Metadata-Version: 2.4
Name: membase-ai
Version: 0.2.6
Summary: Membase SDK: drop-in memory infrastructure for AI agents and apps, hosted or on your machine. Python client, CLI and MCP server.
Project-URL: Homepage, https://www.unibase.com/memory
Project-URL: Documentation, https://unibaseio.gitbook.io/unibase-docs/membase
Project-URL: Repository, https://github.com/unibaseio/membase-ai
Author: Unibase
License: MIT
License-File: LICENSE
Keywords: agent-memory,agents,ai,llm,llm-memory,long-term-memory,mcp,membase,memory
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: httpx<1,>=0.25
Provides-Extra: local
Requires-Dist: mcp<2,>=1.0; extra == 'local'
Requires-Dist: membase-core[all]>=0.2.1; (python_version >= '3.12') and extra == 'local'
Description-Content-Type: text/markdown

# membase-ai

Membase is drop-in memory infrastructure for AI agents and apps: context that persists, built for
production. This is its SDK for developers — the Python client, the `membase` command line, an MCP
server and the TypeScript client — for memory hosted in your Membase account or kept on your
machine.

[Unibase Memory](https://www.unibase.com/memory), the product for people, is powered by Membase:
the
[Chrome extension](https://chromewebstore.google.com/detail/unibase-memory/edmncknbiihfoakimejbepnaeemaaamf),
the [web app](https://www.app.membase.ai) and the desktop app. Docs:
[Membase](https://unibaseio.gitbook.io/unibase-docs/membase) ·
[Discord](https://discord.gg/nB9EfPGsSf) · [X](https://x.com/Unibase_AI).

Benchmarks of the engine: LoCoMo 93.1, LongMemEval_S 92.6, DMR 92.2, with ~6,500 context tokens
per LoCoMo question.

| | `pip install membase-ai` | `pip install 'membase-ai[local]'` |
|---|---|---|
| Memory lives | in your Membase account (hosted) | on this machine, under `~/.membase` |
| You need | an API key (`MEMBASE_API_KEY`) | `OPENAI_API_KEY` (see [Local settings](#local-settings)) |
| In code | `Membase()` | `Membase(local=True)` |
| Command line | `membase …` | `membase --local …`, `membase --local serve`, `membase --local mcp` |
| Install size, Python | small (httpx), 3.10+ | the engine (torch, faiss), Python 3.12 or 3.13 |

The methods and response shapes are the same either way; code moves between hosted and local
memory by changing the constructor. TypeScript: `npm install membase-ai`
([typescript/](https://github.com/unibaseio/membase-ai/tree/main/typescript)). Install `membase-ai`, not `membase`: that is an unrelated package
with the same import name.

```python
from membase import Membase

m = Membase()                    # hosted: MEMBASE_API_KEY (Connect › Developer keys)
# m = Membase(local=True)        # or local: ~/.membase

m.memories.add("We picked Postgres for the ledger service.", container="Engineering")
m.search("what database is the ledger on?")
m.add("Design notes …", container="Engineering", custom_id="design-1")   # a document
m.profile()
m.ask("Which database did we choose for the ledger?")   # hosted, this needs an agent-endpoint key
```

Every method is one operation of the Membase agent protocol (`list_containers`,
`search_memories`, `get_profile`, `list_documents`, `get_document`, `memory_rules`,
`add_memory`, `add_document`, `delete_document`, `forget_memory`, `ask_agent`). Hosted, the
service enforces each key's reach and access level. Local, the same operations run on the
[membase-core](https://pypi.org/project/membase-core/) engine: a memory becomes dated episodes,
a document becomes a topic tree, and search is the engine's multi-round retrieval. Removing a
document or forgetting a memory needs `confirm=True` in both.

## Command line

```bash
membase --local add "We picked Postgres for the ledger" --container Engineering
membase --local search "what did we pick for the ledger?"
membase --local ask "Which database is the ledger on?"
membase --local import ~/Downloads/claude-export.json     # Claude / ChatGPT / markdown / JSON chats
membase --local import ~/chats/ --dry-run                 # a directory, scanned; --dry-run only reports
membase --local documents add notes.md --container Engineering
membase --local profile
membase --local agent ingest trace.json --agent coder     # agent memory: traces -> cases and skills
membase --local agent search "fix flaky deploy" --agent coder
```

`--local` and `--store DIR` go before the command. Without them the commands use the hosted API
(`MEMBASE_API_KEY`), except `agent` and `serve`, which are local only; `import` needs
`membase-ai[local]` either way to read the exports, and hosted it stores each chat as a document.
`MEMBASE_LOCAL=1` (or a directory) makes local the default when `MEMBASE_API_KEY` is not set.

## MCP

```bash
claude mcp add membase -- membase --local mcp       # local memory
claude mcp add --transport http membase https://api.app.membase.io/mcp-http \
  --header "Authorization: Bearer $MEMBASE_API_KEY"  # hosted, at the key's access level
```

Hosted without the header, the client signs in and the connection is read-only. The local server
offers the hosted endpoint's agent-protocol tools: `list_containers`, `search_memories`,
`get_profile`, `list_documents`, `memory_rules`, `add_memory`, `add_document`,
`delete_document`, `forget_memory`, `ask_agent`.

## Local memory over HTTP

```bash
membase --local serve            # http://127.0.0.1:8787/v1
```

`membase serve` answers the agent-protocol routes of the hosted API (containers, search, profile,
rules, documents, memories, ask) from the local engine, so the TypeScript client, or anything else
that speaks the API, can use local memory by pointing its base URL at it. With
`MEMBASE_LOCAL_TOKEN` set, every request must carry that token as its bearer.

## Local settings

Local stores: the `default` container is `~/.membase/memory.db`, others are
`~/.membase/containers/<id>/`, and standing (`static`) profile facts are in
`~/.membase/profile/`. The engine reads its settings from the environment, and from a `.env` file
in the current directory or a parent without overriding what is already set:

| Setting | Default | |
|---|---|---|
| `OPENAI_API_KEY` | | chat (the default provider) and embeddings |
| `MEMBASE_LLM_PROVIDER` | `openai` when `OPENAI_API_KEY` is set | `anthropic` (with `ANTHROPIC_API_KEY`), `ollama` or `openai-compat` (with `MEMBASE_LLM_ENDPOINT`) for chat; then set the model names below to that provider's models |
| `MEMBASE_EMBED_PROVIDER` | `openai` | `openai-compat` sends embeddings to `MEMBASE_LLM_ENDPOINT` instead |
| `MEMBASE_EMBED_MODEL` | `text-embedding-3-small` | embeddings |
| `MEMBASE_EPISODE_MODEL` | `gpt-4.1-mini` | turns conversations into episodes |
| `MEMBASE_DECIDER_MODEL` | `gpt-4o-mini` | picks the episodes a search returns |
| `MEMBASE_READER_MODEL` | `gpt-4o` | writes `ask` answers |
| `MEMBASE_KNOWLEDGE_MODEL` | `gpt-4o-mini` | reads documents into topic trees |
| `MEMBASE_AGENT_MODEL` | `gpt-4o-mini` | `membase agent ingest` |
| `MEMBASE_LANG` | `en` | `zh` for Chinese extraction prompts |

## Where things live

| | |
|---|---|
| `membase/client.py` | the client (hosted, or local through `membase/local`) |
| `membase/local/` | the agent protocol over membase-core: backend, routes, `membase serve` |
| `membase/mcp/` | `membase mcp` |
| `membase/cli.py` | `membase` |
| `typescript/` | the npm package |

`membase-sdk` (PyPI and npm) is the earlier name of this package and is kept as an alias.

## License

MIT, for this repository: the client, command line, MCP server and TypeScript package.
`membase-ai[local]` also installs the compiled membase-core engine, which is under Unibase's
proprietary license.
