context and memory 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 →
Same agent, same prompt. graphCTX gives it the repo memory developers otherwise repeat.
[ 01 ] · how it works · architecture
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.
01 CAPTURE
graphCTX reads package scripts, lockfiles, CI, editorconfig, AGENTS.md, and session episodes so the memory base starts from repo evidence, not model guesses.
02 ANCHOR
Facts are valid against commits and branches. When code changes, memory moves with the DAG instead of drifting on wall-clock timestamps.
03 PROMOTE
Session details can become workspace or user memory only after evidence gates. Secrets and low-trust prose stay out of durable context.
04 RANK
A relevance gate scores topic drift, entities, and file scope so the agent gets specific context instead of a noisy memory dump.
05 DELIVER
Returned memory is compact, budgeted, and tagged with source provenance, making it easier for developers to trust and audit what the agent uses.
[ 02 ] · benchmarks · measured, not claimed
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.
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.
10 facts p50 1.04ms · p95 1.37ms
510 facts p50 1.15ms · p95 3.94ms
5,010 facts p50 1.33ms · p95 22.27ms
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.
[ 03 ] · install · three commands
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
01
$ curl -fsSL https://graph.coder.company/install | sh
install the CLI (detects Node / Bun)
02
$ graphctx install claude
wire your agent (claude · cursor · opencode · generic)
03
$ graphctx doctor
verify graphCTX is connected
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