Capture decisions, docs, runbooks, prior fixes, and corrections once. Reuse them across people, tools, projects, and future agents.
one memory layer · shared across every assistant and teammate
Context that survives sessions, search that understands intent, and a graph that explains blast radius — so every assistant shares the same memory and your team ships without repeating context.
Capture decisions, docs, tasks, and lessons so every AI conversation starts informed instead of blind.
Find code by meaning, not exact wording. Combine semantic and precise modes depending on what you need.
See dependencies and impact paths before changing critical modules, routes, or shared services.
Persistent memory and decision capture across tools, teammates, and long-running projects — captured from real sessions, not written after the fact.
“Don’t do that again” and “why we chose this” stop living in one chat. Capture them once and reuse them everywhere.
A connected map of your project’s memory: which decisions touch which files, and what depends on the module you’re about to change. Graph queries for dependencies, impact, call paths, and dead-code discovery.
Graph queries for dependencies, impact, call paths, and dead-code discovery across the codebase.
Trace impact paths before changing critical modules, routes, or shared services — and ship without costly regressions.
Decisions, lessons, and sessions connect to the files they touch, so context and code stay in sync.
Sold on the memory layer? Connect one repo and capture your first decision in the next five minutes — free to start.
Get Started →Intent-aware retrieval for code, notes, docs, and event history. Search for “authentication handler” and find login, OAuth, JWT, and sessions — even if those exact words aren’t in your query.
Vector-based search that understands meaning and intent. Find code by what it does, not what it’s named.
Combines semantic understanding with keyword precision — best when you know some terms but not the exact names.
Exact text matching for when you know the specific symbol, variable, or string you’re looking for.
Regex-based search for specific code patterns, function signatures, or structural matches.
Scoped search, relevance ranking, and multiple output formats are covered in semantic search.
Scoped, revocable bundles of project memory for onboarding, handoffs, and client work — package exactly what the next person or agent should see, and nothing more.
Bundle the decisions, docs, and prior fixes that matter for a task, project, or client — ready to share.
Shared context can expire or be revoked. Handing off a capsule isn’t handing over your whole workspace.
Keep each client’s context cleanly separated, with redactions applied before anything leaves the workspace.
Shared memory, docs, plans, transcripts, and graph context for multi-person workflows — built on tenant isolation, encrypted storage, and enterprise-ready controls as you scale.
One teammate’s correction helps everyone’s next session. Project memory stops living in one person’s head.
Memory is tied to a workspace, project, or repo. Client and team contexts stay separate unless you share them.
Tenant isolation, encrypted storage, and controls over what gets captured and which surfaces an agent can read.
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, or read how the MCP server keeps memory portable.
Every session is captured and searchable. Recall what you decided, what an agent corrected, and what’s still open — then hand it to the next person or agent with the context attached.
Find the run where a decision was made or a bug was fixed, and reuse it instead of re-deriving it.
The next session loads prior decisions, guardrails, and fixes before it acts — no re-explaining your own repo.
Move work between teammates or agents with the decisions, docs, and history attached as a scoped capsule.
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.
Capture decisions, docs, runbooks, prior fixes, and corrections once. Reuse them across future agents, tools, and teammates.