ContextStream turns repo decisions, guardrails, prior fixes, runbooks, and agent corrections into shared project memory your next AI coding session can use before it touches the repo.
start with one repo · capture real decisions · let the next session pick them up
Full 500-run result: matches Zep, beats supermemory.
Same agent and repo, memory connected: 23/24 vs 14/24.
Passed when the needed runbook or decision was not in code.
When an engineer corrects an agent, explains a repo convention, fixes a bug, or writes a runbook, that context usually stays trapped in a chat, doc, or person’s head. The next AI coding session starts from zero.
“Don’t do that again” lives in one chat session. The next agent makes the same mistake on the same files.
Why you chose this auth flow or that queue is written down somewhere — just not where an agent will look before acting.
Each run re-derives repo conventions, re-reads the codebase, and re-asks questions your project already answered.
ContextStream keeps project memory alive across people, tools, sessions, and AI agents.
Save decisions, lessons, runbooks, docs, tasks, sessions, and corrections from real work.
Keep context tied to the right project, repo, workspace, team, or client.
Surface the right memory before an agent acts — not after it repeats a mistake.
Every correction, fix, and decision makes the next session better than the last.
One memory layer, three ways in: a stream of what your project learned, a map of how it connects, and portable bundles for handing context to the next person or agent.
A live ledger of decisions, lessons, fixes, and corrections — captured while the work happens, reviewable in seconds.
Read more →A connected map of your project’s memory: which decisions touch which files, and what a change is about to break.
Read more →Scoped, revocable bundles of project memory for onboarding, handoffs, and client work — open by link, expire on schedule.
Read more →ContextStream ships as an MCP server, so the same project memory follows you across tools — switch agents without losing what your project knows.
“After a mid-day context wipe, the plan, runbook, decisions, and snapshots let me reconstruct exact state without re-deriving anything. The final epic ticket took minutes because everything already existed as referenceable IDs instead of tribal knowledge.”
Start with a small workspace. Capture a few real decisions, runbooks, and agent corrections, then test whether the next AI coding session starts with the right context.
For individual developers. Persistent project memory across every AI tool you use.
Full graphs + Enhanced Context. The deepest recall ContextStream offers.
Shared project wisdom. Scoped governance and higher concurrency for teams that move.
Capture decisions, guardrails, prior fixes, runbooks, and agent corrections once. Reuse them across future sessions, agents, and teammates.