Brief every agent yourself
Best when handoffs are occasional. You control exactly what the agent hears, with no new setup.
Check how often you repeat the same decisions, constraints, and stopping points as sessions multiply.
Project context for coding agents
You can give three agents the same repository and still explain the same project three times: what the team decided, which path was rejected, what must stay stable, and where yesterday’s work stopped.
There are several reasonable ways to handle that. Compare them here, then decide when ContextStream is worth adding to your workflow.

The underlying job
A project also has operating judgment: why a design changed, which customer constraint governs a change, what failed last week, and which work is still open. Some belongs in the repository. Some lives in a tool’s memory, a handoff, or a developer’s head.
When a new agent starts, the useful question is: how does it get the right current information, with enough evidence for you to check it?
A decision filter
Ask these questions before you add anything.
If your existing notes answer these questions, keep them. If task routing is the gap, focus on orchestration. If project judgment repeatedly gets lost between supported agents while code and notes need to stay inspectable, test a shared context layer.
Where ContextStream fits
ContextStream keeps project context outside any one agent session. The assistant resolves the authorized workspace and project, searches eligible indexed source for paths and lines, and can inspect code relationships where the index supports them. Separately, it can retrieve decisions and their reasons, preferences, constraints, lessons, and handoffs.
The agent uses that material to propose a next step. You check the evidence and decide what to do.
This does not make agents infallible or automatically aligned. Each connected agent must read the relevant context, and useful discoveries must be captured and kept current. ContextStream supplies context for the developer workflow; it does not build or validate the application the agents are coding.
A fair next step
Choose a repository you are permitted to index and a small task with a real prior decision or constraint. First, let an agent work with the notes and source inspection you already rely on. Then try the same task with ContextStream in a supported client.
Compare each answer with the current repository and actual decision. Note setup effort, missing or stale context, corrections, and your own briefing burden. A single run shows what happened on that task; it does not prove a general time saving or a better application outcome.
Before you connect
ContextStream search is hosted. Eligible file contents are sent for indexing even if the MCP helper runs locally. Review repository policy, exclusions, indexed files, and transcript-capture settings before ingest. If upload is not allowed, do not index that repository.
Read privacy and indexing →Setup connects a supported client, account, workspace, project, and index. You also need to decide what deserves to persist and correct it when it changes. Test the actual setup route and time it.
Read the setup guide →There is no guarantee. Client integration, retrieval, index freshness, graph coverage, and model behavior vary. Inspect returned sources and current files before approving a consequential change.
Explore platform details →Make the choice on a real project
If your notes or native memory already carry a handoff cleanly, keep them. If repeated briefings continue across agents, compare ContextStream on one safe, source-checkable task.
THE CONTEXT PLATFORM BEHIND YOUR AGENTS
ContextStream brings your project’s goals, code, decisions, and
hard earned lessons into the AI tools you already use so your agents stay
aligned as the work evolves, with fewer costly detours along the way.
curl -fsSL https://contextstream.io/scripts/mcp.sh | bash
Our June 14, 2026 LongMemEval-S comparison: 90.0% vs SuperMemory’s cited 85.4% result.