Integration

Long-term memory for aichat

aichat is sigoden's all-in-one Rust LLM CLI, but its tool layer doesn't read MCP servers from its own YAML — tools (including MCP) are loaded through the companion repo `sigoden/llm-functions`, which ships an `mcp-bridge` that aichat queries over HTTP. The bridge speaks stdio MCP only, so Engram's hosted SSE endpoint is wired in via `mcp-remote`. After `argc mcp start && argc build`, six `engram_`-prefixed tools are available to every aichat session.

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Install

Three steps: sign up for an Engram API key, paste a BYOK LLM-provider key on /models, then drop the snippet below into aichat.

Three steps to memory in your agent

Sign up. Free, no card. You'll land on a Getting Started page that walks the next two steps.
Add your LLM key. Engram is BYOK. Paste an OpenAI / Anthropic / Groq / Together / Fireworks key and we'll route every extraction and query call through your provider. You pay your provider directly. We never see your inference.
Paste the snippet below into your agent and restart it. Use Authorization: Bearer <api-key>with the API key from your portal.

sigoden's Rust LLM CLI via the llm-functions bridge

aichat doesn't read MCP directly — tools route through the companion sigoden/llm-functions repo's stdio mcp-bridge. Engram's hosted SSE endpoint is wired in via mcp-remote, exposing six engram_-prefixed tools to every aichat session.

Clone the bridge repo and symlink it into aichat:

Terminal

git clone https://github.com/sigoden/llm-functions ~/llm-functions
cd ~/llm-functions
argc link-to-aichat

Add Engram to ~/llm-functions/mcp.json:

~/llm-functions/mcp.json

{
  "mcpServers": {
    "engram": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.lumetra.io/mcp/sse",
        "--transport",
        "sse-only",
        "--header",
        "Authorization:Bearer <api-key>"
      ]
    }
  }
}

Start the bridge and regenerate tool definitions:

Terminal

argc mcp start && argc build

Use Engram from any aichat session: aichat --role %functions% "engram_store_memory: ...". Drop the engram_ prefix by setting "prefix": false on the server entry in mcp.json.

What you can do once memory's wired in

`aichat --role %functions% "engram_store_memory: remember I prefer pytest over unittest"`: one-shot store from any shell
Chat with `aichat -r %functions%` and let the model decide when to call `engram_query_memory` mid-conversation
Pipe shell output straight into memory: `cat decisions.md | aichat --role %functions% "engram_store_memory: $(cat)"`
Cross-check memories landed via the REST API at `api.lumetra.io` (the MCP bridge hits a different host than the REST endpoint)

FAQ

Why the two-repo setup (aichat + llm-functions)?

aichat itself doesn't read MCP servers. Tools, including MCP, are routed through the sibling `sigoden/llm-functions` repo, which ships an `mcp-bridge` that aichat queries on `localhost:8808`. `argc link-to-aichat` symlinks `llm-functions` into aichat's `functions_dir`; `argc mcp start` launches the bridge; `argc build` regenerates `functions.json`.

Why is the tool prefix `engram_` and can I drop it?

The `mcp-bridge` prefixes tool names by server name unless you set `"prefix": false` on the server entry. We keep the prefix by default so Engram tools don't collide with other MCP servers you may have wired into the same bridge. To drop it, add `"prefix": false` to the `engram` entry in `mcp.json`.

Anything special for Anthropic users?

Yes. aichat 0.30.0 doesn't ship a `claude-sonnet-4-5` model entry and Anthropic requires explicit `max_tokens`. Both need to be declared in `~/.config/aichat/config.yaml` (see `config.example.yaml` in the recipe). Without it, the very first call errors with `max_tokens: Field required`.

Related integrations

Ship durable memory in aichat today

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