reme-ai

library0.3.1.10pypypiunverified

ReMe (Remember Me, Refine Me) is a Python library designed for managing and refining an AI agent's long-term memory. It provides functionalities for summarization, retrieval, and contextual awareness, enabling agents to learn and adapt over time. The library is actively maintained with frequent minor releases (multiple per month) to introduce new features, fix bugs, and refine existing memory mechanisms.

llm-agents ai-ml

pip install reme-ai

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · REME-AI

reme-ai

llm-agentspythonv0.3.1.10

Install

44.4s avg

Import

—

Disk

766MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.3.1.9 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 51.75s

py 3.11

✕ build_error

✓ 45.48s

py 3.12

✕ build_error

✓ 39.5s

py 3.13

✕ build_error

✓ 40.78s

py 3.9

✕ build_error

766MB installed

● package 766MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ReMe

✓ from reme import ReMe

✗ import reme

This quickstart demonstrates how to initialize ReMe, add experiences, and then retrieve and summarize them. Ensure you have an LLM API key (e.g., OPENAI_API_KEY) set as an environment variable, as summarization and retrieval functionalities depend on it. If the key is missing, LLM-dependent operations will fail.

import os from reme import ReMe # Ensure your OpenAI API key is set as an environment variable # e.g., export OPENAI_API_KEY='sk-...' openai_api_key = os.environ.get('OPENAI_API_KEY', '') if not openai_api_key: print("WARNING: OPENAI_API_KEY is not set. ReMe operations requiring an LLM will fail.") # Initialize ReMe with a unique agent ID. This creates a local memory store. re_me = ReMe(agent_id="my_first_agent") # Add experiences to the agent's memory re_me.add_experience("I met a new colleague today named Sarah. She works on the frontend team.") re_me.add_experience("Sarah is quite good at React and uses TypeScript.") re_me.add_experience("She mentioned she loves to hike and explore national parks.") # Retrieve relevant memories based on a query query = "Tell me about Sarah." retrieved_memories = re_me.retrieve(query) print(f"\nRetrieved memories for '{query}':") for mem in retrieved_memories: print(f"- {mem}") # Summarize memories (requires LLM interaction) try: summarized_memory = re_me.summarize_memories( query="What do I know about Sarah's work and hobbies?", count=3 ) print(f"\nSummarized memory: {summarized_memory}") except Exception as e: print(f"\nFailed to summarize memories (API key likely missing/invalid): {e}") # To clear the agent's memory store (optional) # re_me.clear_memory()

Debug

Known issues

gotchaBefore v0.3.1.8, attempting to use `chromadb`-backed memory without `chromadb` installed could lead to import errors. While the library now handles this gracefully, users leveraging ChromaDB features must explicitly install `chromadb` (`pip install chromadb`).

fix

Install `chromadb` explicitly if using ChromaDB features: `pip install reme-ai[chroma]` or `pip install chromadb`.

affects: <0.3.1.8

gotchaReMe relies on external LLM services (e.g., OpenAI, LiteLLM) for key functionalities like summarization and retrieval. Ensure required API keys (e.g., `OPENAI_API_KEY`) are set in environment variables or `*.env` files for `ReMe` to function correctly. Without these, LLM-dependent operations will fail.

fix

Set necessary API keys as environment variables (e.g., `export OPENAI_API_KEY='your_key'`) or ensure they are loaded via `python-dotenv`.

affects: All versions

breakingIn v0.3.1.4, core text truncation utilities were refactored and replaced with a new marker system. Custom integrations or logic that directly interacted with or relied on previous internal truncation methods might require review and adaptation.

fix

Review any custom logic that interacts with ReMe's internal text truncation. Prefer using public APIs for truncation or context management.

affects: >=0.3.1.4

gotchaFrom v0.3.1.5 onwards, `summarize` and `retrieve` operations are designed to surface failures more explicitly instead of masking them. If your application previously relied on silent failures for these operations, it might now encounter exceptions for operations that fail.

fix

Implement proper error handling (try-except blocks) around `summarize_memories` and `retrieve` calls to gracefully manage potential failures.

affects: >=0.3.1.5

gotchaThe changelog for v0.3.1.3 states `litellm` was moved to dev dependencies. However, `litellm` is still listed in the `requirements.txt` for v0.3.1.8, implying it remains a core dependency. Users should rely on `pip install reme-ai` to ensure all necessary dependencies are installed.

fix

Always install `reme-ai` via `pip install reme-ai` to ensure all required dependencies, including `litellm`, are correctly managed.

affects: >=0.3.1.3

Upgrade

Version history

0.3.1.10latest on PyPI · released Jun 3, 2026

Audit

Dependencies

python>=3.10requiredRequired Python version as per pyproject.toml

chromadboptionalOptional: Required for using ChromaDB-backed memory systems.

sentence-transformersoptionalOptional: Required for certain local embedding models and vector store functionalities.

Agent activity

45 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

githubhttps://github.com/agentscope-ai/ReMe ↗

packagehttps://pypi.org/project/reme-ai/ ↗

docshttps://reme.agentscope.io/ ↗