A terminal-native AI coding assistant powered by live engineering memory. It remembers your codebase, team findings, decisions, and tests — then compiles the right context for every task.
$ npm install -g lakecode
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Every team can ship v1. The hard part is year two — when the original authors have moved on, the docs are stale, and the only record of why things work is scattered across closed PRs and Slack threads. Coding assistants make this worse, not better.
Most coding assistants start from zero every session. They can write code, but they can't remember why code exists.
They explore the codebase from scratch every time, burning tokens on files they have already seen and patterns they have already learned.
When the context window fills, they summarize and compress — losing the specific details that actually matter for the next step.
Close the session and everything is gone. The next session knows nothing about what was learned, decided, or resolved before.
A persistent, structured memory layer that makes every session build on the last.
Lakecode captures what matters from every session: architecture decisions, debugging findings, team conventions, test outcomes. Memory persists across sessions, branches, and teammates.
Before the agent writes a single line, Lakecode compiles the right context for the task. Not everything — just the memories, code, and conventions that are relevant right now.
The agent starts with exactly the context it needs. No wandering. No re-exploration. It produces better code, faster, because it already knows what your team knows.
Terminal-native. No browser tab. No IDE plugin. No context switching.
Works alongside git, your editor, and your existing workflow. No new windows.
Describe what you need in plain English, or use structured commands for repeatable workflows.
No uploading, syncing, or indexing step. Lakecode reads your local project and integrates with git context.
TypeScript, Python, Rust, Go, Java — if it lives in a repo, Lakecode can work with it.
Memory compounds over time. The more you use Lakecode, the less context you need to provide.
Why the team chose event sourcing. Why the API uses camelCase. Why auth is handled at the gateway. Decisions persist so agents stop re-litigating them.
Root causes, environment quirks, and known failure modes. When a similar error appears, the agent already knows what was tried and what worked.
Error handling patterns. Test structure. Naming standards. Import ordering. The unwritten rules that make code feel like it belongs in your codebase.
Which tests are flaky and why. Which edge cases have been validated. What coverage gaps exist. Agents write better tests because they know what has already been tested.
Lakecode starts learning from your first session. Connect more sources to accelerate.
Repo structure, imports, interfaces, test files, configs. Lakecode reads the project in place — no indexing step, no upload.
Findings, fixes, failed approaches, root causes. Every debugging session can write durable memory that future sessions draw from.
Design decisions, rationale, review comments, rejected approaches. The reasoning behind changes persists beyond the merge.
Architecture docs, runbooks, ADRs, Confluence pages. Structured knowledge extracted and linked to the entities they describe.
Promoted findings, shared constraints, conventions. Individual memory becomes team knowledge through a review process.
Test outcomes, flaky tests, coverage gaps, edge cases validated. Agents write better tests because they know what has been tested.
Lakecode starts learning from your first session. No configuration required.
The context compiler selects only what matters for each task — reducing noise and token waste.
"add caching to the user service"
"fix the flaky order test"
"refactor billing to use the new API"
Measured on real engineering tasks. Compiled context replaces brute-force exploration.
13.3x
fewer input tokens per task
3.8x
fewer tool calls per session
0
re-exploration of known code
ms-009: retry logic task benchmark
Without Lakecode
With Lakecode
Static skill files describe what to do. Lakecode-backed skills know how your team actually does it.
Static skill file
Lakecode-backed skill
Two interfaces, one memory layer.
For teams that need control, compliance, and integration with existing infrastructure.
Control what gets remembered, who can access it, and how long it persists. Approval workflows for team-wide memories.
Integrate with your identity provider. Automatic user provisioning and de-provisioning.
Full audit trail of memory access, creation, and compilation. Export to your SIEM.
Run Lakecode in your own infrastructure. Your data never leaves your environment.
Bring your own LLM endpoints. Route through your proxy. Use models approved by your security team.
Databricks-native
For Databricks customers, Lakecode integrates directly with your lakehouse. Memory is stored in Unity Catalog. Access follows your existing governance model.
Install Lakecode in 30 seconds. Start building memory that compounds.
$ npm install -g lakecode
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