Context layer for engineers
A personal context layer for coding agents.
Find what matters before every turn: architectural decisions, code history, implementation facts, and project instructions - without dumping your entire world into the prompt.
curl -sSfL https://install.memory.build | sh && me loginFor engineers who need agents to work from the right context, not the largest prompt.
Prompting has a ceiling
Bigger prompts do not guarantee better context.
You can paste more files, write longer instructions, and summarize the last five sessions.
Agent quality depends less on how much context you provide and more on whether the right context appears at the right moment.
Context layer, defined
The system between your codebase and your agent.
A coding agent needs more than code. It needs to know what is true, what changed, what failed, and how work is supposed to happen.
A context layer stores that information separately from any one chat, then retrieves the right pieces for the task.
Store
Keep engineering context beyond one chat, branch, sprint, or agent session.
Retrieve
Find relevant facts, decisions, instructions, and history without loading everything.
Expose
Make context available through MCP-compatible clients where you already work.
The inputs
Context is made of facts, history, and instructions.
The context layer works best when durable engineering knowledge is explicit, searchable, and reusable.
Facts
What is true about the system.
Names, contracts, constraints, dependencies, and implementation details your agent should not rediscover.
“We use gRPC for service-to-service calls. JWTs are signed with RS256. Migrations live in /db/changes.”
History
What happened, when, and why.
Decisions, rejected alternatives, migrations, experiments, and the reasoning behind the current state.
“We switched from REST to gRPC because streaming latency mattered. GraphQL was ruled out because caching cost became unpredictable.”
Instructions
How work gets done here.
Runbooks, style guides, review expectations, implementation order, and project-specific agent guardrails.
“For new endpoints: add tracing first, write the handler, update contract tests, then include the RFC link in the PR.”
Retrieval examples
The right context is not always the nearest vector.
Why did we switch from REST to gRPC?
Use semantic and temporal retrieval to find the decision, reasoning, and time window around the change.
What did we rule out yesterday for caching?
Use temporal search to recover recent context and rejected alternatives.
How did auth change this month?
Use temporal, keyword, and metadata search across a defined window.
What applies to billing endpoints?
Use hierarchy and metadata filters to retrieve procedures scoped to the right domain.
Six search modes
Built for precision, not just similarity.
Engineering questions depend on names, dates, owners, hierarchy, and meaning. The context layer should be able to use all of those signals.
Semantic
Find context by meaning when the wording does not match.
Keyword
Match exact services, APIs, identifiers, packages, acronyms, and names.
Temporal
Recover what happened before, after, or during a specific work window.
Metadata
Filter by project, owner, service, status, source, environment, or policy.
Hierarchy
Scope context to nested teams, systems, services, domains, or paths.
Hybrid
Fuse multiple signals so ranking reflects the shape of the question.
Better context beats bigger prompts.
Give your agent a searchable layer of engineering facts, decisions, and instructions instead of forcing every prompt to carry the whole project.
curl -sSfL https://install.memory.build | sh && me loginTrust the mechanism
Not magic memory. Observable context assembly.
Memories live in PostgreSQL. Retrieval is explicit. Stored context can be inspected and refined. The agent is retrieving from a durable context layer you control.
Build your personal context layer
Build a better context layer for every coding session.
Retrieve the right engineering context before your agent acts.
curl -sSfL https://install.memory.build | sh && me loginFAQ
How is a context layer different from memory?
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Memory is the benefit. The context layer is the mechanism for storing, searching, retrieving, and refining that context over time.
Why not just use a larger context window?
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Larger windows help, but they do not decide what matters. Retrieval helps assemble the right information instead of loading everything.
What does it store?
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Reusable engineering context: facts, history, and instructions, including decisions, runbooks, conventions, and project guidance.
Can I use it across tools?
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Yes. MCP compatibility lets the same context layer be accessed from multiple coding clients.