Penfield Memory

Persistent memory that compounds. Your agent remembers conversations, learns preferences, connects ideas, and picks up exactly where it left off—across sessions, days, and channels.

Tools

Memory

Tool	Purpose	When to use
INLINECODE0	Save a memory	User shares preferences, you make a discovery, a decision is made, you learn something worth keeping
INLINECODE1

Hybrid search (BM25 + vector + graph) | Need context before responding, resuming a topic, looking up prior decisions | | penfield_search | Semantic search (higher vector weight) | Fuzzy concept search when you don't have exact terms | | penfield_fetch | Get memory by ID | Following up on a specific memory from recall results | | penfield_update_memory | Edit existing memory | Correcting, adding detail, changing importance or tags |

Knowledge Graph

Tool	Purpose	When to use
INLINECODE5	Link two memories	New info relates to existing knowledge, building understanding over time
INLINECODE6

Remove link between memories | Relationship was created in error or is no longer valid | | penfield_explore | Traverse graph from a memory | Understanding how ideas connect, finding related context |

Context & Analysis

Tool	Purpose	When to use
INLINECODE8	Checkpoint a session	Ending substantive work, preparing for handoff to another agent
INLINECODE9

Resume from checkpoint | Picking up where you or another agent left off | | penfield_list_contexts | List saved checkpoints | Finding previous sessions to resume | | penfield_reflect | Analyze memory patterns | Session start orientation, finding themes, spotting gaps |

Artifacts

Tool	Purpose	When to use
INLINECODE12	Store a file	Saving diagrams, notes, code, reference docs
INLINECODE13

Get a file | Loading previously saved work | | penfield_list_artifacts | List stored files | Browsing saved artifacts | | penfield_delete_artifact | Remove a file | Cleaning up outdated artifacts |

Personality

Tool	Purpose	When to use
INLINECODE16	Load personality config	Session start, identity refresh

Writing Memories That Actually Work

Memory content quality determines whether Penfield is useful or useless. The difference is specificity and context.

Bad — vague, no context, unfindable later:
CODEBLOCK0

Good — specific, contextual, findable:
CODEBLOCK1

What makes a memory findable:

1. 1. Context prefix in brackets: [Preferences], [Project: API Redesign], [Investigation: Payment Bug], INLINECODE20
2. The "why" behind the "what" — rationale matters more than the fact itself
3. Specific details — names, numbers, dates, versions, not vague summaries
4. References to related memories — "This builds on [earlier finding about X]" or "Contradicts previous assumption that Y"

Memory Types

Use the correct type. The system uses these for filtering and analysis.

Type	Use for	Example
INLINECODE21	Verified, durable information	"User's company runs Kubernetes on AWS EKS"
INLINECODE22

Patterns or realizations | "Deployment failures correlate with Friday releases" |
| correction | Fixing prior understanding | "CORRECTION: The timeout isn't Redis — it's a hardcoded batch limit" |
| conversation | Session summaries, notable exchanges | "Discussed migration strategy. User leaning toward incremental approach" |
| reference | Source material, citations | "RFC 8628 defines Device Code Flow for OAuth on input-constrained devices" |
| task | Work items, action items | "TODO: Benchmark recall latency after index rebuild" |
| strategy | Approaches, methods, plans | "For user's codebase: always check types.ts first, it's the source of truth" |
| checkpoint | Milestone states | "Project at 80% — auth complete, UI remaining" |
| identity_core | Immutable identity facts | Set via personality config, rarely stored manually |
| personality_trait | Behavioral patterns | Set via personality config, rarely stored manually |
| relationship | Entity connections | "User works with Chad Schultz on cybersecurity content" |

Importance Scores

Use the full range. Not everything is 0.5.

Score	Meaning	Example
0.9–1.0	Critical — never forget	Architecture decisions, hard-won corrections, core preferences
0.7–0.8

Important — reference often | Project context, key facts about user's work |
| 0.5–0.6 | Normal — useful context | General preferences, session summaries |
| 0.3–0.4 | Minor — background detail | Tangential facts, low-stakes observations |
| 0.1–0.2 | Trivial — probably don't store | If you're questioning whether to store it, don't |

Connecting Memories

Connections are what make Penfield powerful. An isolated memory is just a note. A connected memory is understanding.

After storing a memory, always ask: What does this relate to? Then connect it.

Relationship Types (24)

Knowledge Evolution: supersedes · updates · evolution_of
Use when understanding changes. "We thought X, now we know Y."

Evidence: supports · contradicts · disputes
Use when new information validates or challenges existing beliefs.

Hierarchy: parent_of · child_of · sibling_of · composed_of · part_of
Use for structural relationships. Topics containing subtopics, systems containing components.

Causation: causes · influenced_by · prerequisite_for
Use for cause-and-effect chains and dependencies.

Implementation: implements · documents · tests · example_of
Use when something demonstrates, describes, or validates something else.

Conversation: responds_to · references · inspired_by
Use for attribution and dialogue threads.

Sequence: follows · precedes
Use for ordered steps in a process or timeline.

Dependencies: depends_on
Use when one thing requires another.

Recall Strategy

Good queries find things. Bad queries return noise.

Tune search weights for your query type:

Query type	bm25weight	vectorweight	graph_weight
Exact term lookup ("Twilio auth token")	0.6	0.3	0.1
Concept search ("how we handle errors")

0.2 | 0.6 | 0.2 |
| Connected knowledge ("everything about payments") | 0.2 | 0.3 | 0.5 |
| Default (balanced) | 0.4 | 0.4 | 0.2 |

Filter aggressively:

• - memory_types: ["correction", "insight"] to find discoveries and corrections
• INLINECODE57 to skip noise
• INLINECODE58 to follow connections (default, usually leave on)

Workflows

User shares a preference

CODEBLOCK2

Investigation tracking

CODEBLOCK3

Session handoff

CODEBLOCK4

Next session or different agent:

CODEBLOCK5

What NOT to Store

• - Verbatim conversation transcripts (too verbose, low signal)
• Easily googled facts (use web search instead)
• Ephemeral task state (use working memory)
• Anything the user hasn't consented to store about themselves
• Every minor exchange (be selective — quality over quantity)

Tags

Keep them short, consistent, lowercase. 2–5 per memory.

Good: preferences, architecture, investigation, correction, project-name
Bad: 2026-02-02, important-memory-about-deployment, INLINECODE66

Also Available Outside OpenClaw

The native OpenClaw plugin is the fastest path, but Penfield works with any AI tool anywhere:

Claude Connectors

CODEBLOCK6

Claude Code
CODEBLOCK7

MCP Server — for Gemini CLI, Cursor, Windsurf, Intent, Perplexity Desktop or any MCP-compatible tool:

CODEBLOCK8

API — direct HTTP access at api.penfield.app for custom integrations.

Same memory, same knowledge graph, same account. The plugin is 4-5x faster (no MCP proxy layer), but everything stays in sync regardless of how you connect.

Links

• - Plugin: openclaw-penfield on npm
• Source: github.com/penfieldlabs/openclaw-penfield
• Sign up: portal.penfield.app/sign-up
• Website: penfield.app
• X: @penfieldlabs