Integration
Long-term memory for gptme
gptme is the terminal AI agent: Python, scriptable, and free of UI flash. The Engram plugin is a native gptme plugin. Run `pip install gptme-engram`, set `ENGRAM_API_KEY`, and gptme discovers the six memory tools through its standard entry-point mechanism. There's no MCP bridge and no TOML config to edit.
Install
Three steps: sign up for an Engram API key, paste a BYOK LLM-provider key on /models, then drop the snippet below into gptme.
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.
engram-gptme: terminal AI agent
Routes through mcp-remote (stdio→SSE bridge) since Engram's MCP endpoint is SSE-only and gptme defaults to Streamable HTTP. Source: github.com/lumetra-io/engram-gptme.
- Export your API key:
- Add this block to
~/.config/gptme/config.toml: - Run
gptme --tools mcp "list available MCP tools and exit". The six Engram tools should appear in the agent's tool palette.
export ENGRAM_API_KEY="<api-key>"[mcp]
enabled = true
[[mcp.servers]]
name = "engram"
enabled = true
command = "npx"
args = ["-y", "mcp-remote", "https://mcp.lumetra.io/mcp/sse", "--transport", "sse-only", "--header", "Authorization:Bearer ${ENGRAM_API_KEY}"]What you can do once memory's wired in
- Add a memory layer to gptme without touching its core config
- Pin a default bucket per project via `gptme-engram`'s env-var config so each repo gets its own memory
- Chain gptme tasks across days, so the agent remembers what the previous run accomplished
- Self-host Engram and point gptme at it via `ENGRAM_API_BASE` for a fully local stack
FAQ
Is this still the `mcp-remote` bridge setup from before?
No. `gptme-engram` is a native plugin now: pure pip install, with no `npx mcp-remote` and no TOML edits. The MCP-bridge approach is deprecated for gptme specifically.
Where does it look for the API key?
`ENGRAM_API_KEY` in the environment is the canonical source. If you installed gptme via pipx, use `pipx inject gptme gptme-engram` so the plugin lives in the same environment as the CLI.
Can I override the API base URL?
Yes. `ENGRAM_API_BASE=https://engram.internal.example.com` points the plugin at a self-hosted Engram instance. Same six tools, same agent surface, different backend.
Related integrations
Ship durable memory in gptme today
Free tier: 10K memories and 50K retrievals per month. No credit card. Same Engram backend powers all 41 integrations, so memories you write from one client are immediately queryable from the rest.