Helix — Plugin & Extension Architecture

Status: Draft v1 · Last updated: 2026-06-18 · Related: TSD · MCP Integration · Decisions

Helix is pluggable by construction: every component that could plausibly need swapping — the embedder, the vector/graph store, the extractor, the LLM router, the redactor, the agent connector, the sync backend — sits behind an interface and is discovered via entry points. The default configuration runs local-only and $0; every paid or networked component is opt-in.

Design authority: ADR-030 (plugin architecture) and ADR-018 (store interface). See Decisions.

1. Extension Points

                     ┌──────────────────────────────────────────────┐
   agent text  ──▶   │  AgentConnector   (Claude Code / Cursor / …) │
                     └───────────────┬──────────────────────────────┘
                                     ▼
                     ┌──────────────────────────────────────────────┐
                     │  Extractor   → memories (+ supersede hints)  │
                     │  Redactor    → strip secrets/PII             │
                     │  LLMRouter    → Provider (gemini/openai/…)    │
                     └───────────────┬──────────────────────────────┘
                                     ▼
                     ┌──────────────────────────────────────────────┐
                     │  Embedder   → vectors  (fastembed / gemini)  │
                     ├──────────────────────────────────────────────┤
                     │  VectorStore  +  GraphStore                  │
                     │  SQLite → Lance → pgvector → Qdrant          │
                     └───────────────┬──────────────────────────────┘
                                     ▼
                     ┌──────────────────────────────────────────────┐
                     │  SyncBackend  (optional, opt-in export/pull) │
                     └──────────────────────────────────────────────┘

Interface	Responsibility	Default impl	Optional impls
`Embedder`	text → vector	`FastEmbedEmbedder` (local)	`GeminiEmbedder`, `OpenAIEmbedder`
`VectorStore`	persist + ANN search	`SQLiteStore`	`LanceStore`, `PgvectorStore`, `QdrantStore`
`GraphStore`	nodes + typed edges	`SQLiteGraphStore`	`PgGraphStore`
`Extractor`	turn turns into memories; emit supersede hints	`HeuristicExtractor`	`LLMExtractor`
`LLMRouter` / `Provider`	route extraction/judging calls to a model	`NullRouter` (heuristics only)	`GeminiProvider`, `OpenAIProvider`, `OllamaProvider`
`Redactor`	strip secrets/PII before persist & log	`RegexRedactor`	`LLMRedactor`
`AgentConnector`	speak a client's config dialect / protocol	`ClaudeCodeConnector`	Cursor / Windsurf / VS Code / Gemini / Zed / Codex / `OpenAIRouterConnector`
`SyncBackend`	export/import "strands" to remote	`NoopSync`	`S3Sync`, `HelixCloudSync`

Every interface is small, synchronous-or-async-agnostic, and versioned (HELIX_PLUGIN_API = "1").

2. Registration & Discovery (entry points)

Plugins are ordinary Python (or Node) packages that advertise themselves via entry points. Helix scans the named groups at daemon startup, builds a registry, and selects an implementation by config key.

Python (pyproject.toml)

[project.entry-points."helix.embedders"]
gemini = "helix_gemini.embed:GeminiEmbedder"

[project.entry-points."helix.stores"]
qdrant = "helix_qdrant.store:QdrantStore"

[project.entry-points."helix.extractors"]
llm = "helix_llm_extract:LLMExtractor"

Entry-point groups

Group	Selected by
`helix.embedders`	`embedder = "..."`
`helix.stores`	`store = "..."`
`helix.graph_stores`	`graph_store = "..."`
`helix.extractors`	`extractor = "..."`
`helix.providers`	`llm.provider = "..."`
`helix.redactors`	`redactor = "..."`
`helix.connectors`	`connectors = [...]`
`helix.sync`	`sync.backend = "..."`

helix.toml

embedder   = "fastembed"     # default, local, $0
store      = "sqlite"        # default
graph_store = "sqlite"
extractor  = "heuristic"
redactor   = "regex"

[llm]
provider = "null"            # no network calls by default

TS plugins register analogously through package.json helix manifest fields; the Node loader mirrors the Python registry.

3. Store Upgrade Path

The store interface (ADR-018) is the same for all four backends, so upgrading is a migration, not a rewrite. Pick the smallest store that fits the working set.

   SQLite          LanceStore        PgvectorStore        QdrantStore
   (default)   →   (embedded,     →  (shared Postgres, →  (dedicated ANN
   zero-dep        columnar ANN,     team / server)        service, scale-out)
   single file)    bigger local)

Store	When	Tradeoff
`SQLiteStore`	Default. One project, single machine, `$0`.	Brute-force/IVF on small sets; fine to tens of thousands.
`LanceStore`	Larger local corpora, still embedded, no service.	Columnar, fast ANN, bigger disk footprint.
`PgvectorStore`	Team shares a Postgres; SQL ecosystem.	Needs a Postgres; ANN quality depends on index config.
`QdrantStore`	Large-scale / high-QPS ANN, payload filtering.	Runs a dedicated service; ops overhead.

Migration command:

helix store migrate --to qdrant --url http://127.0.0.1:6333
# re-embeds only if the embedder changed; otherwise copies vectors + graph + metadata

Because IDs are human-readable and stable (see API Reference), edges and citations survive migration unchanged.

4. Embedding Providers

Provider	Network	Cost	Notes
`fastembed` (default)	none	`$0`	Local ONNX models; the reason Helix is `$0` by default
`gemini`	yes	metered	Opt-in; needs `GEMINI_API_KEY`
`openai`	yes	metered	Opt-in; needs `OPENAI_API_KEY`

The embedder is recorded in the strand manifest so a corpus is never silently re-embedded with a mismatched model on import.

embedder = "gemini"
[embedder.gemini]
model = "text-embedding-004"
# api key from env GEMINI_API_KEY — never written to config or logs

5. Agent Connectors

Each coding agent reads MCP server config from a different file with a different top-level key. The AgentConnector for each client knows its dialect and can write the right snippet via helix connect <client>.

Client	Config file	Top-level key	Notable cap
Claude Code	`.mcp.json` / `~/.claude.json`	`mcpServers`	~25k-token tool output
Cursor	`.cursor/mcp.json`	`mcpServers`	—
Windsurf	`~/.codeium/windsurf/mcp_config.json`	`mcpServers`	100-tool cap
VS Code	`.vscode/mcp.json`	`servers`	128 tools/request
Gemini CLI	`~/.gemini/settings.json`	`mcpServers`	—
Zed	settings	`context_servers`	—
Codex CLI	`~/.codex/config.toml`	`[mcp_servers.<name>]` (TOML)	—

JSON dialect (Claude Code / Cursor / Windsurf / Gemini)

{
  "mcpServers": {
    "helix": { "command": "helix-mcp", "args": ["--stdio"] }
  }
}

VS Code dialect — same shape, different key:

{ "servers": { "helix": { "command": "helix-mcp", "args": ["--stdio"] } } }

Zed dialect — context_servers:

{ "context_servers": { "helix": { "command": "helix-mcp", "args": ["--stdio"] } } }

Codex CLI dialect — TOML:

[mcp_servers.helix]
command = "helix-mcp"
args    = ["--stdio"]

All of these point at the stdio shim, which proxies to the one local daemon (see API Reference §1).

5.1 OpenAI-compatible "memory router" fallback (non-MCP agents)

Agents that don't speak MCP can still get memory via an OpenAI-compatible memory router: point the agent's base URL at Helix instead of the model provider, and Helix transparently recalls + injects memory before forwarding the completion. This is the Supermemory-style base-URL swap pattern, authenticated with x-api-key. (https://supermemory.ai/docs/memory-router/overview)

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:7878/v1/openai/https://api.openai.com",  # prefix swap
    api_key="sk-...",
    default_headers={"x-api-key": "helix-local"},
)
# requests flow through Helix: recall → inject → forward → extract → persist

A REST/SDK surface in the Mem0 style is also available for scripted use. (https://docs.mem0.ai/open-source/features/rest-api)

6. Worked Example — Writing a Plugin

A minimal third-party embedder.

pyproject.toml

[project]
name = "helix-cohere"
dependencies = ["helix-sdk>=1", "cohere"]

[project.entry-points."helix.embedders"]
cohere = "helix_cohere:CohereEmbedder"

helix_cohere/__init__.py

from helix.plugins import Embedder, register   # stable plugin API (v1)
import cohere, os

class CohereEmbedder(Embedder):
    api_version = "1"                 # must match HELIX_PLUGIN_API
    name = "cohere"
    dim = 1024

    def __init__(self, config: dict):
        self._client = cohere.Client(os.environ["COHERE_API_KEY"])
        self._model = config.get("model", "embed-english-v3.0")

    def embed(self, texts: list[str]) -> list[list[float]]:
        resp = self._client.embed(texts=texts, model=self._model,
                                  input_type="search_document")
        return resp.embeddings

register(CohereEmbedder)              # optional explicit registration

Enable it

embedder = "cohere"
[embedder.cohere]
model = "embed-english-v3.0"

pip install helix-cohere
helix doctor          # validates api_version, dim, env vars, round-trip embed
helix daemon restart

helix doctor rejects plugins whose api_version doesn't match the daemon's HELIX_PLUGIN_API, whose declared dim disagrees with a probe, or that touch the network when telemetry/offline mode forbids it.

7. Stability & Compatibility Policy

Plugin API is versioned (HELIX_PLUGIN_API). Within a major version, interfaces are additive-only: new optional methods get safe defaults; existing signatures never change meaning.
Capability negotiation. A plugin declares api_version; the daemon refuses to load mismatched majors rather than failing mysteriously at runtime.
Defaults stay $0 and local. A plugin that requires the network must declare requires_network = True; in offline/telemetry-off mode the daemon will not load it unless explicitly allowed.
Stores are forward-only. The store interface (ADR-018) gains capabilities additively; migrations are one-way and re-use stable IDs so the graph survives.
Secrets stay in env. Plugins read credentials from environment variables only; config and logs never carry keys (enforced by the Redactor; see Observability and Security Model).

Sources

Supermemory OpenAI-compatible memory router (base-URL swap, x-api-key) — https://supermemory.ai/docs/memory-router/overview
Mem0 open-source REST API — https://docs.mem0.ai/open-source/features/rest-api
MCP client config dialects (mcpServers / servers / context_servers / TOML; tool caps) — https://modelcontextprotocol.io/specification/2025-06-18/basic/transports

See also: API Reference · TSD · MCP Integration · Observability · Security Model · Decisions

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Helix — Plugin & Extension Architecture

1. Extension Points

2. Registration & Discovery (entry points)

3. Store Upgrade Path

4. Embedding Providers

5. Agent Connectors

5.1 OpenAI-compatible "memory router" fallback (non-MCP agents)

6. Worked Example — Writing a Plugin

7. Stability & Compatibility Policy

Sources

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Helix — Plugin & Extension Architecture

1. Extension Points

2. Registration & Discovery (entry points)

3. Store Upgrade Path

4. Embedding Providers

5. Agent Connectors

5.1 OpenAI-compatible "memory router" fallback (non-MCP agents)

6. Worked Example — Writing a Plugin

7. Stability & Compatibility Policy

Sources