Memory Pipeline

Give your AI agent a memory that actually works.

AI agents wake up blank every session. Memory Pipeline fixes that — it extracts what matters from past conversations, connects the dots, and generates a daily briefing so your agent starts each session primed instead of clueless.

What It Does

Component

When it runs

What it does

Extract	Between sessions	Pulls structured facts (decisions, preferences, learnings) from daily notes and transcripts
Link	Between sessions	Builds a knowledge graph — connects related facts, flags contradictions
Brief	Between sessions	Generates a compact BRIEFING.md loaded at session start
Ingest	On demand	Imports external knowledge (ChatGPT exports, etc.) into searchable memory
Performance Hooks	During sessions	Pre-game briefing injection, tool discipline, output compression, after-action review

Why This Is Different

Most "memory" solutions are just vector search over chat logs. This is a cognitive architecture — inspired by how human memory actually works:

• Extraction over accumulation — Instead of dumping everything into a database, it identifies what's worth remembering: decisions, preferences, learnings, commitments. The rest is noise.
• Knowledge graph, not just embeddings — Facts get linked to each other with bidirectional relationships. Your agent doesn't just find similar text — it understands that a decision about your tech stack relates to a project deadline relates to a preference you stated three weeks ago.
• Briefing over retrieval — Rather than hoping the right context gets retrieved at query time, your agent starts every session with a curated cheat sheet. Active projects, recent decisions, personality reminders. Zero cold-start lag.
• No mid-swing coaching — Borrowed from performance psychology. Corrections happen between sessions, not during. The after-action review feeds into the next briefing. The loop is closed — just not mid-execution.

Quick Start

Install

clawdhub install memory-pipeline

Setup

bash skills/memory-pipeline/scripts/setup.sh

The setup script will detect your workspace, check dependencies (Python 3 + any LLM API key), create the memory/ directory, and run the full pipeline.

Requirements

• Python 3
• At least one LLM API key (auto-detected):

- OpenAI (OPENAI_API_KEY or ~/.config/openai/api_key) - Anthropic (ANTHROPIC_API_KEY or ~/.config/anthropic/api_key) - Gemini (GEMINI_API_KEY or ~/.config/gemini/api_key)

Run Manually

# Full pipeline
python3 skills/memory-pipeline/scripts/memory-extract.py
python3 skills/memory-pipeline/scripts/memory-link.py
python3 skills/memory-pipeline/scripts/memory-briefing.py

Automate via Heartbeat

Add to your HEARTBEAT.md for daily automatic runs:

### Daily Memory Pipeline
- **Frequency:** Once per day (morning)
- **Action:** Run the memory pipeline:
  1. `python3 skills/memory-pipeline/scripts/memory-extract.py`
  2. `python3 skills/memory-pipeline/scripts/memory-link.py`
  3. `python3 skills/memory-pipeline/scripts/memory-briefing.py`

Import External Knowledge

Already have years of conversations in ChatGPT? Import them so your agent knows what you know.

ChatGPT Export

# 1. Export from ChatGPT: Settings → Data Controls → Export Data
# 2. Drop the zip in your workspace
# 3. Run:
python3 skills/memory-pipeline/scripts/ingest-chatgpt.py ~/imports/chatgpt-export.zip

# Preview first (recommended):
python3 skills/memory-pipeline/scripts/ingest-chatgpt.py ~/imports/chatgpt-export.zip --dry-run

What it does:

• Parses ChatGPT's conversation tree format


• Filters out throwaway conversations (configurable: --min-turns, --min-length)


• Supports topic exclusion (edit EXCLUDE_PATTERNS to skip unwanted topics)


• Outputs clean, dated markdown files to memory/knowledge/chatgpt/


• Files are automatically indexed by OpenClaw's semantic search

Options:

• --dry-run — Preview without writing files


• --keep-all — Skip all filtering


• --min-turns N — Minimum user messages to keep (default: 2)


• --min-length N — Minimum total characters (default: 200)

Adding Other Sources

The pattern is extensible. Create ingest-.py, parse the format, write markdown to memory/knowledge//. The indexer handles the rest.

How the Pipeline Works

Stage 1: Extract

Script: memory-extract.py

Reads daily notes (memory/YYYY-MM-DD.md) and session transcripts, then uses an LLM to extract structured facts:

{"type": "decision", "content": "Use Rust for the backend", "subject": "Project Architecture", "confidence": 0.9}
{"type": "preference", "content": "Prefers Google Drive over Notion", "subject": "Tools", "confidence": 0.95}

Output: memory/extracted.jsonl

Stage 2: Link

Script: memory-link.py

Takes extracted facts and builds a knowledge graph:

• Generates embeddings for semantic similarity


• Creates bidirectional links between related facts


• Detects contradictions and marks superseded facts


• Auto-generates domain tags

Output: memory/knowledge-graph.json + memory/knowledge-summary.md

Stage 3: Briefing

Script: memory-briefing.py

Generates a compact daily briefing (< 2000 chars) combining:

• Personality traits (from SOUL.md)


• User context (from USER.md)


• Active projects and recent decisions


• Open todos

Output: BRIEFING.md (workspace root)

Performance Hooks (Optional)

Four lifecycle hooks that enforce execution discipline during sessions. Based on a principle from performance psychology: separate preparation from execution.

User Message → Agent Loop
  ├── before_agent_start  →  Briefing packet (memory + checklist)
  ├── before_tool_call    →  Policy enforcement (deny list)
  ├── tool_result_persist →  Output compression (prevent context bloat)
  └── agent_end           →  After-action review (durable notes)

Configuration

{
  "enabled": true,
  "briefing": {
    "maxChars": 6000,
    "checklist": [
      "Restate the task in one sentence.",
      "List constraints and success criteria.",
      "Retrieve only the minimum relevant memory.",
      "Prefer tools over guessing when facts matter."
    ],
    "memoryFiles": ["memory/IDENTITY.md", "memory/PROJECTS.md"]
  },
  "tools": {
    "deny": ["dangerous_tool"],
    "maxToolResultChars": 12000
  },
  "afterAction": {
    "writeMemoryFile": "memory/AFTER_ACTION.md",
    "maxBullets": 8
  }
}

Hook Details

Hook	What it does

before_agent_start	Loads memory files, builds bounded briefing packet, injects into system prompt
before_tool_call	Checks tool against deny list, prevents unsafe calls
tool_result_persist	Head (60%) + tail (30%) compression of large results
agent_end	Appends session summary to memory file with tools used and outcomes

Output Files

File

Location

Purpose

BRIEFING.md	Workspace root	Daily context cheat sheet
extracted.jsonl	memory/	All extracted facts (append-only)
knowledge-graph.json	memory/	Full graph with embeddings and links
knowledge-summary.md	memory/	Human-readable graph summary
knowledge/chatgpt/*.md	memory/	Ingested ChatGPT conversations

Customization

• Change LLM models — Edit model names in each script (supports OpenAI, Anthropic, Gemini)
• Adjust extraction — Modify the extraction prompt in memory-extract.py to focus on different fact types
• Tune link sensitivity — Change the similarity threshold in memory-link.py (default: 0.3)
• Filter ingestion — Edit EXCLUDE_PATTERNS in ingest-chatgpt.py for topic exclusion

Troubleshooting

Problem

Fix

No facts extracted	Check that daily notes or transcripts exist; verify API key
Low-quality links	Add OpenAI key for embedding-based similarity; adjust threshold
Briefing too long	Reduce facts in template or let LLM generation handle it (auto-constrained to 2000 chars)

See Also

• Setup Guide — Detailed installation and configuration