OpenClaw-memgpt – MemGPT's three-tier memory architecture as an OpenClaw plugin

OpenClaw-memgpt, a new plugin for the OpenClaw agent framework, implements MemGPT's three-tier memory architecture—core, recall, and archival memory—to provide persistent, searchable memory across sessions. The plugin is provider-agnostic, supports cross-session persistence and conversation summarization, and is available for OpenClaw ≥ 2026.5.31 with Python 3.11 and Node ≥ 20.12.0.

OpenClaw plugin providing MemGPT's three-tier memory architecture for any OpenClaw agent. openclaw-memgpt gives an OpenClaw agent the memory architecture from MemGPT https://github.com/letta-ai/letta : three tiers the agent manages itself through tool calls. Core memory — a small, always-in-context block holding the agent's persona and what it knows about you. The agent edits it directly as facts change. Recall memory — the searchable history of your conversation, so the agent can look up what was said earlier even after it has scrolled out of context. Archival memory — an embedding-backed long-term store for facts the agent chooses to keep, retrievable by semantic search. On top of the tiers the plugin provides cross-session persistence memory survives restarts — the default in gateway mode, and supported in one-shot --local mode and conversation summarisation older turns are compacted into recall memory when context fills up, instead of being lost . It is provider-agnostic : the memory sidecar talks to any OpenAI-compatible endpoint OpenAI, Anthropic, a local LLM, LiteLLM/OpenRouter, etc. , configured once through the setup wizard. Under the hood the plugin runs a small Python sidecar built on a maintained MemGPT fork, published to PyPI as openclaw-memgpt-sidecar https://pypi.org/project/openclaw-memgpt-sidecar/ . The plugin spawns and manages that process for you. OpenClaw ≥ 2026.5.31 Python 3.11 3.12 not yet supported — for the memory sidecar Node ≥ 20.12.0 — the plugin uses it to run the sidecaron your uv PATH openclaw plugins install openclaw-memgpt On first use the plugin notices it isn't configured yet and points you at the setup wizard. Run it any time to re configure: openclaw memgpt setup Optionally, warm the embedding-model cache ahead of time so your first agent turn isn't slowed by a one-time ~60 s model download: openclaw memgpt prewarm To remove the plugin and its data: openclaw memgpt uninstall The interactive uninstall lets you keep your memory data or remove everything; --keep-data , --force , and --dry-run are also available. 1. Install the plugin openclaw plugins install openclaw-memgpt 2. Configure it provider, API key, model, … openclaw memgpt setup 3. optional Pre-download the embedding model so turn 1 is fast openclaw memgpt prewarm 4. Talk to your agent — it now remembers across turns and sessions openclaw agent --message "Hi, I'm working on a thesis due March 15. Remember that." openclaw agent --message "When is my thesis due?" → the agent recalls "March 15" from memory openclaw memgpt setup walks you through, in order: Provider — Anthropic, OpenAI, or any OpenAI-compatible endpoint. Base URL — only for the OpenAI-compatible option. API key — paste it stored in a private mode- 0600 file or point the plugin at an environment variable you already have set e.g ANTHROPIC API KEY / OPENAI API KEY . Keys are never written into openclaw.json . Model — the model the sidecar should use. Observability — off default , default , or verbose see below . Sidecar URL — leave blank to let the plugin spawn and manage the sidecar itself; set it only if you run the sidecar yourself. A few notes: Conversation access. Storing memory means reading the conversation, so the plugin needs OpenClaw's conversation-access permission. In gateway mode OpenClaw requires you to grant this explicitly; the setup wizard does it for you and tells you so. Manual sidecar advanced . Set OPENCLAW MEMGPT SIDECAR URL or the sidecarUrl config field to attach to a sidecar you started yourself instead of having the plugin spawn one. The agent calls these tools itself; you don't invoke them directly. | Tool | What it does | |---|---| core memory append | Append to the contents of core memory. | core memory replace | Replace the contents of core memory. To delete memories, use an empty string for new content. | archival memory insert | Add to archival memory, phrased so it can be queried later. | archival memory search | Search archival memory using semantic embedding-based search. | conversation search | Search prior conversation history using case-insensitive string matching. | conversation search date | Search prior conversation history using a date range. | send message | Send a message to the user. | When enabled, the plugin writes a structured event log as JSON Lines to <OpenClaw state dir /memgpt-observability.jsonl e.g. ~/.openclaw/memgpt-observability.jsonl . Three levels: - default — no events captured. off - — one event per memory operation, default metadata only no message/query/result text : {"kind":"archival search","namespace":"my-agent","ts":"2026-06-23T02:27:40.833Z","meta":{"total":3,"page":0,"numPages":1}} - — the same events verbose plus the content queries, results, written text for full provenance: {"kind":"archival search","namespace":"my-agent","ts":"2026-06-23T02:27:40.833Z","meta":{"total":3,"page":0,"numPages":1},"content":{"query":"when is my thesis due","results": "User's thesis is due March 15" }} The JSONL file is the authoritative record. You can also subscribe to events live from your own code: js import { memoryEvents, MEMORY EVENT CHANNEL } from "openclaw-memgpt"; memoryEvents.on MEMORY EVENT CHANNEL, e = { console.log e.kind, e.namespace, e.meta ; } ; Privacy. Your API key is stored in a private file at <OpenClaw state dir /plugins/openclaw-memgpt/api-key mode 0600 , in a 0700 directory . At verbose , the observability log contains your conversation content. If you use backup or file-sync tools Time Machine, iCloud Drive, dotfile managers , consider excluding both of these paths. The agent replies to you conversationally. Unlike MemGPT's reference CLI, which forces every reply through a send message tool call, this plugin lets the agent answer in free-form text. This is purely a user-experience choice and does not affect how memory is stored or recalled. First-run cold start ~60 s . The first time the embedding model is needed it is downloaded and cached. Run openclaw memgpt prewarm or accept the wizard's offer to get this out of the way; every run after that is fast. One-shot Either run --local mode. openclaw memgpt prewarm first, or accept the one-time cold-start cost on the first turn — it works normally afterwards. "The agent doesn't remember things across sessions." Check that your API key is correct — that the configured environment variable is set, or the stored key is valid. Memory operations silently no-op if the sidecar can't reach the LLM. "My first turn times out cold start ." Run openclaw memgpt prewarm to download and cache the embedding model up front, or wait through the first ~60 s. "Embedder cache appears unavailable." Run openclaw memgpt prewarm to re-download the embedding model. This is the first public release of openclaw-memgpt . Bug reports and feedback are very welcome — future versions will incorporate user reports and iterate on the plugin's design. Please open an issue on GitHub https://github.com/xltvy/openclaw-memgpt/issues . Built on the memory architecture from MemGPT , created by the Letta https://github.com/letta-ai/letta team — full credit to the original authors. The plugin consumes a maintained MemGPT fork published as openclaw-memgpt-sidecar https://pypi.org/project/openclaw-memgpt-sidecar/ .