ContextStream

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Project context for coding agents

More coding agents.
One project.
Who carries the decisions?

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.

Compare the approaches ↓ See what ContextStream indexes →

A shared project carries more than its code.

The underlying job

Code shows what exists.
Decisions explain why.

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?

Your practical choices

Five ways to carry project context

Start with the approach you already use. Each can be the right answer for a different workflow.

01

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.

02

Keep notes and rules in the repo

Best when conventions are stable and your team maintains a versioned decision record, runbook, or agent instruction file.

Check who updates changing decisions, flags old advice, and ensures the next agent reads the relevant page.

03

Use your agent’s built-in memory

Best when most work stays inside one editor or harness, and its memory is useful and inspectable.

Check what it saves, what you can correct, and whether it follows you into the other client you use.

04

Add an orchestrator

Best when assigning work, routing messages, and supervising parallel agents is the main problem.

Check where durable project knowledge lives, who records Agent A’s discovery, and how Agent B knows it is current.

05

Use a shared context layer

Best when repeated work crosses agent, session, or tool boundaries and needs both project knowledge and inspectable code evidence.

Check supported clients, setup, upkeep, source freshness, and whether hosted indexing is allowed.

These approaches can work together. Your repo notes and orchestrator can remain useful alongside a shared context layer.

A decision filter

Which approach fits your project?

Ask these questions before you add anything.

1. **Where does work cross a boundary?**One agent in one tool, or several agents and clients over time?
2. **What keeps going missing?**Stable instructions, current source, a changing decision, a lesson, or another agent’s stopping point?
3. **Who updates it?**When Agent A learns something, who records it, corrects it later, and makes it available to Agent B?
4. **Can you inspect it?**Can you see source, project scope, date, and freshness before trusting the next plan?
5. **Can you use it safely?**May this repository be indexed by a hosted service, and is the setup and upkeep justified?

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

Bring code evidence and project judgment into the same handoff.

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.

Explore the platform → Repository search → Decisions and lessons →

1

Resolve the authorized workspace and project.

2

Inspect eligible indexed source and code relationships where available.

3

Retrieve relevant decisions, constraints, lessons, or handoffs.

4

Review the agent’s proposed action against current evidence.

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

Try one handoff you can check.

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.

Ask each fresh agent to:

1. State the current goal.
2. Cite relevant source and the prior decision.
3. Name one constraint.
4. Propose the first checkable action.

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

The tradeoffs to check first

Does code stay on my machine?

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 →

Will setup or maintenance become another job?

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 →

Will every agent use the context correctly?

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

See whether the next agent has what it needs.

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.

Review setup and choose a client → Check data boundaries first → Or start free in the workspace →

Welcome

Start free

THE CONTEXT PLATFORM BEHIND YOUR AGENTS

Your agents see the task.
Give them the bigger picture.

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 ›_PowerShell

MCP for your agents

$ curl -fsSL https://contextstream.io/scripts/mcp.sh | bash

Compare ContextStream

Published Benchmarks

Memory accuracyLongMemEval-S · 500 questions

90.0%

Code recallUseful-file Recall@10

99.38%

Hosted search · global edge networkMean p50 · 4 runs

95ms

[View results](https://contextstream.io/benchmarks)

Our June 14, 2026 LongMemEval-S comparison: 90.0% vs SuperMemory’s cited 85.4% result.

↑

The understanding behind the work.

idle

Project memory preserved

Project memory00 IN VIEW

DECISIONSWhat you chose—and why.↗LESSONSWhat went wrong. What to do differently.↗GUARDRAILSThe boundaries the work needs to respect.↗CODE CONTEXTHow the code connects—and what a change could affect.↗HANDOFFSWhat’s done, what’s open, and where to continue.↗RUNBOOKSThe steps and checks for doing it again.↗COORDINATIONWho’s working on what, and what changed.↗

Explore the full platform →

Choose a capability. Follow its evidence.

›ESC

↑ ↓ navigate   ↵ openNo commands to memorize.

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