Long-term memory for your agents

One command to install, one config line to activate. The plugin self-installs the bm CLI on first init (one-time ~10s) and works in local mode by default. A single CLI flip switches to true cloud routing through Basic Memory Cloud.

# Install the plugin
hermes plugins install basicmachines-co/basic-memory \
  --path integrations/hermes

# Activate in ~/.hermes/config.yaml
memory:
  provider: basic-memory

# Verify
hermes memory status
#   Provider:  basic-memory
#   Plugin:    installed ✓
#   Status:    available ✓

Ten tools curated from the full Basic Memory MCP surface. The agent gets the operations that matter for long-term memory: search by meaning, read by permalink, capture decisions, navigate the graph, list projects across workspaces. Every read/write tool takes optional project/project_id for cross-project routing without reconfiguring the plugin.

bm_search       semantic + full-text search (call this before answering)
bm_read         fetch a note by title, permalink, or memory:// URL
bm_write        create a new note (decisions, meetings, insights)
bm_edit         append / prepend / find-replace / replace-section
bm_context      navigate via memory:// URLs to find related notes
bm_delete       delete a note
bm_move         move a note to a different folder
bm_recent       notes touched recently ("yesterday", "2 weeks", "7d")
bm_projects     list available projects with their UUIDs
bm_workspaces   list Basic Memory Cloud workspaces

Plus automatic capture:
  per turn       every exchange appends to a session-transcript note
  end of session a separate summary note, linked via summary_of

The bundled skill teaches the agent how to behave. The instruction set is small and opinionated: search before answering, offer to capture, suggest connections, don't over-capture. The full skill ships with the plugin and is loaded into the agent's system prompt automatically.

# Basic Memory Knowledge Graph

You have access to a persistent knowledge graph backed by Basic
Memory. The graph survives across sessions and is shared with
other tools (Claude Desktop, Obsidian, the bm CLI).

## When to use each tool

| Situation                                          | Tool                       |
|----------------------------------------------------|----------------------------|
| User asks about a topic that might be documented   | bm_search → bm_read        |
| User exposes a decision or meeting outcome         | offer to bm_write          |
| Updating prior work                                | bm_edit                    |
| Exploring related concepts                         | bm_context                 |
| "What was I working on yesterday?"                 | bm_recent                  |

## Behavior guidelines

1. Search before answering. If the user asks "what did we decide
   about X?", run bm_search first.
2. Offer to capture. When the user shares decisions or meeting
   outcomes, ask: "Should I save this as a note?"
3. Suggest connections. When a search returns related notes, surface
   them so the user knows what already exists.
4. Don't over-capture. Auto-capture is already running per turn.
5. Sensitive info. Don't capture credentials or personal data
   without confirmation.

The OpenClaw plugin is structured the same way (install via npm, agent tool surface, slash commands) and additionally ships a set of bundled skills. The agent doesn't just have tools. It has named workflows for the recurring memory operations.

memory-defrag             cleanup and reorganization of memory files
memory-ingest             import existing material into Basic Memory
memory-lifecycle          manage note/project lifecycle workflows
memory-literary-analysis  analyze texts and reading notes
memory-metadata-search    query notes by frontmatter fields
memory-notes              guidance for writing well-structured notes
memory-reflect            periodic consolidation into durable memory
memory-research           research synthesis into durable notes
memory-schema             schema lifecycle (infer, create, validate, diff)
memory-tasks              structured task tracking that survives compaction

Here's an OpenClaw agent working through memory-research papers, captured live in Basic Memory Cloud. Each paper gets a structured note (type: research-paper, with summary, problem, method, observations). The sidebar shows the agent's growing corpus organized into folders. Click into any note and you can read, edit, correct, or extend what the agent wrote. The agent owns the writes; you own the file.