, TraceMemConfig
from tracemem_core.adapters.langchain import LangChainAdapter

adapter = LangChainAdapter()

config = TraceMemConfig(
    mode="local",
    project_root=Path.cwd(),
    neo4j_uri="bolt://localhost:7687",
    neo4j_user="neo4j",
    neo4j_password="your-password",
)

async with TraceMem(config=config) as tm:
    # Convert LangChain messages to internal format
    messages = adapter.convert([
        HumanMessage(content="Fix the auth bug in login.py"),
        AIMessage(
            content="I'll check the authentication code...",
            tool_calls=[{"id": "call_1", "name": "read_file", "args": {"path": "src/login.py"}}],
        ),
        ToolMessage(content="def login(): ...", tool_call_id="call_1"),
    ])

    # Import the conversation
    await tm.import_trace("conv-123", messages)

Direct Usage (No Framework)

from tracemem_core import TraceMem, Message, ToolCall

async with TraceMem() as tm:
    # Add messages directly
    await tm.add_message("conv-123", Message(role="user", content="Read auth.py"))
    await tm.add_message("conv-123", Message(
        role="assistant",
        content="I'll read that file",
        tool_calls=[ToolCall(id="call_1", name="read_file", args={"path": "auth.py"})],
    ))
    await tm.add_message("conv-123", Message(
        role="tool",
        content="def authenticate(): ...",
        tool_call_id="call_1",
    ))

Configuration

Environment Variables

All settings can be configured via environment variables with the TRACEMEM_ prefix:

export TRACEMEM_NEO4J_URI="bolt://localhost:7687"
export TRACEMEM_NEO4J_USER="neo4j"
export TRACEMEM_NEO4J_PASSWORD="password"
export TRACEMEM_OPENAI_API_KEY="sk-..."
export TRACEMEM_MODE="local"

TraceMemConfig Options

Option	Type	Default	Description
mode	"local" | "global"	"local"	Operating mode for path handling
project_root	Path	None	Root directory for local mode
neo4j_uri	str	"bolt://localhost:7687"	Neo4j connection URI
neo4j_user	str	"neo4j"	Neo4j username
neo4j_password	str	"password"	Neo4j password
neo4j_database	str	"neo4j"	Neo4j database name
lancedb_path	Path	Auto	Vector store location
embedding_model	str	"text-embedding-3-small"	OpenAI embedding model
embedding_dimensions	int	1536	Embedding vector dimensions
openai_api_key	str	None	OpenAI API key

Operating Modes

Local Mode

Paths are stored relative to project_root. The project can be moved or renamed and memory remains valid.

config = TraceMemConfig(
    mode="local",
    project_root=Path("/home/user/my-project"),
)

# File at /home/user/my-project/src/auth.py
# Stored as: file://src/auth.py

Global Mode

Paths are stored as absolute paths. Memory spans across projects.

config = TraceMemConfig(mode="global")

# File at /home/user/my-project/src/auth.py
# Stored as: file:///home/user/my-project/src/auth.py

API Reference

TraceMem

The main interface for the knowledge graph memory system.

async with TraceMem(config, embedder, resource_extractor) as tm:

Create a TraceMem instance. Must be used as an async context manager.

Parameters:

• config (TraceMemConfig, optional): Configuration settings
• embedder (Embedder, optional): Custom embedder implementation
• resource_extractor (ResourceExtractor, optional): Custom resource extractor

---

await tm.import_trace(conversation_id, messages)

Import a conversation trace from a list of Messages.

Parameters:

• conversation_id (str): Unique identifier for the conversation
• messages (list[Message]): List of Message objects to import

Returns: dict[str, UUID] - Created node IDs

from tracemem_core.adapters.langchain import LangChainAdapter

adapter = LangChainAdapter()
messages = adapter.convert(langchain_messages)
result = await tm.import_trace("conv-123", messages)
# result = {"user_text": UUID(...), "agent_text": UUID(...), ...}

---

await tm.add_message(conversation_id, message)

Add a single message to the knowledge graph.

Parameters:

• conversation_id (str): Unique identifier for the conversation
• message (Message): The Message to add

Returns: dict[str, UUID] - Created node IDs

await tm.add_message("conv-123", Message(role="user", content="Hello"))
await tm.add_message("conv-123", Message(
    role="assistant",
    content="I'll read that file",
    tool_calls=[ToolCall(id="c1", name="read_file", args={"path": "auth.py"})],
))

Data Models

Message

Internal message representation. Framework-agnostic.

from tracemem_core import Message, ToolCall

# User message
Message(role="user", content="Hello")

# Assistant message with tool calls
Message(
    role="assistant",
    content="I'll read that file",
    tool_calls=[ToolCall(id="call_1", name="read_file", args={"path": "auth.py"})],
)

# Tool result
Message(role="tool", content="file contents...", tool_call_id="call_1")

# System message
Message(role="system", content="You are a helpful assistant")

ToolCall

A tool invocation within an assistant message.

ToolCall(
    id="call_123",           # Unique tool call ID
    name="read_file",        # Tool name
    args={"path": "auth.py"} # Tool arguments (default: {})
)

Adapters

Adapters convert framework-specific messages to TraceMem's internal Message format.

LangChainAdapter

Converts LangChain messages (HumanMessage, AIMessage, ToolMessage, SystemMessage).

from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from tracemem_core.adapters.langchain import LangChainAdapter

adapter = LangChainAdapter()

# Convert a list of messages
messages = adapter.convert([
    HumanMessage(content="Hello"),
    AIMessage(content="Hi there!"),
])

# Convert a single message
message = adapter.convert_single(HumanMessage(content="Hello"))

Custom Adapter

Implement the TraceAdapter protocol:

from tracemem_core.adapters.protocol import TraceAdapter
from tracemem_core import Message

class MyAdapter:
    def convert(self, messages: list) -> list[Message]:
        return [self.convert_single(m) for m in messages]

    def convert_single(self, message) -> Message:
        # Your conversion logic
        return Message(role="user", content=str(message))

Resource Extraction

TraceMem automatically extracts resource URIs from tool calls using the ResourceExtractor protocol.

Default Extractor

The DefaultResourceExtractor looks for common argument patterns:

• File arguments: path, file_path, filepath, file, filename
• URL arguments: url, uri, endpoint

# These tool calls will have resources extracted:
ToolCall(name="read_file", args={"path": "src/auth.py"})      # -> file://src/auth.py
ToolCall(name="fetch", args={"url": "https://api.example.com"}) # -> https://api.example.com

Custom Extractor

Implement the ResourceExtractor protocol for custom extraction:

from tracemem_core import ResourceExtractor

class MyExtractor:
    def extract(self, tool_name: str, args: dict) -> str | None:
        if tool_name == "query_database":
            return f"db://{args.get('table')}"
        if tool_name == "read_file" and "path" in args:
            return f"file://{args['path']}"
        return None

async with TraceMem(resource_extractor=MyExtractor()) as tm:
    ...

Custom Embedder

Implement the Embedder protocol to use a custom embedding model:

from tracemem_core import Embedder, TraceMem

class MyEmbedder:
    @property
    def dimensions(self) -> int:
        return 768

    async def embed(self, text: str) -> list[float]:
        # Your embedding logic
        return [0.1] * 768

    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
        return [await self.embed(t) for t in texts]

async with TraceMem(embedder=MyEmbedder()) as tm:
    ...

Neo4j Setup

Option 1: Docker Compose (Recommended for Development)

The repository includes a docker-compose.yml for local development:

# Start Neo4j (uses port 17687 to avoid conflicts)
docker compose up -d neo4j

# Check status
docker compose ps

# Stop
docker compose down

Connection settings for development:

• URI: bolt://localhost:17687
• User: neo4j
• Password: testpassword

Option 2: Neo4j Desktop

1. Download Neo4j Desktop
2. Create a new project and database
3. Start the database
4. Use the bolt URI shown in the connection details (default: bolt://localhost:7687)

Option 3: Docker (Manual)

docker run -d \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/your-password \
  neo4j:5-community

Option 4: Neo4j Aura (Cloud)

1. Create a free instance at Neo4j Aura
2. Use the provided connection URI

Graph Schema

TraceMem automatically creates the following schema:

Node Labels

Label	Description	Key Properties
UserText	User messages (searchable)	id, text, conversation_id
AgentText	Agent responses	id, text, conversation_id
ResourceVersion	Resource snapshots	id, content_hash, uri
Resource	Resource identity	id, uri, current_content_hash

Relationships

Type	From	To	Description
MESSAGE	UserText	AgentText	Links user query to agent response
VERSION_OF	ResourceVersion	Resource	Links version to resource identity
{TOOL_NAME}	AgentText	ResourceVersion	Tool invocation (READ_FILE, EDIT, etc.)

Development

Setup

# Clone and install dependencies
git clone <repo-url>
cd tracemem/tracemem_core
uv sync --dev

Running Tests

# Start Neo4j for integration tests
docker compose up -d neo4j

# Run all tests
uv run pytest tests/ -v

# Run unit tests only (no Neo4j required)
uv run pytest tests/ -v --ignore=tests/storage/graph/ --ignore=tests/integration/

# Run integration tests only
uv run pytest tests/integration/test_tracemem_integration.py -v

# Run with coverage
uv run pytest tests/ --cov=src/tracemem_core --cov-report=html

Test Markers

• neo4j: Tests requiring a Neo4j database
• openai: Tests requiring an OpenAI API key

# Skip Neo4j tests
uv run pytest tests/ -v -m "not neo4j"

# Skip OpenAI tests
uv run pytest tests/ -v -m "not openai"

License

MIT