# I accidentally hit SOTA on agentic memory by using AI companions

> Source: <https://graph.coder.company/>
> Published: 2026-06-14 07:03:45+00:00

#
Built to be the best

context layer for AI coding agents.

graphCTX keeps repo knowledge close to the work: commands, conventions, decisions, and hard-won fixes. Developers spend less time re-explaining context and more time shipping, with local memory that is fast, private, and reliable.

`curl -fsSL https://graph.coder.company/install | sh`

[See the benchmarks →](#bench)

user: what's the deploy command?

agent: I don't see one in the repo —
you'll need to tell me.

user: what's the deploy command?

agent: ./scripts/ship.sh --canary --wait
mem:9f3a2c

Same agent, same prompt. graphCTX gives it the repo memory developers otherwise repeat.

## How graphCTX manages memory.

The system is deliberately narrow: capture reliable coding facts, keep them valid as the repo changes, and return only the context the agent can use.

### Extract trusted coding facts

graphCTX reads package scripts, lockfiles, CI, editorconfig, AGENTS.md, and session episodes so the memory base starts from repo evidence, not model guesses.

### Bind memory to git state

Facts are valid against commits and branches. When code changes, memory moves with the DAG instead of drifting on wall-clock timestamps.

### Promote only durable knowledge

Session details can become workspace or user memory only after evidence gates. Secrets and low-trust prose stay out of durable context.

### Choose the smallest useful set

A relevance gate scores topic drift, entities, and file scope so the agent gets specific context instead of a noisy memory dump.

### Attach provenance to every recall

Returned memory is compact, budgeted, and tagged with source provenance, making it easier for developers to trust and audit what the agent uses.

## Benchmarked against Supermemory.

Same coding-fact set, same queries. graphCTX runs locally and answers in
~1ms; a live Supermemory search round-trip measured ~494ms (p50), so
recall stays fast and predictable during development.
Reproducible: `graphctx compare --deep`

.

### Latency stays flat at scale

Per-prompt retrieval p50/p95 as the workspace grows. Indexed lookup plus a bounded semantic re-rank keeps the hot path at ~1ms — a 5,000-fact monorepo retrieves as fast as an empty one.

### Reliable after compaction

Post-compaction solve rate across 14 coding tasks. graphCTX restored the needed repo fact in every run.

// graphctx eval run --arms A,B,C

// scope: this compares local latency + cost on direct coding-fact retrieval for developer workflows. Supermemory targets general/conversational memory with cloud connectors, cross-document reasoning, and neural embeddings that graphCTX doesn't attempt.

## Start using repo memory in 30 seconds

No account. No API key. No cloud setup. Install the CLI, connect your agent, and give every session repo-aware memory.

`curl -fsSL https://graph.coder.company/install | sh`

Prefer npm? npm i -g graphctx

` $ curl -fsSL https://graph.coder.company/install | sh `

install the CLI (detects Node / Bun)

` $ graphctx install claude `

wire your agent (claude · cursor · opencode · generic)

` $ graphctx doctor `

verify graphCTX is connected