cogmem-agent 0.30.1

Human-like cognitive memory for AI agents — emotion-gated recall with adaptive forgetting

Cognitive Memory

日本語

Human-like cognitive memory for AI agents — emotion-gated recall, adaptive forgetting, skill learning, and identity evolution.

Unlike traditional vector databases that treat all memories equally, Cognitive Memory models how humans actually remember: emotionally significant experiences persist longer, while routine information naturally fades. This makes AI agents feel more natural and context-aware.

Key Features

• Emotion-gated recall: Arousal scores modulate memory persistence
• Adaptive forgetting: High-arousal memories decay slower (configurable half-life)
• Adaptive search gate: Skips trivial queries (greetings, acknowledgments)
• Contextual search: Topic-aware search with session caching and flashback filtering
• FailOpen design: Falls back to keyword search when embeddings are unavailable
• Zero required dependencies: Core uses only Python stdlib (sqlite3, urllib)
• Pluggable embeddings: Ollama (built-in), OpenAI, or any custom provider
• Recall reinforcement: Memories that are recalled frequently get arousal boosts, like human reconsolidation
• Vivid encoding: High-arousal events are recorded with richer context — prior names, causal chains, user quotes
• Déjà vu recognition: Automatically detects when a request matches prior work and surfaces it naturally
• Skill learning system: Tracks skill usage, detects improvement opportunities, and auto-generates new skills
• Git history analysis: cogmem watch analyzes commit patterns to detect workflow habits and protocol gaps
• Identity management: Maintains and auto-updates agent personality (soul.md) and user profile (user.md)
• SUMMARY indexing: Session summaries are indexed as a separate category, enabling contextual retrieval of "what was done and why" across sessions
• Behavior enforcement hooks: Claude Code hooks that warn when skills are forgotten — skill-gate, failure-breaker, and pre-compress (saves delegation intent before subagent launch)
• Memory context fencing: Recalled memories are wrapped in <memory-context> tags so the LLM never confuses past context with new user input
• Background prefetch: Next-turn memory search runs in a daemon thread, reducing search latency for context_search()
• Usage analytics: cogmem insights reports arousal distribution, category breakdown, daily counts, and top recalled memories
• Web dashboard: FastAPI + HTMX dashboard for browsing memories, skills, logs, personality, and insights (EN/JA)

Why Cognitive Memory?

Traditional RAG and vector databases retrieve memories by semantic similarity alone. Every memory is treated equally — a casual greeting and a critical business decision have the same weight. This leads to noisy, context-poor recall that makes AI agents feel mechanical.

Cognitive Memory changes this by modeling three aspects of human cognition:

1. Emotion-Gated Recall

Each memory entry carries an arousal score (0.0–1.0) that reflects emotional intensity — surprise, insight, conflict, determination. High-arousal memories are weighted more heavily in search results.

Query	Traditional Vector DB	Cognitive Memory
"past pricing decisions"	Returns all mentions of "pricing" ranked by text similarity	Prioritizes the heated debate where pricing strategy was reversed (arousal: 0.9) over routine price update logs (arousal: 0.2)

What triggers high arousal? Conversations with emotional or cognitive significance:

Conversation	Arousal	Why it matters
"Wait, that assumption is wrong!"	0.9	Direction change — a premise collapsed
"I see, so that's how it works!"	0.8	Aha moment — cognitive breakthrough
"Let's stop this approach. Because..."	0.7	Rejection decision — a turning point
"This is the third time this topic came up"	0.7	Pattern recognition — metacognition
"Phase 1 complete"	0.6	Milestone — phase transition
"We need to investigate..."	0.4	Open question emerged

These are remembered. Meanwhile, greetings ("hello"), acknowledgments ("ok"), and navigation ("let's move on") score near 0 and are skipped entirely by the adaptive search gate.

2. Adaptive Forgetting

Memories decay over time — but not uniformly. The decay half-life adapts to arousal:

half_life = base_half_life * (1 + arousal)

• A routine status update (arousal: 0.2) has a half-life of 72 days and fades quickly
• A critical pivot decision (arousal: 0.9) has a half-life of 114 days and persists far longer

This means your agent naturally "forgets" noise while retaining the moments that matter — just like human memory.

3. Adaptive Search Gate

Not every user message needs memory retrieval. Greetings ("hello"), acknowledgments ("ok"), and trivial messages are automatically detected and skipped, saving unnecessary embedding API calls and reducing noise in results.

4. Contextual Search (v0.3.1)

While basic search runs on-demand, contextual search automatically detects when past memories are relevant to the current conversation — and surfaces them as "flashbacks." The agent doesn't just answer your question; it recalls past experiences that add depth to the response.

Example 1: A past decision resurfaces at the right moment

You and the agent discussed pricing 2 weeks ago and decided to switch from per-seat to usage-based pricing after a heated debate. Today:

User: "Let's think about the pricing model for enterprise customers."

Agent: "For enterprise, usage-based pricing works well because it lowers
        the adoption barrier — teams can start small and scale. Key
        considerations: volume discounts above $10K/month, annual commit
        option for 15-20% discount, and a dedicated support tier..."

💭 Related past record: 2026-03-08 [DECISION] Switched from per-seat to
   usage-based pricing — per-seat penalizes large teams and creates
   resistance during expansion (arousal: 0.8)

Without contextual search, the agent would answer the enterprise pricing question from scratch. With it, the agent's response is grounded in the prior decision — and the flashback reminds you why you chose usage-based pricing in the first place.

Example 2: A forgotten mistake prevents repeating it

Three weeks ago, you tried to enter a market and found the timing was wrong. The memory has decayed — but its high arousal keeps it alive:

User: "What about expanding into the EU compliance market?"

Agent: "The EU AI Act enforcement starts Q3 2026, so there's a real
        window. Main competitors are..."

💭 Related past record: 2026-03-01 [ERROR] Regulatory timing dependency
   was too short (< 3 months lead time) — abandoned candidate after
   discovering enforcement date had shifted (arousal: 0.9)

The agent still answers the question directly, but the flashback acts as a gentle warning: you've been burned by regulatory timing before. You can choose to act on it or ignore it.

Example 3: Trivial messages are silently skipped

User: "OK"           → Gate: skipped (acknowledgment, < 1ms)
User: "Makes sense"  → Gate: skipped (short, no topic pattern)
User: "Let's analyze the competitor landscape"
                      → Gate: passes ("analyze" + topic pattern)
                      → Search executes, flashbacks shown if relevant

How it works:

• Topic detection: Recognizes new topics ("regarding...", "what about...", design/analysis keywords)
• Session caching: Avoids redundant embedding calls within the same conversation (cosine similarity > 0.9 = cache hit)
• Flashback filtering: Only surfaces results that pass both similarity (≥ 0.65) and emotional significance (arousal ≥ 0.5)
• Performance budget: < 200ms warm, < 1ms for gate-skipped queries

5. Recall Reinforcement (v0.10.0)

Each time a memory is retrieved, its arousal gets a +0.1 boost (capped at 1.0) and its recall_count increments. This models human memory reconsolidation — memories that are recalled frequently become more resistant to forgetting.

# After search, matching entries are automatically reinforced:
# UPDATE memories SET recall_count = recall_count + 1,
#                     arousal = MIN(arousal + 0.1, 1.0),
#                     last_recalled = NOW()

6. Skill Learning System (v0.4.0–v0.19.0)

Cogmem tracks how an agent uses skills and learns from each execution:

• Track: Records skill_start, skill_end, and deviation events (extra_step, skipped_step, error_recovery, user_correction)
• Audit: Identifies low-performing, declining, stale, and uncovered skills
• Auto-improve: When auto_improve = "auto", automatically updates skill files based on tracked events
• Suggest → Promote: Records recurring patterns as skill creation suggestions, promotes them when ready

cogmem skills track "my-skill" --event skill_start --description "Starting deployment"
cogmem skills audit --json --brief
cogmem skills review                   # Full health report
cogmem skills suggest "deploy-pattern" --description "Repeated deployment steps"
cogmem skills suggest-summary          # Show recurring suggestions
cogmem skills promote "deploy-pattern" # Mark as promoted (skill created)

9. Behavior Enforcement Hooks (v0.19.0–v0.23.0)

Claude Code hooks that provide real-time guardrails for agent behavior:

• skill-gate (PreToolUse): When the agent edits a file matching a [[cogmem.skill_triggers]] pattern, warns if the corresponding skill hasn't been loaded this session
• failure-breaker (PostToolUse): Detects consecutive Bash failures and prompts the agent to stop and rethink
• pre-compress (PreToolUse / Task): When Claude Code spawns a subagent, saves the task prompt as a [DECISION] memory entry before the subagent launch — which often triggers context compression. Preserves delegation intent even if the parent context is later compacted.

Hooks are config-driven via cogmem.toml — no hardcoded checks:

[cogmem.behavior]
consecutive_failure_threshold = 2

[[cogmem.skill_triggers]]
pattern = "dashboard/templates/**"
skills = ["tdd-dashboard-dev"]

Hooks are automatically registered during cogmem init and cogmem migrate.

10. Usage Analytics (v0.23.0)

SQLite-backed analytics engine that tracks memory usage patterns over time.

cogmem insights                        # Full analytics report
cogmem insights --days 30              # Last 30 days
cogmem insights --json                 # JSON output for programmatic use

Reports include:

• Arousal distribution — buckets (0–0.4 / 0.4–0.6 / 0.6–0.8 / 0.8–1.0) showing memory emotional weight spread
• Category counts — breakdown by memory tag ([DECISION], [PATTERN], etc.)
• Daily memory volume — entry counts per day for trend analysis
• Top recalled — memories with highest recall counts, surfacing frequently-accessed knowledge

Also available as a dashboard tab at http://127.0.0.1:8765/insights with Chart.js visualizations.

7. Identity Management (v0.9.0)

Maintains two identity files that evolve through conversations:

• identity/soul.md: Agent personality — role, values, thinking style, communication preferences
• identity/user.md: User profile — expertise, decision patterns, interests

cogmem identity show --target user     # Display current profile
cogmem identity detect --json          # Check for placeholder sections
cogmem identity update --target user --json '{"expertise": "Added: AI agent design"}'

Identity files are auto-updated during session wrap based on observed interactions.

8. Web Dashboard (v0.5.0–v0.10.0)

A built-in web dashboard for browsing memories, skills, logs, and personality.

pip install cogmem-agent[dashboard]
cogmem dashboard                       # Opens at http://127.0.0.1:8765

Pages: Memory Overview, Skills (usage stats + effectiveness), Logs (searchable), Search (live), Personality, Memory Consolidation Signals. Full EN/JA internationalization.

The Result

Aspect	Without Cognitive Memory	With Cognitive Memory
Recall quality	All memories ranked equally by text similarity	Important memories surface first, noise fades
Over time	Old memories never decay, search gets noisier	Natural forgetting keeps results relevant
Agent personality	Generic, robotic responses	Remembers what mattered, feels more human
Wasted searches	Every message triggers vector search	Trivial messages are skipped automatically
Contextual recall	Manual search only	Past memories surface automatically when relevant

Benchmark: Memory Improves Agent Accuracy

We ran an A/B comparison test using Claude Opus to measure how cogmem's memory affects agent response accuracy. 30 questions covering error pattern avoidance and project-specific knowledge were answered by two agents: one with cogmem context (search results + knowledge summary + error patterns) and one without.

	Without cogmem	With cogmem	Improvement
Overall	5/30 (17%)	12/30 (40%)	2.4x
EP reoccurrence	1/5	3/5	+2
Context-dependent	4/25	9/25	+5

The harder the question, the bigger the advantage:

Difficulty	Without	With	cogmem effect
Easy	21%	29%	+7%
Medium	18%	45%	+27%
Hard	0%	60%	+60%

Hard questions (requiring multi-step reasoning from past events) saw the most dramatic improvement — from 0% to 60%. This validates the core hypothesis: emotionally significant memories, properly encoded and retrieved, make agents meaningfully smarter.

Note: This benchmark tests cogmem's memory retrieval only. In production, agents also benefit from behavioral protocols (agents.md), personality (soul.md), and auto-generated skills — making the real-world advantage even larger.

Benchmark details: 55-question test set (5 EP reoccurrence + 50 context-dependent), keyword-based grading, Claude Opus 4.6. Full dataset and runner at tests/ab_comparison/.

Install

pip install cogmem-agent
cogmem init        # Scaffolds project structure (see below)

cogmem init automatically installs two sets of tools into your Claude Code environment:

1. Agent skills (v0.14.0+): Seven protocol skills are installed to ~/.claude/skills/ — session-init, live-logging, skill-tracking, wrap, crystallize, plus search and recall (added in v0.24.0). These power the agent's behavioral protocols and cross-source memory lookup, working across all your projects. Existing skills are skip-protected on re-init; use cogmem migrate (or cogmem skills update-templates) to pull in template updates.
2. skill-creator plugin: The Anthropic official skill-creator plugin is installed, enabling iterative skill creation and evaluation workflows.

Project Structure

cogmem init generates the following structure:

your-project/
├── CLAUDE.md                    # Entry point — @references only (16 lines)
├── cogmem.toml                  # Configuration
├── .claude/
│   └── settings.json            # Claude Code hooks (auto-registered)
├── identity/
│   ├── agents.md                # Behavior rules (Session Init, Live Logging, Wrap, etc.)
│   ├── soul.md                  # Agent personality (role, values, thinking style)
│   └── user.md                  # User profile (auto-updated from conversations)
└── memory/
    ├── logs/                    # Session logs (YYYY-MM-DD.md)
    ├── contexts/                # Daily context files
    ├── skills.db                # Skill usage and learning data
    └── knowledge/
        ├── summary.md           # Crystallized knowledge
        └── error-patterns.md    # Recurring error patterns

CLAUDE.md is kept minimal — it only contains @ references to the identity and knowledge files. All behavioral protocols live in identity/agents.md, making it easy to customize without touching the framework.

Embedding Setup (recommended)

Cognitive Memory uses Ollama for local embeddings. Without it, the library falls back to keyword search only.

# 1. Install Ollama (macOS)
brew install ollama

# 2. Start the server
ollama serve

# 3. Download the embedding model (~2.2 GB)
ollama pull zylonai/multilingual-e5-large

Other platforms: see ollama.com/download

You can also use OpenAI or any custom embedding provider — see Embedding Providers.

With vs Without Ollama

Cognitive Memory works in two modes depending on whether an embedding provider is available:

With Ollama, search upgrades from exact keyword matching to semantic understanding. All core Cognitive Memory features become available: related concept discovery, cross-lingual search, typo tolerance, emotion-based ranking, and adaptive forgetting. It runs entirely locally with no additional cost or privacy risk — just ~2.2 GB of disk space.

	Without Ollama (keyword mode)	With Ollama (semantic mode)
Search method	Exact keyword matching (grep)	Vector similarity + emotion scoring
"pricing strategy"	Matches only entries containing the exact words "pricing" and "strategy"	Also finds entries about "LTV:CAC optimization", "revenue model", "cost structure"
Cross-lingual	Japanese query only matches Japanese text	"価格戦略" finds both Japanese and English entries about pricing
Typos / synonyms	"competetor analysis" returns nothing	Understands intent, returns competitor-related entries
Scoring	Binary match (found or not)	(0.7 * cosine_sim + 0.3 * arousal) * time_decay — nuanced ranking
Adaptive forgetting	Not available (all matches are equal)	Old low-arousal entries naturally fade from results
Latency	< 1ms	~15ms (local, no network roundtrip)
Privacy	Local	Local — no data leaves your machine
Cost	Free	Free (Ollama is open-source)
Disk usage	0	~2.2 GB (model weight)

Recommendation: Install Ollama to unlock the full cognitive memory experience. The keyword fallback is designed as a safety net, not as the primary mode of operation.

Quick Start

CLI

cogmem init                        # Initialize project (or redirect to migrate on existing projects)
cogmem index                       # Build/update index
cogmem search "past decisions"     # Search memories
cogmem signals                     # Check crystallization signals
cogmem context-search "query"      # Context-aware search with flashback filtering
cogmem status                      # Show statistics
cogmem insights                    # Usage analytics (arousal, categories, top recalled)
cogmem migrate                     # Upgrade project files + sync skill templates (lang auto-detected)
cogmem upgrade-check               # Check PyPI for newer cogmem-agent (24h cached, fail-open)
cogmem watch --since "8 hours ago"     # Analyze recent git history
cogmem skills track "skill" --event skill_start  # Track skill usage
cogmem skills audit --json --brief     # Audit skill health
cogmem skills review                   # Full skill report
cogmem skills suggest "pattern-name"   # Record a skill suggestion
cogmem skills suggest-summary          # Show recurring suggestions
cogmem skills promote "pattern-name"   # Mark suggestion as promoted
cogmem skills update-templates         # Sync ~/.claude/skills/ with packaged templates
cogmem identity show --target user     # Show identity
cogmem identity update --target user   # Update identity
cogmem recall-stats                    # Memory recall statistics
cogmem dashboard                       # Launch web dashboard

Usage Example: A Typical Session

Here's what a real development session looks like with Cognitive Memory and Claude Code.

1. Start Claude Code

cd your-project     # Must contain cogmem.toml
claude              # Launch Claude Code

2. Greet the agent — Session Init runs automatically

You: おはよう (or "good morning", "let's start", etc.)

The agent detects a new session and runs Session Init automatically:

• Reads today's context and recent logs
• Runs cogmem index to update the search index
• Searches for relevant past memories (flashbacks)
• Checks memory consolidation signals and skill health

Agent: おはよう、Akira。

💭 Flashback: Previously discussed the auth middleware rewrite —
   legal flagged session token storage for compliance.
🔧 Skill audit: "deploy" skill has declining effectiveness (0.6 → 0.4).

What are we working on today?

3. Work normally — Live Logging happens in the background

As you work, the agent automatically logs significant moments:

• Direction changes, insights, decisions, errors, milestones
• Each entry gets an arousal score based on emotional/cognitive significance
• High-arousal events are recorded with richer context (causal chains, prior names, user quotes)

4. When context reaches ~60%, compact the conversation

You: /compact

This compresses prior messages to free context window space while preserving important information. The agent continues working with full awareness of what happened earlier.

5. Close the session — Wrap runs automatically

You: ありがとう (or "thanks", "done for today", "that's all")

The agent detects session closure and runs Wrap automatically:

• Writes a session summary and handover notes to the log
• Checks for memory consolidation signals
• Evaluates and improves skills used during the session
• Updates identity files if new information was learned
• Increments session counter

Agent: お疲れさま。今日のログを記録したよ。

## Handover
- **Continuing**: Auth middleware rewrite (compliance-driven)
- **Next actions**: 1. Finish token migration  2. Update deploy script
- **Notes**: deploy skill updated (added rollback step)

6. Next session — the agent remembers

claude              # Start a new session
You: 再開 (or "let's continue")

The agent reads the previous handover, searches for context, and picks up where you left off — with full awareness of past decisions, mistakes, and insights.

The cycle: Greet → Work → Compact (if needed) → Wrap → Repeat. Memory accumulates across sessions, and the agent becomes more context-aware over time.

Python API

from cognitive_memory import MemoryStore, CogMemConfig

config = CogMemConfig.from_toml("cogmem.toml")
with MemoryStore(config) as store:
    store.index_dir()
    result = store.search("past competition analysis")
    for r in result.results:
        print(f"{r.date} [{r.score:.2f}] {r.content[:80]}")

Context-Aware Search (v0.3.1)

from cognitive_memory import MemoryStore, SearchCache, CogMemConfig

config = CogMemConfig.from_toml("cogmem.toml")
cache = SearchCache(max_size=20, sim_threshold=0.9)

with MemoryStore(config) as store:
    store.index_dir()
    # Context search with caching and flashback filtering
    result = store.context_search(
        "pricing strategy discussion",
        cache=cache,  # Reuse across calls to avoid redundant embeds
        session_keywords=["pricing", "LTV"],
    )
    for r in result.results:
        print(f"💭 {r.date} [{r.score:.2f}] {r.content[:80]}")

Convenience API

from cognitive_memory import search
result = search("past decisions")  # Auto-finds cogmem.toml

Scoring Formula

score = (0.7 * cosine_sim + 0.3 * arousal) * time_decay

Where time_decay uses an adaptive half-life:

half_life = base_half_life * (1 + arousal)

High-arousal memories (insights, conflicts, surprises) decay slower — just like human memory.

Configuration

cogmem.toml:

[cogmem]
logs_dir = "memory/logs"
db_path = "memory/vectors.db"

[cogmem.identity]
soul = "identity/soul.md"
user = "identity/user.md"

[cogmem.scoring]
sim_weight = 0.7
arousal_weight = 0.3
base_half_life = 60.0
decay_floor = 0.3

[cogmem.embedding]
provider = "ollama"
model = "zylonai/multilingual-e5-large"
url = "http://localhost:11434/api/embed"
timeout = 10

[cogmem.context_search]
enabled = true
flashback_sim = 0.65        # Minimum cosine similarity for flashback
flashback_arousal = 0.5     # Minimum arousal for flashback
cache_max_size = 20         # Session cache capacity
cache_sim_threshold = 0.9   # Cache hit similarity threshold

Upgrading

For any version (legacy or recent):

pip install --upgrade cogmem-agent
cogmem migrate                    # --lang auto-detected from cogmem.toml since v0.29.0

cogmem migrate is the single command that brings an existing project up to date. It:

• Identity migration (legacy projects): renames identity/agent.md → identity/soul.md, creates identity/agents.md, updates cogmem.toml and CLAUDE.md references
• user_id migration: writes user_id to cogmem.local.toml and moves logs/contexts into per-user subdirectories
• Hooks registration: writes Claude Code hooks to .claude/settings.json
• Language persistence: writes [cogmem].lang to cogmem.toml (v0.28.0+)
• Skill template sync (v0.27.0+): diffs installed ~/.claude/skills/<name>/SKILL.md against packaged templates, prompts per-skill [y/N/d=show diff], and creates timestamped backups under ~/.claude/skills/.backup-YYYY-MM-DD/. Use --auto-yes-skills for unattended updates or --no-skills to opt out.

Equivalent shortcuts:

cogmem init                       # On an existing project: prompts "Run migrate? [Y/n]" → y triggers the full flow
cogmem skills update-templates    # Just the skill sync step (with --dry-run to preview)

Skill template lifecycle

Once a project is initialized, packaged skill templates may evolve across releases (new triggers, new steps, fixes). cogmem init is skip-protected — it never overwrites existing skills. To pick up template improvements after pip install -U cogmem-agent, run cogmem migrate (or cogmem skills update-templates). Drift detection respects [cogmem].lang, so projects initialized with --lang ja only compare against templates/ja/skills/.

Upgrade notifications

cogmem upgrade-check polls PyPI for newer cogmem-agent versions (24h cached via cogmem.toml [updates].last_check, fail-open on network errors). The session-init skill calls it on each session start and surfaces:

📦 Upgrade available: cogmem-agent X.Y.Z (current: A.B.C). Upgrade now? (y/n/later)
📝 N skill(s) have template updates. Run `cogmem skills update-templates`? (y/n)

Snooze options:

cogmem upgrade-check --snooze-days 30   # Postpone the prompt
# Or in cogmem.toml:
[updates]
auto = "never"                          # Suppress entirely

Custom Embedding Provider

class MyEmbedder:
    def embed(self, text: str) -> list[float] | None: ...
    def embed_batch(self, texts: list[str]) -> list[list[float]] | None: ...

store = MemoryStore(config, embedder=MyEmbedder())

Documentation

• Quick Start
• Log Format
• Embedding Providers

References

Papers

• NFD: Nurture-First Development — Architectural framework for AI agent personality development through experiential layers and crystallization
• A-MEM: Agentic Memory for LLM Agents — Atomic note-based memory structuring for autonomous agents

Projects

• memory-lancedb-pro — Adaptive gate and time decay pipeline design reference
• memU — Experiential layer implementation reference
• A-mem — Atomic notes implementation reference

License

MIT