openmemo 0.6.0

The Memory Infrastructure for AI Agents

OpenMemo

The Memory Infrastructure for AI Agents.

Most AI memory systems today are just wrappers around vector databases.

OpenMemo is different.

Instead of storing memory as flat embeddings, OpenMemo introduces a structured memory architecture designed for long-running AI systems.

MemCell → MemScene → Memory Pyramid → Reconstructive Recall

OpenMemo enables AI agents to remember, evolve, and reason over past experience — rather than simply retrieving text chunks.

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Quickstart

Install

pip install openmemo

Python SDK

from openmemo import Memory

memory = Memory()

# Write memories with agent isolation and scenes
memory.add("User prefers PostgreSQL for production",
           agent_id="my_agent",
           scene="infrastructure",
           cell_type="preference")

memory.add("Always run tests before deploying",
           agent_id="my_agent",
           scene="workflow",
           cell_type="constraint")

# Recall with context
results = memory.recall("database preference", agent_id="my_agent")
for r in results:
    print(r["content"], r["score"])

# List scenes
scenes = memory.scenes(agent_id="my_agent")

# Delete a memory
memory.delete(memory_id)

REST API

# Start local server
pip install "openmemo[server]"
openmemo serve --port 8080

# Or use the cloud API
# Write
curl -X POST https://api.openmemo.ai/memory/write \
  -H "Content-Type: application/json" \
  -d '{
    "content": "User prefers PostgreSQL",
    "agent_id": "my_agent",
    "scene": "infrastructure",
    "cell_type": "preference"
  }'

# Recall
curl -X POST https://api.openmemo.ai/memory/recall \
  -H "Content-Type: application/json" \
  -d '{"query": "database preference", "agent_id": "my_agent"}'

# Search
curl -X POST https://api.openmemo.ai/memory/search \
  -H "Content-Type: application/json" \
  -d '{"query": "database", "agent_id": "my_agent"}'

# Scenes
curl https://api.openmemo.ai/memory/scenes?agent_id=my_agent

# Delete
curl -X DELETE https://api.openmemo.ai/memory/{id}

MCP Adapter (for Claude)

from openmemo.adapters.mcp import OpenMemoMCPServer

server = OpenMemoMCPServer()
tools = server.get_tools()  # memory_write, memory_recall, memory_search
result = server.handle_tool("memory_write", {"content": "User prefers Python"})

LangChain Adapter

from openmemo.adapters.langchain import OpenMemoMemory

memory = OpenMemoMemory(agent_id="my_agent")
memory.save_context({"input": "hello"}, {"output": "hi"})
history = memory.load_memory_variables({"input": "greeting"})

---

Key Concepts

agent_id — Multi-Agent Isolation

Each agent gets its own memory namespace. Memories are isolated by agent_id.

# Agent A's memories
memory.add("prefers Python", agent_id="agent_a")

# Agent B's memories
memory.add("prefers Rust", agent_id="agent_b")

# Only returns agent_a's memories
memory.recall("language preference", agent_id="agent_a")

scene — Contextual Grouping

Scenes group related memories by context. They are auto-created when you write with a scene parameter.

memory.add("Use Flask for API", agent_id="a1", scene="project_setup")
memory.add("Deploy to AWS", agent_id="a1", scene="infrastructure")

# Filter recall by scene
memory.recall("setup", agent_id="a1", scene="project_setup")

cell_type — Typed Memory

MemCells support 5 types for structured memory:

Type	Use Case
fact	Factual information (default)
decision	Choices and rationale
preference	User/agent preferences
constraint	Rules and limitations
observation	Behavioral observations

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Why OpenMemo?

Most AI memory systems work like this:

Store → Embed → Similarity Search → Inject Context

This breaks when AI systems run for long periods:

• Memory becomes noisy
• Conflicting facts accumulate
• Context windows explode
• Past reasoning is lost

OpenMemo solves these with a structured memory architecture:

MemCell — Atomic Memory

Each memory is a structured unit with lifecycle stages, importance scoring, and conflict detection.

MemScene — Contextual Memory

Related memories are grouped into scenes, reducing retrieval noise.

Memory Pyramid — Hierarchical Compression

L0  Profile Memory
L1  Category Memory
L2  Episodic Memory
L3  Raw Events

Reconstructive Recall

Instead of returning raw chunks, OpenMemo reconstructs coherent narratives with conflict annotations.

Memory Governance

Conflict detection, memory evolution, maintenance workers, and duplicate cleanup.

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Architecture

Applications / Agents
      │
      ▼
OpenMemo SDK (Memory class)
      │
      ▼
OpenMemo Core
  ├── MemCell Engine (typed cells, lifecycle, evolution)
  ├── Scene Manager (auto-detection, grouping)
  ├── Recall Engine (BM25 + Vector, hybrid retrieval)
  ├── Reconstruct Engine (narrative + conflict annotation)
  ├── Memory Pyramid (hierarchical compression)
  ├── Skill Engine (pattern extraction)
  └── Governance Layer (conflict detection, versioning)
      │
      ▼
Storage (SQLite default, pluggable)

Adapters

Adapter	Status	Usage
MCP (Claude)	Available	from openmemo.adapters.mcp import OpenMemoMCPServer
LangChain	Available	from openmemo.adapters.langchain import OpenMemoMemory
OpenClaw	Available	from openmemo.adapters.openclaw import OpenClawMemoryBackend

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API Reference

REST Endpoints

Method	Path	Description
POST	/memory/write	Write a memory
POST	/memory/recall	Recall relevant memories
POST	/memory/search	Search memories (raw top-K)
GET	/memory/scenes	List all scenes
DELETE	/memory/{id}	Delete a memory
POST	/memory/reconstruct	Reconstruct narrative
POST	/api/maintain	Run maintenance
GET	/api/stats	Get statistics
GET	/health	Health check
GET	/docs	API documentation

SDK Methods

memory = Memory(db_path="openmemo.db")

memory.add(content, agent_id="", scene="", cell_type="fact")
memory.recall(query, agent_id="", scene="", top_k=10, budget=2000)
memory.search(query, agent_id="", top_k=10)
memory.reconstruct(query, agent_id="")
memory.scenes(agent_id="")
memory.delete(memory_id)
memory.maintain()
memory.stats()

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Cookbooks

See cookbooks/ for complete examples:

• coding_assistant.py — Programming assistant with project context
• customer_support.py — Support agent with customer history
• personal_memory.py — Personal assistant with evolving knowledge

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Comparison

	Vector DB	Chat History	OpenMemo
Structure	Flat embeddings	Flat log	Hierarchical (MemCell + MemScene)
Conflict handling	None	None	Automatic detection + resolution
Evolution	Append-only	Append-only	Consolidate, promote, forget
Recall	Top-K similarity	Last N messages	Hybrid retrieval + reconstructive recall
Token control	Fixed window	Grows forever	Pyramid auto-compression
Agent isolation	Manual	None	Built-in agent_id
Governance	None	None	Built-in maintenance

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Installation

From PyPI

pip install openmemo           # Core SDK
pip install "openmemo[server]" # With REST server

From GitHub

pip install git+https://github.com/openmemoai/openmemo.git

Development

git clone https://github.com/openmemoai/openmemo.git
cd openmemo
pip install -e ".[dev]"
pytest tests/

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Contributing

We welcome community contributions.

Good areas for contribution include:

• New adapters for AI frameworks
• Example cookbooks
• Storage backends
• Documentation improvements

Core memory engine changes require review by the maintainers.

See CONTRIBUTING.md for details.

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License

OpenMemo is released under the Apache License 2.0.

See the LICENSE file for full details.

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Community

OpenMemo is an early-stage project exploring long-term memory for AI systems.

Feedback, ideas, and contributions are welcome.