The coding agent that remembers your repo.

Most coding agents start every session from zero. memcode keeps a persistent model of your repo in .memcode: the subsystems, what you worked on last week, which approaches failed and why, and the preferences you have corrected it on. The longer you use it, the less you have to explain.

One Go binary, a full terminal UI, and it runs against whatever models you already have: your own API keys, a local endpoint like Ollama, or a hosted memcode account.

memcode terminal UI: the matrix splash and the model picker, from Automatic to any specific model

memcode editing code while tracking a live task plan in the terminal

memcode completing a multi-step plan, 20 of 20 tasks done

Remembers your repo	Version-control-friendly state in `.memcode` that survives sessions and machines. Searchable session history. Repeated failures distill into lessons that resurface when they matter. Honors `MEMCODE.md`, `AGENTS.md`, and `CLAUDE.md` instructions, and compresses oversized ones once instead of re-reading them forever.
Model policy in the client	Automatic mode routes each call by what the turn needs: cheap models for routine work, strong models for planning, review, high-risk changes, and recovery after its own mistakes. Pin any model with `/model`. Failures walk catalog-defined fallback chains. See ROUTING.md.
Bring your own models	Works with no account: point it at any OpenAI-compatible endpoint. Provider-native APIs get full fidelity, the Responses API on api.openai.com, Messages on api.anthropic.com, Gemini and xAI likewise. Standard key env vars are picked up automatically.
Reads the room	Tracks the working mood of the session. When you are correcting it, it stops cutting corners, spends more on the model, and asks before acting. When things are calm it stays out of the way.
A real terminal UI	Multiline editing, slash commands with autocomplete, streaming tool output, interrupt and redirect mid-turn, themes, and a live context meter.
Plans before it builds	`/plan` researches with parallel scouts, drafts, gets a cross-model review, and turns the approved plan into a binding contract for execution.
Delegates and parallelizes	Spawn read-only explorers or full sub-agents, run detached background jobs, and manage them with `/jobs`, `/tail`, `/kill`.
Table stakes, done properly	MCP client, Agent Skills, hooks (HOOKS.md), resident LSP for diagnostics and navigation, a sandboxed shell with a real command classifier, vision and PDF input, prompt caching, and context compaction that respects the model's actual window (COMPACTION.md).
Self-updating	Stages updates in the background and applies them on the next launch. `MEMCODE_AUTO_UPDATE=off` keeps it manual.

Quick install

curl -fsSL https://memcode.ai/install.sh | sh

Or with Go:

go install github.com/memcode-ai/memcode@latest

Or build from source:

git clone https://github.com/memcode-ai/memcode
cd memcode && go build -o memcode .

Then run memcode in a repo.

Use any model

MEMCODE_ENDPOINT_URL=http://localhost:11434/v1 memcode      # Ollama, local
MEMCODE_ENDPOINT_URL=https://api.openai.com/v1 memcode      # your OpenAI key (OPENAI_API_KEY)
MEMCODE_ENDPOINT_URL=https://api.anthropic.com memcode      # your Anthropic key (ANTHROPIC_API_KEY)

OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, XAI_API_KEY, FIREWORKS_API_KEY, and friends are picked up automatically for their hosts. Named endpoints live in config, and /model switches models mid-session.

With a memcode account you get one balance across every vendor, a key vault for BYOK, and hosted web search:

memcode login

Architecture

The CLI is the agent: all model selection, escalation, and recovery run client-side; every backend is a plain serving surface speaking one OpenAI-compatible wire (protocol/PROTOCOL.md). Point memcode at your own API keys, a local endpoint like Ollama, or a hosted memcode.ai account (one balance across vendors, a BYOK vault, and team features).

License

MIT. See LICENSE.

Quick install

Use any model

Architecture

License

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