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Persisent Mind

**Persistent, searchable, context-aware memory for AI agents.

Rating: 4.7 (242 reviews)
Downloads: 1,928 downloads
Version: 1.0.0

Overview

**Persistent, searchable, context-aware memory for AI agents.

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PersistentMind

Persistent, searchable, context-aware memory for AI agents. Store what matters. Never lose context again.

Free and open-source (MIT License) • Zero dependencies • Works locally • No API keys required

Why This Skill?

AI agents forget everything between sessions. Every time you start a new conversation, you repeat the same context: your preferences, your project setup, corrections to previous mistakes, procedures you've documented. This skill solves that permanently.

Problems it solves:

Agents forget user preferences between sessions
Same mistakes repeated because corrections aren't persisted
Project context has to be re-explained every time
No way to build up a team knowledge base over time

Core Concepts

Memory Types

Type	Use For	Example
fact	Factual information	"Database is PostgreSQL 16"
preference	User preferences	"User prefers concise responses"
procedure	How-to steps	"Run migrations with: poetry run alembic upgrade head"
correction	Mistakes + fixes	"Never use wildcard imports — CI will fail"
context	Background info	"This is a B2B SaaS product for HR teams"
relationship	How things relate	"AuthService depends on UserRepository"
reminder	Notes for later	"Check with team before changing DB schema"

Memory Scopes

Scope	Persists	Use For
global	Always	Cross-project preferences, universal rules
project	Within project	Project-specific facts, procedures, corrections
session	Current session only	Temporary working notes

Features

1. Store Memories

python

from persistentmind import PersistentMind, MemoryType, MemoryScope

mm = PersistentMind(project="my-app")

# Critical correction — will always surface first in context
mm.remember(
    "Never use wildcard imports — the linter will fail CI",
    memory_type=MemoryType.CORRECTION,
    scope=MemoryScope.PROJECT,
    importance=10.0,
    tags=["linting", "ci", "imports"]
)

# Global preference — applies everywhere
mm.remember(
    "User prefers code examples over long explanations",
    memory_type=MemoryType.PREFERENCE,
    scope=MemoryScope.GLOBAL,
    importance=8.0
)

# Auto-tags extracted from content automatically if you don't specify
mm.remember(
    "The Stripe API key is in .env as STRIPE_SECRET_KEY",
    memory_type=MemoryType.FACT,
    scope=MemoryScope.PROJECT,
    importance=9.0
)

2. Search Memories

python

# Full-text search with relevance scoring
results = mm.recall("database migrations")
for r in results:
    print(f"[{r.relevance_score:.2f}] [{r.memory.memory_type}] {r.memory.content}")

# Search with filters
results = mm.recall("imports", type_filter="correction", min_importance=7.0)

# Get by type
corrections = mm.recall_by_type(MemoryType.CORRECTION)

# Get by tag
db_memories = mm.recall_by_tag("database")

3. Inject Context Into Prompts

python

# Get a formatted context block to prepend to any prompt
context = mm.get_context(project="my-app", max_tokens_estimate=1500)

prompt = f"""
{context}

---

User request: {user_input}
"""

Output:

text

# Relevant Memory Context

⚠️ [CORRECTION] Never use wildcard imports — the linter will fail CI
⚙️ [PREFERENCE] User prefers code examples over long explanations
📌 [FACT] The Stripe API key is in .env as STRIPE_SECRET_KEY
📋 [PROCEDURE] Run migrations with: poetry run alembic upgrade head

Corrections always surface first. Importance score determines ranking.

4. Memory Management

python

# Update an existing memory
mm.update_memory(memory_id="mem_abc123", importance=9.0, tags=["critical"])

# Archive a memory (soft delete)
mm.forget("mem_abc123")

# Permanently delete
mm.forget("mem_abc123", permanent=True)

# Expire automatically after N days
mm.remember("Temp token: abc...", expires_in_days=7)

5. Deduplication

python

# Find near-duplicate memories (dry run — just report)
groups = mm.consolidate(dry_run=True)
for g in groups:
    print(f"Found {g['count']} similar memories:")
    for m in g['memories']:
        print(f"  - {m['content']}")

# Actually merge them
mm.consolidate(dry_run=False)

6. Team Sharing

python

# Export your memory set
mm.export_memories("team_memories.json")

# Import a colleague's memories
mm.import_memories("team_memories.json")

7. Summary & Stats

python

print(mm.format_summary())

text

🧠 Total Active Memories: 24  |  Archived: 3
   Avg Importance: 7.4/10

📊 BY TYPE
  • correction             4
  • fact                   8
  • preference             5
  • procedure              4
  • context                3

Importance Scoring Guide

Score	Use When
10	Critical — never violate (e.g. security rules, CI requirements)
8-9	Important — strong preference or key fact
5-7	Useful but not critical
1-4	Nice to know, low priority

API Reference

`PersistentMind(storage_path, project, session_id, auto_cleanup_days)`

Initialize. Data stored in .persistentmind/ by default.

`remember(content, memory_type, scope, tags, importance, project, expires_in_days, source)`

Store a new memory. Returns Memory object.

`recall(query, scope_filter, type_filter, project_filter, limit, min_importance)`

Search memories. Returns List[MemorySearchResult] sorted by relevance.

`recall_by_type(memory_type, limit)`

Get all memories of a specific type, sorted by importance.

`recall_by_tag(tag, limit)`

Get all memories with a specific tag.

`get_context(project, max_tokens_estimate)`

Get formatted context block for prompt injection. Corrections surfaced first.

`update_memory(memory_id, content, importance, tags)`

Update an existing memory's fields.

`forget(memory_id, permanent)`

Archive (default) or permanently delete a memory.

`consolidate(dry_run)`

Find near-duplicate memories. Set dry_run=False to merge them.

`get_stats()`

Return memory statistics dictionary.

`format_summary()`

Human-readable memory summary.

`export_memories(output_file, include_archived)`

Export to JSON for backup or team sharing.

`import_memories(input_file, overwrite_duplicates)`

Import from JSON export file.

Privacy & Security

✅ Zero telemetry — No data sent anywhere
✅ Local-only storage — Everything in .persistentmind/ on your machine
✅ No API keys required — Zero credentials needed
✅ No authentication — No accounts or logins
✅ Full transparency — MIT licensed, source code included

Changelog

[1.0.0] - 2026-02-16

✨ Initial release — PersistentMind
✨ 7 memory types: fact, preference, procedure, context, correction, relationship, reminder
✨ 3 scopes: global, project, session
✨ Full-text search with relevance scoring, importance boosting, recency decay
✨ Prompt context injection via get_context()
✨ Automatic tag extraction from content
✨ Memory consolidation for deduplication
✨ Export/import for team sharing
✨ Auto-expiry and stale session cleanup
✨ Zero dependencies, local-only storage, MIT licensed

Last Updated: February 16, 2026 Current Version: 1.0.0 Status: Active & Community-Maintained

Installation

Terminal bash


openclaw install persisent-mind

Copied!

💻Code Examples

📋 [PROCEDURE] Run migrations with: poetry run alembic upgrade head

-procedure-run-migrations-with-poetry-run-alembic-upgrade-head.txt

Corrections always surface first. Importance score determines ranking.

### 4. Memory Management

example.py

from persistentmind import PersistentMind, MemoryType, MemoryScope

mm = PersistentMind(project="my-app")

# Critical correction — will always surface first in context
mm.remember(
    "Never use wildcard imports — the linter will fail CI",
    memory_type=MemoryType.CORRECTION,
    scope=MemoryScope.PROJECT,
    importance=10.0,
    tags=["linting", "ci", "imports"]
)

# Global preference — applies everywhere
mm.remember(
    "User prefers code examples over long explanations",
    memory_type=MemoryType.PREFERENCE,
    scope=MemoryScope.GLOBAL,
    importance=8.0
)

# Auto-tags extracted from content automatically if you don't specify
mm.remember(
    "The Stripe API key is in .env as STRIPE_SECRET_KEY",
    memory_type=MemoryType.FACT,
    scope=MemoryScope.PROJECT,
    importance=9.0
)

example.py

# Full-text search with relevance scoring
results = mm.recall("database migrations")
for r in results:
    print(f"[{r.relevance_score:.2f}] [{r.memory.memory_type}] {r.memory.content}")

# Search with filters
results = mm.recall("imports", type_filter="correction", min_importance=7.0)

# Get by type
corrections = mm.recall_by_type(MemoryType.CORRECTION)

# Get by tag
db_memories = mm.recall_by_tag("database")

example.py

# Get a formatted context block to prepend to any prompt
context = mm.get_context(project="my-app", max_tokens_estimate=1500)

prompt = f"""
{context}

---

User request: {user_input}
"""

example.txt

# Relevant Memory Context

⚠️ [CORRECTION] Never use wildcard imports — the linter will fail CI
⚙️ [PREFERENCE] User prefers code examples over long explanations
📌 [FACT] The Stripe API key is in .env as STRIPE_SECRET_KEY
📋 [PROCEDURE] Run migrations with: poetry run alembic upgrade head

example.py

# Update an existing memory
mm.update_memory(memory_id="mem_abc123", importance=9.0, tags=["critical"])

# Archive a memory (soft delete)
mm.forget("mem_abc123")

# Permanently delete
mm.forget("mem_abc123", permanent=True)

# Expire automatically after N days
mm.remember("Temp token: abc...", expires_in_days=7)

example.py

# Find near-duplicate memories (dry run — just report)
groups = mm.consolidate(dry_run=True)
for g in groups:
    print(f"Found {g['count']} similar memories:")
    for m in g['memories']:
        print(f"  - {m['content']}")

# Actually merge them
mm.consolidate(dry_run=False)

example.py

# Export your memory set
mm.export_memories("team_memories.json")

# Import a colleague's memories
mm.import_memories("team_memories.json")

example.txt

🧠 Total Active Memories: 24  |  Archived: 3
   Avg Importance: 7.4/10

📊 BY TYPE
  • correction             4
  • fact                   8
  • preference             5
  • procedure              4
  • context                3