recollect-ai 0.2.1

Long-term memory layer for AI agents and assistants

Recollect

🌐 Live Demo · Interactive Showcase · LangChain · LlamaIndex

The showcase on this page is raw code.
For the real interactive presentation (add memories, hybrid search, chat simulation), go to the live site above.

Long-term memory layer for AI agents and assistants—self-hosted, library-first, and integration-ready for LangChain and LlamaIndex OSS workflows.

Why Recollect

• ADD-only facts from conversations (optional LLM extraction)
• Scoped memory — user_id, agent_id, run_id
• Hybrid retrieval — semantic + keyword + entity boost
• Pluggable stores — SQLite (default), Qdrant, Chroma, pgvector
• No platform required — embed in your app; open Apache 2.0

Install

git clone https://github.com/cobusgreyling/recollect.git
cd recollect
pip install -e ".[dev]"
pip install -e packages/llama-index-memory-recollect   # LlamaIndex adapter

PyPI (package name recollect-ai, import recollect):

pip install recollect-ai
pip install llama-index-memory-recollect

Extras

Extra	Purpose
openai	OpenAI LLM + embeddings
langchain	Tools + Runnable helpers
llamaindex	Core types for LlamaIndex apps
qdrant / chroma / pgvector	Vector backends
huggingface	HF sentence-transformers embedder
all	Everything

5-minute local demo (no API keys)

pip install -e .
recollect demo

from recollect import Memory, RecollectConfig

memory = Memory(RecollectConfig.local_dev())
memory.add("Prefers PostgreSQL over MySQL", user_id="alice", infer=False)
print(memory.search("database", filters={"user_id": "alice"}))

LangChain

from recollect import Memory, RecollectConfig
from recollect.integrations.langchain import create_recollect_tools, memory_runnable

memory = Memory(RecollectConfig.local_dev())
tools = create_recollect_tools(memory, user_id="alice")
ctx = memory_runnable(memory, user_id="alice").invoke({"input": "database prefs"})

Examples: examples/langchain_travel_agent.py, examples/langgraph_memory_node.py

LlamaIndex

from llama_index.memory.recollect import RecollectMemory
from recollect.config import RecollectConfig

memory = RecollectMemory.from_config(
    context={"user_id": "alice"},
    config=RecollectConfig.local_dev(),
)
# SimpleChatEngine.from_defaults(llm=llm, memory=memory)

Package: packages/llama-index-memory-recollect

API

Method	Purpose
add(...)	Extract/store memories
search(query, filters={}, top_k=5)	Hybrid retrieval
get / get_all / delete	CRUD

AsyncMemory provides the same API with async/await.

Development

make install
make test
make demo

Roadmap

• LangChain tools + Runnable
• LlamaIndex RecollectMemory
• Async API, multi-backend stores
• LlamaHub listing + upstream LlamaIndex notebook PR
• LangGraph cookbook in docs site

🌐 Live Interactive Showcase (recommended)

The best way to experience Recollect is the fully interactive browser demo:

→ https://cobusgreyling.github.io/recollect/showcase.html

It includes:

• Live add memory form with full scoping (user_id, agent_id, run_id)
• Hybrid search lab that shows semantic + keyword + entity scores (mirrors the Python implementation)
• Chat simulation — retrieve memories, get a simulated reply, and write the exchange back

Everything runs 100% in your browser using the same local-embedder + hybrid retrieval logic as the library. Data stays in your localStorage.

(The links to docs/showcase.html and docs/index.html in this README point at the source code on GitHub, which is why it looks like “just code”. The real presentation is at the link above.)

Acknowledgments

Patterns draw on public agent-memory research and OSS work, including additive extraction and multi-signal retrieval popularized by Mem0. Recollect is an independent codebase.

License

Apache 2.0 — see LICENSE.