Avalon Memory Crystal Server (amcs)

A Go MCP server for capturing and retrieving thoughts, memory, and project context. Exposes tools over Streamable HTTP, backed by Postgres with pgvector for semantic search.

What it does

• Capture thoughts with automatic embedding and metadata extraction
• Search thoughts semantically via vector similarity
• Organise thoughts into projects and retrieve full project context
• Summarise and recall memory across topics and time windows
• Link related thoughts and traverse relationships

Stack

• Go — MCP server over Streamable HTTP
• Postgres + pgvector — storage and vector search
• LiteLLM — primary hosted AI provider (embeddings + metadata extraction)
• OpenRouter — default upstream behind LiteLLM
• Ollama — supported local or self-hosted OpenAI-compatible provider

Tools

Tool	Purpose
capture_thought	Store a thought with embedding and metadata
search_thoughts	Semantic similarity search
list_thoughts	Filter thoughts by type, topic, person, date
thought_stats	Counts and top topics/people
get_thought	Retrieve a thought by ID
update_thought	Patch content or metadata
delete_thought	Hard delete
archive_thought	Soft delete
create_project	Register a named project
list_projects	List projects with thought counts
get_project_context	Recent + semantic context for a project
set_active_project	Set session project scope
get_active_project	Get current session project
summarize_thoughts	LLM prose summary over a filtered set
recall_context	Semantic + recency context block for injection
link_thoughts	Create a typed relationship between thoughts
related_thoughts	Explicit links + semantic neighbours
save_file	Store a base64-encoded image, document, audio file, or other binary and optionally link it to a thought
load_file	Retrieve a stored file by ID as base64 plus metadata
list_files	Browse stored files by thought, project, or kind
backfill_embeddings	Generate missing embeddings for stored thoughts
reparse_thought_metadata	Re-extract and normalize metadata for stored thoughts

Configuration

Config is YAML-driven. Copy configs/config.example.yaml and set:

• database.url — Postgres connection string
• auth.mode — api_keys or oauth_client_credentials
• auth.keys — API keys for MCP access via x-brain-key or Authorization: Bearer <key> when auth.mode=api_keys
• auth.oauth.clients — client registry when auth.mode=oauth_client_credentials

OAuth Client Credentials flow (auth.mode=oauth_client_credentials):

1. Obtain a token — POST /oauth/token (public, no auth required):

   POST /oauth/token
   Content-Type: application/x-www-form-urlencoded
   Authorization: Basic base64(client_id:client_secret)
   
   grant_type=client_credentials
   

   Returns: {"access_token": "...", "token_type": "bearer", "expires_in": 3600}

2. Use the token on the MCP endpoint:

   Authorization: Bearer <access_token>
   

Alternatively, pass client_id and client_secret as body parameters instead of Authorization: Basic. Direct Authorization: Basic credential validation on the MCP endpoint is also supported as a fallback (no token required).

• ai.litellm.base_url and ai.litellm.api_key — LiteLLM proxy
• ai.ollama.base_url and ai.ollama.api_key — Ollama local or remote server

See llm/plan.md for full architecture and implementation plan.

Backfill

Run backfill_embeddings after switching embedding models or importing thoughts without vectors.

{
  "project": "optional-project-name",
  "limit": 100,
  "include_archived": false,
  "older_than_days": 0,
  "dry_run": false
}

• dry_run: true — report counts without calling the embedding provider
• limit — max thoughts per call (default 100)
• Embeddings are generated in parallel (4 workers) and upserted; one failure does not abort the run

Metadata Reparse

Run reparse_thought_metadata to fix stale or inconsistent metadata by re-extracting it from thought content.

{
  "project": "optional-project-name",
  "limit": 100,
  "include_archived": false,
  "older_than_days": 0,
  "dry_run": false
}

• dry_run: true scans only and does not call metadata extraction or write updates
• If extraction fails for a thought, existing metadata is normalized and written only if it changes
• Metadata reparse runs in parallel (4 workers); one failure does not abort the run

File Storage

Use save_file to persist binary files as base64. Files can optionally be linked to a memory by passing thought_id, which also adds an attachment reference to that thought's metadata.

{
  "name": "meeting-notes.pdf",
  "media_type": "application/pdf",
  "kind": "document",
  "thought_id": "optional-thought-uuid",
  "content_base64": "<base64-payload>"
}

Load a stored file again with:

{
  "id": "stored-file-uuid"
}

List files for a thought or project with:

{
  "thought_id": "optional-thought-uuid",
  "project": "optional-project-name",
  "kind": "optional-image-document-audio-file",
  "limit": 20
}

Automatic backfill (optional, config-gated):

backfill:
  enabled: true
  run_on_startup: true   # run once on server start
  interval: "15m"        # repeat every 15 minutes
  batch_size: 20
  max_per_run: 100
  include_archived: false

Search fallback: when no embeddings exist for the active model in scope, search_thoughts, recall_context, get_project_context, summarize_thoughts, and related_thoughts automatically fall back to Postgres full-text search so results are never silently empty.

Client Setup

Claude Code

# API key auth
claude mcp add --transport http amcs http://localhost:8080/mcp --header "x-brain-key: <key>"

# Bearer token auth
claude mcp add --transport http amcs http://localhost:8080/mcp --header "Authorization: Bearer <token>"

OpenAI Codex

Add to ~/.codex/config.toml:

[[mcp_servers]]
name = "amcs"
url  = "http://localhost:8080/mcp"

[mcp_servers.headers]
x-brain-key = "<key>"

OpenCode

# API key auth
opencode mcp add --name amcs --type remote --url http://localhost:8080/mcp --header "x-brain-key=<key>"

# Bearer token auth
opencode mcp add --name amcs --type remote --url http://localhost:8080/mcp --header "Authorization=Bearer <token>"

Or add directly to opencode.json / ~/.config/opencode/config.json:

{
  "mcp": {
    "amcs": {
      "type": "remote",
      "url": "http://localhost:8080/mcp",
      "headers": {
        "x-brain-key": "<key>"
      }
    }
  }
}

Development

Run the SQL migrations against a local database with:

DATABASE_URL=postgres://... make migrate

LLM integration instructions are served at /llm.

Containers

The repo now includes a Dockerfile and Compose files for running the app with Postgres + pgvector.

1. Set a real LiteLLM key in your shell: export AMCS_LITELLM_API_KEY=your-key
2. Start the stack with your runtime: docker compose -f docker-compose.yml -f docker-compose.docker.yml up --build podman compose -f docker-compose.yml up --build
3. Call the service on http://localhost:8080

Notes:

• The app uses configs/docker.yaml inside the container.
• The local ./configs directory is mounted into /app/configs, so config edits apply without rebuilding the image.
• AMCS_LITELLM_BASE_URL overrides the LiteLLM endpoint, so you can retarget it without editing YAML.
• AMCS_OLLAMA_BASE_URL overrides the Ollama endpoint for local or remote servers.
• The Compose stack uses a default bridge network named amcs.
• The base Compose file uses host.containers.internal, which is Podman-friendly.
• The Docker override file adds host-gateway aliases so Docker can resolve the same host endpoint.
• Database migrations 001 through 005 run automatically when the Postgres volume is created for the first time.
• migrations/006_rls_and_grants.sql is intentionally skipped during container bootstrap because it contains deployment-specific grants for a role named amcs_user.

Ollama

Set ai.provider: "ollama" to use a local or self-hosted Ollama server through its OpenAI-compatible API.

Example:

ai:
  provider: "ollama"
  embeddings:
    model: "nomic-embed-text"
    dimensions: 768
  metadata:
    model: "llama3.2"
    temperature: 0.1
  ollama:
    base_url: "http://localhost:11434/v1"
    api_key: "ollama"
    request_headers: {}

Notes:

• For remote Ollama servers, point ai.ollama.base_url at the remote /v1 endpoint.
• The client always sends Bearer auth; Ollama ignores it locally, so api_key: "ollama" is a safe default.
• ai.embeddings.dimensions must match the embedding model you actually use, or startup will fail the database vector-dimension check.