Agent Memory Tools

Searches, stores, and manages agent memory across 4 sources (fact store, vector embeddings, BM25, knowledge graph). Runs 100% local via Ollama — no API keys, no cloud dependency. Use when searching workspace knowledge, extracting facts from text, detecting contradictions, auto-ingesting file changes, or building entity graphs. Triggers on memory recall, fact extraction, knowledge search, workspace indexing.

Multi-source memory recall and fact management. Runs locally via Ollama (0€).

Architecture

text

Question → unified_recall.py → fan-out 4 sources → merge → score → rerank → answer
                                 ├─ Fact store (Convex or local JSON)
                                 ├─ Vector embeddings (nomic)
                                 ├─ BM25 full-text (QMD)
                                 └─ Knowledge graph (JSON)

File changed → auto_ingest.py → extract facts → contradiction check → store
                               → update embeddings → rebuild graph

Setup

bash

# Install Ollama models (one-time)
ollama pull gemma3:4b              # LLM (~2s/call)
ollama pull nomic-embed-text-v2-moe  # Embeddings

# Verify everything works
python3 scripts/selftest.py

Requirements: Python 3.9+, Ollama, curl. Optional: QMD CLI (bun install -g qmd).

Core Scripts

Search memory

bash

# Unified recall — recommended (all 4 sources, scored + reranked)
python3 scripts/unified_recall.py "What bugs happened last week?" --debug

# Multi-hop reasoning (chains searches with LLM synthesis)
python3 scripts/multihop_search.py "How does the deploy pipeline work?" --embed

# Temporal decay (recent facts score higher, errors protected)
python3 scripts/decay_search.py "recent issues" --half-life 14

Extract and store facts

bash

# Extract from text
python3 scripts/extract_facts.py "Some conversation or document" --store --debug

# Extract from file
python3 scripts/extract_facts.py --file path/to/doc.md --store

# Pipe from stdin
cat summary.md | python3 scripts/extract_facts.py --store

Facts are checked for contradictions locally (gemma3, ~2s) before storage. Categories: knowledge, error, timeline, preference, tool, client, hr.

Auto-ingest workspace changes

bash

python3 scripts/auto_ingest.py --scan          # One-shot: process modified .md files
python3 scripts/auto_ingest.py --watch          # Daemon: poll for changes every 30s
python3 scripts/auto_ingest.py --file doc.md    # Single file

Dedup by content hash + 5 min cooldown. Triggers: fact extraction → storage → embed cache update → graph rebuild.

Build knowledge graph

bash

python3 scripts/knowledge_graph.py              # Full rebuild
python3 scripts/knowledge_graph.py --dry-run    # Preview without writing

Graph stored at .cache/knowledge-graph.json. Auto-rebuilt incrementally by auto_ingest.py.

Run tests

bash

python3 scripts/tests.py    # 28 unit tests

Configuration

Edit scripts/config.json. See references/configuration.md for full guide.

Storage backend — auto-detected:

• convexUrl set → uses Convex (agentMemory API)
• No convexUrl → uses local .cache/agent-facts.json

Model presets — switch LLM/embeddings provider in one flag:

bash

python3 scripts/unified_recall.py "query" --preset ollama      # Default
python3 scripts/unified_recall.py "query" --preset lmstudio
python3 scripts/unified_recall.py "query" --preset openai

Per-script model override — in config.json → scriptOverrides:

json

"scriptOverrides": {
  "recall":  { "llm": { "model": "gemma3:4b", "apiFormat": "ollama" } },
  "extract": { "llm": { "model": "gemma3:4b", "apiFormat": "ollama" } }
}

Recommended models by RAM:

RAM	LLM	Embeddings
4 GB	gemma3:1b	nomic-embed-text
8 GB	gemma3:4b ✓	nomic-embed-text-v2-moe
16+ GB	qwen3.5:27b	nomic-embed-text-v2-moe

⚠ Avoid Qwen 3.5 for JSON tasks — outputs to "thinking" field instead of response.

Platform auto-trigger

Platform	Method
macOS	LaunchAgent with WatchPaths
Linux	systemd timer or cron
Windows	Task Scheduler

See references/configuration.md for examples.

File Structure

text

scripts/
├── unified_recall.py      # Multi-source search + scoring + synthesis
├── extract_facts.py       # Fact extraction + contradiction check + storage
├── auto_ingest.py         # File watcher / scanner pipeline
├── multihop_search.py     # Chained reasoning search
├── decay_search.py        # Time-weighted search
├── knowledge_graph.py     # Entity/relationship graph builder
├── fact_store.py          # Storage abstraction (Convex / local JSON)
├── llm_client.py          # LLM/embedding client (Ollama/LM Studio/OpenAI)
├── selftest.py            # Setup validation
├── tests.py               # Unit tests (28)
└── config.json            # Configuration + presets
references/
└── configuration.md       # Full configuration guide