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
`penfield_store`	Save a memory	User shares preferences, you make a discovery, a decision is made, you learn something worth keeping
`penfield_recall`	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
`penfield_connect`	Link two memories	New info relates to existing knowledge, building understanding over time
`penfield_disconnect`	Remove link between memories	Relationship was created in error or is no longer valid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
`penfield_save_context`	Checkpoint a session	Ending substantive work, preparing for handoff to another agent
`penfield_restore_context`	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
`penfield_save_artifact`	Store a file	Saving diagrams, notes, code, reference docs
`penfield_retrieve_artifact`	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`penfield_delete_artifact`	Remove a file	Cleaning up outdated artifacts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
`penfield_awaken`	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:

"User likes Python"

Good — specific, contextual, findable:

"[Preferences] User prefers Python over JavaScript for backend work.
Reason: frustrated by JS callback patterns and lack of type safety.
Values type hints and explicit error handling. Uses FastAPI for APIs."

What makes a memory findable:

Context prefix in brackets:

[Preferences]

[Project: API Redesign]

[Investigation: Payment Bug]

[Decision]

The "why" behind the "what" — rationale matters more than the fact itself
Specific details — names, numbers, dates, versions, not vague summaries
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
`fact`	Verified, durable information	"User's company runs Kubernetes on AWS EKS"
`insight`	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	bm25_weight	vector_weight	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
```
importance_threshold: 0.7
```
to skip noise
```
enable_graph_expansion: true
```
to follow connections (default, usually leave on)

Workflows

User shares a preference

penfield_store({
  content: "[Preferences] User wants responses under 3 paragraphs unless complexity demands more. Dislikes bullet points in casual conversation.",
  memory_type: "fact",
  importance: 0.8,
  tags: ["preferences", "communication"]
})

Investigation tracking

// Start
penfield_store({
  content: "[Investigation: Deployment Failures] Reports of 500 errors after every Friday deploy. Checking release pipeline, config drift, and traffic patterns.",
  memory_type: "task",
  importance: 0.7,
  tags: ["investigation", "deployment"]
})
// Discovery — connect to the investigation
discovery = penfield_store({
content: "[Investigation: Deployment Failures] INSIGHT: Friday deploys coincide with weekly batch job at 17:00 UTC. Both compete for DB connection pool. Not a deploy issue — it's resource contention.",
memory_type: "insight",
importance: 0.9,
tags: ["investigation", "deployment", "root-cause"]
})
penfield_connect({
from_memory_id: discovery.id,
to_memory_id: initial_report.id,
relationship_type: "responds_to"
})
// Correction — supersede wrong assumption
correction = penfield_store({
content: "[Investigation: Deployment Failures] CORRECTION: Not a CI/CD problem. Friday batch job + deploy = connection pool exhaustion. Fix: stagger batch job to 03:00 UTC.",
memory_type: "correction",
importance: 0.9,
tags: ["investigation", "deployment", "correction"]
})
penfield_connect({
from_memory_id: correction.id,
to_memory_id: initial_report.id,
relationship_type: "supersedes"
})

Session handoff

penfield_save_context({
  name: "deployment-investigation-2026-02",
  description: "Investigated deployment timeout issues. memory_id: " + discovery.id,
  memory_ids: [discovery.id, correction.id, initial_report.id]
})

Next session or different agent:

penfield_restore_context({
  name: "deployment-investigation-2026-02"
})

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)

Also Available Outside OpenClaw

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

Claude Connectors

Name: Penfield
Remote MCP server URL: https://mcp.penfield.app

Claude Code

Claude mcp add --transport http --scope user penfield https://mcp.penfield.app

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

{
  "mcpServers": {
    "penfield": {
      "command": "npx",
      "args": [
        "mcp-remote@latest",
        "https://mcp.penfield.app/"
      ]
    }
  }
}

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.

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Penfield Memory

Tools

Memory

Knowledge Graph

Context & Analysis

Artifacts

Personality

Writing Memories That Actually Work

Memory Types

Importance Scores

Connecting Memories

Relationship Types (24)

Recall Strategy

Workflows

User shares a preference

Investigation tracking

Session handoff

What NOT to Store

Tags

Also Available Outside OpenClaw

Links

Quick Start

Manual Installation

TEAR & SHARE

Tags

Multi-Agent Patterns

Memory Systems Design

Context Degradation Detection

Context Optimization

Tool Design for Agents

Channels

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