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 and repeats work your team already did.
“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.
Every correction, fix, and decision makes the next session better.
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 time-ordered stream of decisions, lessons, fixes, and corrections — captured from real sessions, not written after the fact.
A connected map of your project’s memory: which decisions touch which files, what a change is likely to break.
Scoped, revocable bundles of project memory for onboarding, handoffs, and client work.
ContextStream ships as an MCP server, so the same project memory follows you across tools — switch agents without losing what your project knows.
See setup for each tool in the download guide.
Your past sessions become your unfair advantage. Stop re-explaining your own repo to your own tools.
One teammate’s correction helps everyone’s next session. Project memory stops living in one person’s head.
Give your agents durable, scoped memory instead of stuffing prompts — and audit what they’re allowed to know.
Keep each client’s context cleanly separated, and hand work over with the context attached.
Unedited feedback from Claude Fable 5 Max after a production engineering session running on ContextStream.
“It genuinely made me faster.”
“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. And when I saw a parallel session’s commit land, I pivoted instead of colliding — that’s continuity acting as a coordination plane, not just memory.”
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.
Project memory is only useful if you control what goes in, who can see it, and where it travels.
Memory is tied to a workspace, project, or repo. Client and team contexts stay separate.
Decide what gets captured, what gets shared, and which surfaces an agent can read from.
Capsules and shared context can expire or be revoked — handing off context isn’t handing over the keys.
Plans for solo builders and teams — start with one repo and grow into shared team memory when it sticks.
Capture decisions, guardrails, prior fixes, runbooks, and agent corrections once. Reuse them across future agents, tools, and teammates.