tomevault-io 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.
Install
openclaw plugins install openclaw-penfield
openclaw penfield login
openclaw gateway restart
Sign up: https://portal.penfield.app/sign-up
Every Session Starts Here
Always do this first, before anything else:
penfield_awaken()
penfield_reflect({ time_window: "7d" })
awaken loads your identity and personality context. reflect orients you on recent work. Without these, you're starting cold.
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_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Remove a file | Cleaning up outdated artifacts |
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 correctionsimportance_threshold: 0.7to skip noiseenable_graph_expansion: trueto 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({
memory_ids: [discovery.id, correction.id, initial_report.id],
session_id: "deployment-investigation-2026-02"
})
Next session or different agent:
penfield_restore_context({
checkpoint_id: "checkpoint-uuid",
merge_mode: "append"
})
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, UserPreferencesForCommunicationStyle
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.
Links
- Plugin: openclaw-penfield on npm
- Source: github.com/penfieldlabs/openclaw-penfield
- Sign up: portal.penfield.app/sign-up
- Website: penfield.app
- X: @penfieldlabs
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