memory

name	memory
description	Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.
triggers	["remember","search past","recall","what was done","previous session","save context","remember that","memory","ChromaDB","vector memory","persistent memory"]
negatives	["git history","file system","file notes","backup","database","memory admin (use chromadb)","backup memory (use chromadb)"]
license	MIT
compatibility	opencode
metadata	{"workflow":"productivity","audience":"developers","version":"1.0.0","author":"shokunin"}
allowed-tools	Read Bash Write

Memory

Persistent memory across sessions using ChromaDB vector search. Every conversation is stored and retrievable.

How It Works

The memory system uses ChromaDB (local vector database, no server needed) to store conversation context as embeddings. When you start a new session, the agent searches past memory for relevant context.

• Storage: ~/.shokunin/memory/chroma_db/ (ChromaDB persistent files)
• Sessions: ~/.shokunin/memory/sessions/ (markdown summaries per session)
• MCP Server: ~/.shokunin/memory/mcp-server.py

Workflow

Step 1: Start memory server (if not running)

The memory MCP server is configured in opencode.json. It starts automatically when OpenCode connects to it.

Step 2: Save context during session

At the end of each significant task, save context:

store_context with:
  text: "Summary of what was done, key decisions, code patterns"
  tags: ["project-name", "feature", "language"]
  project: "project-name"
  session_id: "current-session-id"

Step 3: Search past memory at session start

When starting a new session, search for relevant context:

search_context with:
  query: "what we discussed about auth"
  project: "current-project"

Step 4: Get full session summary

get_session_summary with:
  session_id: "session-id"

Automatic Session Save

The agent should automatically:

1. At the end of the session, save a summary of key decisions and context
2. At the start of a new session, search for relevant past context
3. Present relevant past context to the user naturally

Error Handling

Error	Cause	Fix
ChromaDB not found	Not installed	pip install chromadb
Collection not found	First run	Creates automatically on first store
Slow first query	Downloading ONNX model	First run downloads ~79MB. Subsequent runs are instant.
Memory not returning results	No data stored yet	Normal on first use. Start by saving something.
Embedding model fails to download	Network blocked or proxy required	Set CHROMADB_EMBEDDING_MODEL env var. Fall back to all-MiniLM-L6-v2 which is smaller (~23MB).
Duplicate or stale entries flooding results	Auto-save firing too frequently during rapid iteration	Apply 5-second debounce before storing context. Consolidate entries with memory_consolidate_memories periodically.
Collection corrupted after crash	Write interrupted during power loss or force-quit	Restore from latest backup in ~/.shokunin/memory/backups/. Run memory-healthcheck.ps1 to verify integrity.
Search returns irrelevant results for short queries	Under-2-word queries have poor vector signal	Use 3+ word queries. Include project name, topic, and a descriptive verb phrase.
Session ID collision or reuse	Wrapper failed to generate unique ID, or manual copy-paste	Check ~/.shokunin/current-session.json. Run python chroma-helper.py session list to verify uniqueness.

Commands

/remember [text]     → Save an important piece of context
/search [query]      → Search past memory
/whatdidwe [topic]   → What did we discuss about X before?
/forget [id]         → Remove a specific memory entry

Anti-Patterns

Pattern	Problem	Fix
Storing secrets or tokens in memory entries	Plaintext memory is readable by any process with filesystem access	Never store API keys, passwords, or tokens. Use environment variables or a dedicated secret manager.
Saving every single message instead of checkpoints	Floods the vector DB with near-duplicate embeddings, degrading search quality	Save only decisions, file changes, and commands. Debounce rapid successive saves to 5 seconds.
Using memory as a key-value store	ChromaDB is optimized for semantic similarity, not exact-match lookups	Use the file system or a config file for deterministic data. Reserve memory for fuzzy, contextual recall.
Searching with overly broad queries like "what did we do"	Returns too many low-relevance results, wasting tokens and attention	Narrow the query: project name + topic + timeframe. Example: "auth refactoring decisions May 2026".
Not consolidating old sessions	Vector DB grows unbounded, slowing queries and increasing disk usage	Run memory_consolidate_memories weekly. Archive sessions older than 90 days.
Assuming memory entries are immutable truth	Entries represent frozen-in-time claims. Code may have changed since then.	Always verify file paths and function names before acting. Use verify_file_path or grep to confirm.
Skipping session search at conversation start	Loses all context from prior work, forcing the agent to re-discover already-solved problems	Mandatory step: always run session list and search before responding to any task.

Advanced ChromaDB Patterns

Collection Management

# List collections
collections = client.list_collections()
for c in collections: print(c.name, c.count())

# Create collection with custom embedding function
from chromadb.utils import embedding_functions
ef = embedding_functions.OpenAIEmbeddingFunction(api_key="...", model_name="text-embedding-3-small")
collection = client.create_collection(name="custom", embedding_function=ef)

# Delete by metadata filter
collection.delete(where={"project": "test-project"})

# Peek at stored data
sample = collection.peek(limit=5)

Embedding Model Selection

Model	Dimensions	Speed	Accuracy
all-MiniLM-L6-v2 (default/ONNX)	384	Fastest	Good for short text
text-embedding-3-small (OpenAI)	1536	API-dependent	Excellent
text-embedding-3-large (OpenAI)	3072	API-dependent	Best (expensive)

Default ONNX model (all-MiniLM-L6-v2) runs locally, no API key needed. Downloads once (~79MB).

Performance Tuning

# Batch inserts (100x faster than single inserts)
batch_size = 100
for i in range(0, len(documents), batch_size):
    collection.add(
        documents=documents[i:i+batch_size],
        metadatas=metadatas[i:i+batch_size],
        ids=ids[i:i+batch_size]
    )

# Limit query results
results = collection.query(query_texts=["query"], n_results=10)

# Get with limit (prevents OOM)
all_data = collection.get(limit=500, include=["documents", "metadatas"])

Storage Monitoring

import os

db_path = "~/.shokunin/memory/chroma_db"
size = sum(os.path.getsize(os.path.join(root, f)) for root, _, files in os.walk(db_path) for f in files)
print(f"Database size: {size / 1024 / 1024:.1f} MB")
print(f"Collection count: {collection.count()}")

Related Scripts

• ~/.shokunin/scripts/seed-memory.ps1 — Imports session markdown files into ChromaDB for first-time population
• ~/.shokunin/scripts/memory-healthcheck.ps1 — Verifies ChromaDB collection integrity and reports health status
• ~/.shokunin/scripts/read-transcript.py — Parses MCP session transcripts and extracts structured decisions and sections

Checklist

• Session ID is unique before storing (check with session list)
• Entry text is descriptive: what, why, and result (not just what)
• Type matches VALID_TYPES in MCP server config
• Tags align with previous entries for consistent search
• Session_end saved at end of every session

Sources

• ChromaDB documentation (docs.trychroma.com) — vector database setup, embedding models, and query best practices
• OpenAI text-embedding-ada-002 documentation (platform.openai.com/docs/guides/embeddings) — production embedding model benchmarks
• MCP Protocol specification (modelcontextprotocol.io) — agent-to-tool communication protocol
• Sentence Transformers all-MiniLM-L6-v2 (sbert.net/docs/pretrained_models.html) — lightweight local embedding alternative
• ONNX Runtime documentation (onnxruntime.ai) — cross-platform model inference for local embeddings
• "Designing Data-Intensive Applications" by Martin Kleppmann (O'Reilly, 2017) — durability, replication, and consistency patterns relevant to local DB design