reeve 0.1.13

Lifetime persistent memory for LLMs. A lightweight SDK for structured knowledge graphs.

Reeve

Add persistent memory to any AI agent in one line.

from reeve import ReeveAgent

agent = ReeveAgent(llm=llm)

agent.chat("I prefer Python over JavaScript")
print(agent.chat("What programming language do I prefer?"))

Python

No manual retrieval. No manual storage. No prompt engineering.

Reeve automatically retrieves, updates, and shares memory across AI agents.

---

Why Reeve?

Most AI applications rely on:

• Chat history
• Vector databases
• Custom memory prompts
• Hand-written retrieval pipelines

As applications grow, these approaches become difficult to maintain and often fail to preserve long-term context.

Reeve provides a persistent memory layer that:

• Stores durable knowledge instead of raw conversation logs
• Automatically retrieves relevant memory before model calls
• Extracts and stores new knowledge after conversations
• Shares memory across agents, frameworks, and models
• Works with existing AI stacks without changing application logic

---

Quick Start

Install Reeve:

pip install reeve

One-Line Agent Memory

from reeve import ReeveAgent

agent = ReeveAgent(llm=llm)

agent.chat("Our startup uses Memgraph")

print(
    agent.chat("What database do we use?")
)

Memgraph

Memory retrieval, storage, and updates happen automatically.

---

Which API Should I Use?

I want memory in 30 seconds

from reeve import ReeveAgent

agent = ReeveAgent(llm=llm)

I already use LangGraph, LangChain, CrewAI, Agno, or another framework

from reeve import ReeveMemory

agent = Agent(
    middleware=[
        ReeveMemory()
    ]
)

I want complete control

from reeve.tools import (
    store_memory,
    query_memory
)

---

How Reeve Works

User Message
    ↓
Retrieve Memory
    ↓
Inject Context
    ↓
LLM
    ↓
Extract Knowledge
    ↓
Store Memory

Reeve handles the memory lifecycle automatically so developers can focus on building agents instead of memory infrastructure.

---

Features

• ReeveAgent for one-line memory integration

• ReeveMemory middleware for existing agent frameworks

• Shared memory across agents and models

• Automatic memory retrieval and injection

• Durable knowledge extraction

• Relationship-aware memory graph

• Entity deduplication and reconciliation

• Importance-aware memory storage

• Team and organization memory via namespaces

• Framework adapters for:

  • OpenAI Agents SDK
  • LangGraph
  • LangChain
  • CrewAI
  • Agno

• Hosted MCP support

• Backward-compatible low-level APIs

---

Shared Memory Across Agents

Use the same namespace across multiple agents:

from reeve import ReeveMemory

memory = ReeveMemory(
    namespace="company_memory"
)

Claude, GPT, Gemini, Cursor, LangGraph agents, and custom applications can share the same memory layer.

---

Framework Support

OpenAI Agents SDK

from reeve import ReeveMiddleware
from reeve.adapters import OpenAIAgentsReeveAdapter

LangGraph

from reeve import ReeveMemory

LangChain

from reeve import ReeveMemory

CrewAI

from reeve import ReeveMemory

Agno

from reeve import ReeveMemory

---

Backward Compatibility

Existing integrations continue to work:

from reeve.tools import (
    store_memory,
    query_memory
)

No migration is required.

---

Documentation

The full developer guide includes:

• ReeveAgent
• ReeveMemory
• Framework adapters
• Shared memory
• Retrieval policies
• Advanced configuration
• Migration guides

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License

MIT