cachly-dev/cachly-mcp

🧠 Knowledge & Memory
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πŸ“‡ ☁️ 🏠 🍎 πŸͺŸ 🐧 - Persistent AI memory brain for Claude Code, Cursor, Copilot, Windsurf, Cline & Zed. sessionstart() briefs your AI on last session, lessons, and open tasks in one call. 84 tools β€” Team Brain, semantic BM25+ search, Team Telepathy, Ambient Git Learning, Memory Crystals, Analytics, and managed Valkey/Redis. npx @cachly-dev/init β€” free tier, no credit card. Website

Quick Install

One-Click IDE Configuration
claude_desktop_config.json
{
  "mcpServers": {
    "cachly-dev-cachly-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "cachly-dev-cachly-mcp"
      ]
    }
  }
}
Or

Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.

Documentation Overview

🧠 cachly AI Brain β€” MCP Server

Your AI is brilliant for one session. Then it forgets you.

Every morning you re-explain your architecture, your deploy process, the bug you already fixed last week. cachly gives your AI β€” and your whole team β€” a permanent, shared brain that gets smarter with every commit.

npm version Β  npm downloads Β  Free tier Β  GDPR: EU servers Β  126 MCP tools Β  License: Apache-2.0

⚑ Get your free Brain β†’ cachly.dev
Free forever Β· no credit card Β· 1-command setup Β· German servers Β· GDPR


The story you already live every day

You are a good engineer. You want to ship, not babysit a forgetful assistant.

But every session starts at zero. Your AI doesn't remember the race condition you chased for three hours on Tuesday. It doesn't know your deploy gotchas. It can't tell you that Carol already solved this exact bug in March β€” because Carol's knowledge lives in Carol's head, and yours in yours.

So you re-explain. You re-research. Your team makes the same mistake in five different branches. And when someone leaves, their hard-won knowledge walks out the door with them.

The villain isn't your AI. It's amnesia. Context death between sessions, and knowledge silos between people. The average developer loses ~45 minutes a day re-establishing context that should already exist.

You don't need a smarter model. You need a memory that doesn't reset β€” and one that your whole team shares.


Meet your guide

cachly is the brain layer that sits under whatever AI you already use. We've watched hundreds of teams lose the same knowledge the same way, and we built the fix:

  • It learns automatically β€” from every commit, every fix, every session. No extra calls.
  • It arrives pre-briefed β€” your AI opens each session already knowing your stack.
  • It's shared β€” one engineer's solved bug becomes the whole team's reflex.
  • It's provable β€” quality-aware recall beats raw text search by +33.3 % Precision@1 (see the benchmark). A claim without a number is marketing; this is the number.
  • It's neutral β€” speaks MCP, so it works with Claude, Cursor, Copilot, Windsurf, Cline, Zed. Switch models anytime β€” your brain stays.

We're not the hero of this story. You are. cachly is the thing that makes you the engineer whose AI never forgets and whose team compounds knowledge instead of losing it.


Taste it first β€” no account, no risk

npx @cachly-dev/mcp-server@latest demo

Run it in any project folder. It reads YOUR git history and shows what your AI would know β€” your bugs fixed, your patterns, your past decisions. Nothing leaves your machine.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚  Brain Preview β€” What your AI would know                    β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  Commits: 847   Lessons: 634   Contributors: 7              β”‚
β”‚  Date range: 2024-01-12 β†’ 2026-05-14                        β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  Security fixes your AI would know:                         β”‚
β”‚  β€’ fix(auth): JWT expiry check before signature validation  β”‚
β”‚  β€’ security: sanitize webhook payload before JSON.parse     β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  Bug fixes your AI would remember:                          β”‚
β”‚  β€’ fix: Redis pub/sub race condition under high concurrency β”‚
β”‚  β€’ fix: k8s readinessProbe threshold too low for cold start β”‚
β”‚  β€’ fix: Stripe idempotency_key missing on retry path        β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  With cachly, your AI arrives pre-briefed every session.    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Like what you see? Make it permanent in the next step.


Brain-first β€” Semantic Cache as Proof-Point

cachly is not a semantic cache with a brain bolt-on. The Brain is the product. The Semantic Cache is the proof-point β€” it shows ROI in dollars from day one, with zero trust required. It opens the door. The Brain is why teams never leave.

Wedge β€” LandMoat β€” Retain
FeatureSemantic CacheAI Brain (Lessons, Recall, Team-Sharing)
ValueMeasurable cost savings from day oneCompounding team intelligence
MetricCache-hit rate, $/month savedLessons retained, WoW trend, recall quality
AnalogyDatadog APM (surfaces the problem)Stripe (becomes critical infrastructure)

The org-level advantage: Brain lessons and cache hits are shared across the whole team β€” one person's fix becomes every agent's reflex. Anthropic Projects Memory is per-user and model-locked. cachly is team-wide and model-neutral. That's the structural moat no first-party tool can build.


Setup β€” one command

npx @cachly-dev/mcp-server@latest autopilot

Autopilot does everything in a single command: it auto-detects every AI editor you use, writes the MCP config, signs you in via browser device-flow (one click, no password, no credit card), and bootstraps your brain from git history. Restart your editor and your AI arrives pre-briefed β€” every session, automatically.

Already inside Claude / Cursor / Copilot? Paste this to your AI and it configures everything itself:

Set up cachly for this project. Run: npx @cachly-dev/mcp-server@latest autopilot
It gives my AI persistent memory across sessions. Follow the browser login
(one click, no credit card), then restart the editor.

Our agreement with you: Free forever tier. GDPR, EU servers. No model lock-in β€” leave anytime and take your data. No code content is ever stored.


What changes the moment you turn it on

The momentWithout cachlyWith cachly
Session start"What's your architecture again?""Ready. 23 lessons. Last session: deployed API."
A known bug returnsRe-researches from scratch"You fixed this March 12 β€” here's the exact command."
You open an unfamiliar fileCold start"Carol fixed 3 bugs here. Related: fix:stripe-retry."
A teammate leavesTheir knowledge leaves tooTheir lessons stay, attributed, searchable
New hire, day oneWeeks to onboardsetup β†’ full team context instantly
Pre-deployHope nothing breaksBrain predicts failure risks from past patterns

This is the transformation: from the engineer who re-explains everything every morning β†’ to the team whose collective brain never forgets and gets sharper with every commit.


cachly vs. Claude's built-in memory

Anthropic now ships memory for Claude β€” and it's genuinely good for one developer, using only Claude, alone. That's not the game we're playing. Here's the honest map:

cachlyClaude built-in memory
Works across teamsβœ… one engineer's fix β†’ everyone's reflex❌ per-user / per-agent only
Works across models & toolsβœ… MCP β€” Claude, Cursor, Copilot, Windsurf, Zedβ€¦βŒ Claude + Anthropic API only
Structured knowledgeβœ… topic Β· outcome Β· severity Β· causal graph⚠️ flat text files, read linearly
Causal root-cause (causal_trace)βœ… problem β†’ chain β†’ proven fix❌
Provable recall qualityβœ… +33.3 % Precision@1 vs. BM25 (benchmark)❌ no public metric
Governance (review, attribution, audit)βœ… team_confirm, roles, audit trail❌
Self-hosting / BYOK / VPCβœ… data stays in your infra❌ Anthropic-hosted
Survives a model switchβœ… your brain is yours❌ memory is gone or fragmented
Zero-setup for one solo user⚠️ ~1 commandβœ… built in

The honest takeaway: if you're a solo dev who only ever uses Claude, the built-in memory is great β€” use it. If you work on a team, switch tools, care about proof, or need governance and data residency, that's a gap Anthropic structurally can't close without breaking its own lock-in. That gap is where cachly wins. (Full strategic analysis: STRATEGY.md.)


vs. other memory tools

cachlymem0MemGPT / LettaPlain CLAUDE.md
Persistent memoryβœ…βœ…βœ…Manual
MCP server (no code changes)βœ…βœ…βŒβœ…
Causal root cause analysisβœ…βŒβŒβŒ
Fully automatic (no explicit calls)βœ…βŒβŒβŒ
Team knowledge graph + attributionβœ…Paid❌❌
Provable recall lift (published)βœ…βŒβŒβŒ
Git-ambient learningβœ…βŒβŒβŒ
GDPR / EU serversβœ…βŒβŒβœ…
Free tier foreverβœ…LimitedβŒβœ…

The standout moves

CapabilityWhat it does
causal_traceRoot-cause analysis through memory: problem β†’ causal chain β†’ the fix that worked, with date and commands. No other system builds and queries a causal graph.
brain_who_knows"Who on my team knows about Kubernetes deploys?" β†’ ranked experts πŸ₯‡πŸ₯ˆπŸ₯‰, built automatically from authorship.
brain_file_mapBefore you touch a file: who's worked on it and which lessons reference it.
team_expertise_mapThe whole team's skills matrix in one table β€” onboarding and bus-factor insurance.
brain_collab_pairsPerson↔Person Collaboration Graph β€” "Frag X und Y, die haben das zusammen gelΓΆst." Bus-factor alerts included.
brain_portabilityW9 Model-Neutrality β€” config for 7 clients (Claude, Cursor, Copilot, Windsurf, Cline, Zed, Continue). "Same Brain, any model."
brain_from_gitReads your entire git history and populates the team knowledge graph (people + files + lessons) β€” zero setup, retroactively.
brain_coverage / skill_gapsA 0–100 health score for your knowledge + a ranked list of blind spots to fix.
brain_predictPredicts likely failures before they happen, from past incident patterns.
Ambient GitA git hook auto-extracts lessons from every commit. Zero extra calls.

causal_trace in action:

causal_trace(problem="auth breaks after restart")

β†’ Root: k8s:namespace-terminating
β†’ Via:  keycloak:jwks-race
β†’ Fix:  PollUntilContextTimeout 3min  ← used this March 12, worked

30 minutes of git blame in one call.


What runs automatically after setup

TriggerWhat the Brain does β€” no prompting
First tool callSession starts; project indexed in background
Before every taskRecalls relevant past lessons
During debuggingTraces root causes through causal memory
Before deploysPredicts failure risks from past patterns
After every fixStores the lesson with commands + file paths + author
Every git commitHook extracts a lesson from the commit
Editor closesSession summary saved for next time

CLI Commands

npx @cachly-dev/mcp-server@latest autopilot # One command β€” signs in, configures every editor, bootstraps from git
npx @cachly-dev/mcp-server@latest demo      # Preview your Brain (no account needed)
npx @cachly-dev/mcp-server@latest bench     # Recall quality vs flat-file memory (no auth required)
npx @cachly-dev/mcp-server@latest autosetup # Interactive variant β€” pick editors yourself
npx @cachly-dev/mcp-server@latest health    # Check token, API, editors, git hook
npx @cachly-dev/mcp-server@latest digest    # Weekly Brain summary β€” shareable
npx @cachly-dev/mcp-server@latest share     # Generate a shareable stats card + tweet
npx @cachly-dev/mcp-server@latest publish   # Publish your Brain as an importable link (--public)
npx @cachly-dev/mcp-server@latest badge     # Get a live README badge for your Brain
npx @cachly-dev/mcp-server@latest invite    # Invite a teammate to share your Brain
npx @cachly-dev/mcp-server@latest index .   # Index a project's code into the Brain (CI-friendly)
npx @cachly-dev/mcp-server@latest learn-git # Auto-learn lessons from recent git commits

Tip β€” auto-learn on every merged PR: run learn-git in CI via the cachly-brain-setup GitHub Action with mode: learn. Each merged PR teaches your Brain automatically.


CI integration β€” your pipeline teaches the Brain

Every CI run is a lesson: a red→green transition is a proven fix, a green→red one is a known cause. Ready-to-paste templates live in src/ci-integration/:

  • GitHub Actions β€” copy brain-from-ci-action.yml into .github/workflows/. It triggers on workflow_run (completed) and pushes the outcome to your Brain. Requires CACHLY_JWT + CACHLY_BRAIN_INSTANCE_ID secrets.
  • GitLab CI β€” copy brain-from-ci-gitlab.yml into your pipeline: two .post jobs (on_success / on_failure) with allow_failure: true. Want more than outcome pushes? The full GitLab template cachly.gitlab-ci.yml adds hidden jobs for learn / scan / confirm β€” pull it in with include: remote: (it is an includable template, not a CI/CD Catalog component).
  • Anything else β€” push-ci-outcome.mjs is a standalone Node.js helper with zero dependencies. It always exits 0 β€” your CI never fails because of a Brain push.

Already have months of CI history? Backfill it in one call with the brain_from_ci MCP tool β€” bulk-ingests past outcomes the same way brain_from_git ingests commits.


MCP Tools (126 total)

The full tool catalog is generated from sdk/mcp/src/tools.ts. Cross-surface coverage is tracked in ../../docs/generated/surface-parity.md, and pinned OpenAPI/OpenAI/Anthropic/LangChain projections live in ../../docs/generated/tool-specs/.

🧠 Session & Memory (most used)

ToolWhat it does
session_startFull briefing: last session, open failures, recent lessons, brain health
session_endSave what you built; auto-extract lessons from summary + git log
learn_from_attemptsStore structured lessons after any fix, deploy, or discovery (with author, visibility)
recall_best_solutionBest known solution for a topic β€” with success/failure history
smart_recallHybrid BM25 + semantic + causal-graph search β€” 11 languages, quality-reranked
remember_contextCache architecture findings, decisions, file summaries
compact_recoverFull context recovery after hitting the context-window limit

πŸ‘₯ Team Brain & Org Knowledge Graph

ToolWhat it does
team_learn / team_recallShare lessons across the team with author attribution
team_confirmA reviewer confirms a lesson (πŸ›‘οΈ senior / βœ”οΈ peer) β†’ ranks higher in recall Β· reviewer-gated
team_assign_role / team_roster / team_whoamiRoles (πŸ‘‘ admin Β· πŸ›‘οΈ reviewer Β· ✏️ contributor Β· πŸ‘οΈ viewer) β€” enforced once an admin is set
team_auditImmutable, admin-only governance trail: every role change & lesson confirmation
brain_who_knowsFind your team's experts on any topic β€” ranked πŸ₯‡πŸ₯ˆπŸ₯‰
brain_file_mapExperts + lessons per file, before you touch it
team_expertise_mapFull team skills matrix in one table
brain_collab_pairsPerson↔Person Collaboration Graph β€” who collaborates with whom, bus-factor alerts
brain_portabilityConfig snippets for 7 MCP clients β€” proves model-neutrality, same Brain everywhere
skill_gapsKnowledge blind spots: unresolved failures, missing attribution
brain_coverage0–100 knowledge-health score for your codebase
madc_deliberateSpecialist AI agents vote to resolve contradictory lessons
memory_crystalizeDistill all lessons into a Crystal for instant team context
team_crystallizeTeam Crystal β€” fixes that 2+ teammates independently converged on (the cross-person, causal layer)

🧬 Causal Intelligence

ToolWhat it does
causal_traceRoot-cause analysis through the Causal Knowledge Graph
brain_predict / brain_predict_failuresPredict likely failures before they happen
brain_from_gitBootstrap people + files + lessons from git history β€” incremental
brain_from_ciBulk-ingest CI outcomes: red→green becomes a fix lesson + causal fixes edge, green→red a causes edge — brain_from_git for CI logs
memory_consolidateDetect contradictions, merge duplicates, expire stale lessons
ckg_inspectInspect the causal graph around any concept

🌐 Shareable & Public Brains

ToolWhat it does
brain_seed_starterSeed 16 universal lessons so your first smart_recall hits β€” auto-runs on a fresh repo
brain_shareExport a Brain snapshot as a shareable link (public or unlisted)
brain_importImport any shared Brain into yours β€” topic_prefix, min_confidence, dry_run
brain_share_list / brain_unshareList your shares Β· revoke a share (link goes dead)
brain_discoverSearch the Brain marketplace for ready-made knowledge bases

🌍 Knowledge Commons Β· βš™οΈ Infrastructure Β· πŸ“‹ Roadmap

ToolWhat it does
syndicate / fedbrain_searchContribute to / search the global Knowledge Commons
brain_marketplace / brain_installBrowse + install curated Domain Brains (Kubernetes, Auth, DB…) into your Brain
cache_get / cache_set / semantic_search / index_projectCache + semantic ops β€” pass org_id on cache_get/cache_set to share the cache org-wide (writes mirror to org:{org_id}:sem, reads fall back to it on miss)
cache_stats / cache_org_statsTokenmaxxing ROI: hits, estimated USD saved + monthly projection β€” per instance or aggregated across your whole org. Zero hits yet? You get a day-1 ROI projection instead.
list_instances / create_instance / delete_instanceManage Brain instances
roadmap_add / roadmap_nextPersistent project roadmap stored in the Brain

…and ~70 more. Run health to see what's wired up in your editor.


FAQ

Does my AI need to call session_start manually? No. Sessions start and end automatically on the first tool call and when the editor closes.

How is this different from Claude's built-in memory? Claude's memory is per-user, Claude-only, flat-file, and unbenchmarked. cachly is team-shared, model-neutral (any MCP client), structured + causal, governed, and has a published recall benchmark. See the comparison table above.

Can my whole team share one Brain? Yes β€” that's the point. team_learn / team_recall, or npx @cachly-dev/mcp-server@latest invite teammate@example.com.

Is my code sent to cachly servers? No code content is stored. cachly stores lesson text, commit messages, session summaries, and key-value context. All data on EU servers, GDPR-compliant.

What is causal_trace and why is it unique? Given any error, it walks the Causal Knowledge Graph to find root cause, intermediate causes, and the exact fix that worked β€” including date and commands. No other memory system builds or queries a causal graph.

What if I hit the context-window limit mid-session? Call compact_recover. It reconstructs full context from Memory Crystal + recent sessions + WIP registry β€” typically one tool call.


Editor support matrix

npx @cachly-dev/mcp-server@latest autopilot auto-detects and configures all of the following. Manual snippets are in the Manual Setup section below.

Editor / ClientAuto-setupConfig file writtenGlobal configNotes
Claude Codeβœ…~/.claude/mcp.json + .mcp.jsonβœ… global alwaysRuntime device-flow sign-in on first tool call
Cursorβœ… detected via .cursor/.cursor/mcp.jsonβ€”Project-level; restart Cursor after setup
Windsurfβœ… detected via .windsurf/.windsurf/mcp.jsonβ€”Project-level; restart Windsurf after setup
VS Code + Copilotβœ… detected via .vscode/.vscode/mcp.jsonβ€”Requires VS Code MCP extension or Copilot chat
Clineβœ… detected via VS Code.vscode/mcp.jsonβ€”Shares config with Copilot; restart VS Code
Continue.devβœ… detected via .continue/.continue/config.jsonβ€”Uses modelContextProtocolServers key
Zedβœ… detected via .zed/.zed/settings.jsonβ€”Uses context_servers key
Windsurf (global)autosetup --editor windsurf~/.windsurf/mcp.jsonβœ…Pass --editor to target global config
Any other MCP clientautosetup --editor claude.mcp.jsonβ€”Standard mcpServers stdio format

Which sign-in path each editor uses:

ScenarioPath
autosetup from a real terminal (TTY)OAuth device-flow β†’ browser click β†’ API key saved automatically
autosetup from VSCode task / CI (non-TTY)Auto-detects non-interactive, opens browser with step-by-step guide, prints CACHLY_JWT=... autosetup instruction
First tool call from Claude Code (no JWT yet)Inline device-flow: MCP returns URL + code, browser opens automatically, next call proceeds
CACHLY_JWT=cky_live_xxx npx ... autosetupSkips auth step entirely, uses provided key

Tip β€” fastest per-project setup from inside Claude Code:

Set up cachly for this project: npx @cachly-dev/mcp-server@latest autopilot

Claude runs it and restarts automatically.


Manual Setup

Claude Code (~/.claude/mcp.json or .mcp.json)
{
  "mcpServers": {
    "cachly": {
      "command": "npx",
      "args": ["-y", "@cachly-dev/mcp-server@latest"]
    }
  }
}

On the first tool call your AI will prompt you to sign in β€” takes 10 seconds.

Cursor / Windsurf / VS Code / Copilot / Cline
{
  "mcpServers": {
    "cachly": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@cachly-dev/mcp-server@latest"]
    }
  }
}
Zed (.zed/settings.json)
{
  "context_servers": {
    "cachly": {
      "command": {
        "path": "npx",
        "args": ["-y", "@cachly-dev/mcp-server@latest"]
      }
    }
  }
}

Self-hosting & BYOK

cachly is bring-your-own-key and self-host friendly out of the box β€” no enterprise contract required to keep data in your own infra.

Bring your own embedding key (BYOK). Semantic search runs on the embedding provider you choose. Set one env var and cachly auto-detects it; no key needed if you prefer cachly's server-side embeddings (uses your JWT):

ProviderEnv varModel
OpenAIOPENAI_API_KEYtext-embedding-3-small
Google GeminiGEMINI_API_KEYtext-embedding-004
MistralMISTRAL_API_KEYmistral-embed
CohereCOHERE_API_KEYembed-english-v3.0
Ollama (local, free)OLLAMA_BASE_URLnomic-embed-text
cachly (server-side)(none β€” uses JWT)managed

Force a specific one with CACHLY_EMBED_PROVIDER=openai. Run npx @cachly-dev/mcp-server@latest health to confirm which provider is active.

Point at your own backend (self-hosting). Every cachly install can talk to a private backend instead of api.cachly.dev:

# One-shot: wire up the wizard against your self-hosted backend
npx @cachly-dev/mcp-server@latest autopilot --api-url https://cachly.mycorp.internal

# Or non-interactively
npx @cachly-dev/mcp-server@latest autosetup \
  --instance-id <uuid> --api-key <cky_live_...> \
  --api-url https://cachly.mycorp.internal

autosetup bakes CACHLY_API_URL into the editor config only when it differs from the default cloud β€” so default installs stay clean, and self-hosted installs keep talking to your backend on every editor launch. All data stays in your infra.


Pricing

TierRAMPriceBest for
Free25 MB€0/mo foreverDev & side projects
Dev200 MB€19/moIndividual developers
Pro900 MB€49/moTeams
Speed900 MB + Dragonfly€79/moAI-heavy workloads
Business7 GB€199/moScale-ups

βœ… All plans: EU servers Β· GDPR-compliant Β· 99.9% SLA Β· No credit card for Free


Environment Variables

VariableDefaultDescription
CACHLY_JWTβ€”API token (set by wizard automatically)
CACHLY_BRAIN_INSTANCE_IDβ€”Default instance UUID (optional β€” auto-resolved)
CACHLY_API_URLhttps://api.cachly.devOverride for self-hosted
CACHLY_NO_TELEMETRYunsetSet to 1 to disable anonymous usage pings

🧠 Brain v3 β€” what's new

FeatureToolWhat it does
Autonomous hygienebrain_hygieneSweeps stale lessons, flags provisional, archives orphans
PR risk scancachly-action scan / predict modesMatches PR title, body and changed files against Brain lessons via the /scan API β€” posts a PR comment with risk score before CI runs
Multi-agent arbitrationbrain_conflicts Β· brain_resolve_conflictDetects + resolves conflicting lessons across agents
Plans dashboardbrain_planPersistent plans in the UI with step tracking and brain-viz overlay
Privacy federationbrain_contribute_signal Β· brain_import_metaShare patterns without sharing data β€” k-anonymous global commons

πŸ› οΈ Ecosystem & Docs

One brain, wherever you work. Start with the MCP server, or drop the same memory straight into your editor β€” your lessons follow you across all of them.

PackageWhat it does
@cachly-dev/mcp-server← you are here Β· works with Claude, Cursor, Copilot, Windsurf, Cline, Zed
Cachly Brain for VS CodeOne-click memory in the editor β€” status bar, lessons view, ambient learning. No terminal needed.
Cachly Brain for JetBrainsSame brain for IntelliJ / PyCharm / GoLand / WebStorm / Rider β€” status bar, brain health, lessons view.
@cachly-dev/openclawCut LLM costs 60–90% in JS/TS apps
cachly-dev/cachly-actionGitHub Action: auto-setup, PR risk scan, auto-learn from merged PRs, weekly hygiene

Stop re-explaining yourself to your own tools. Give your AI β€” and your team β€” a brain that remembers, learns, and gets sharper with every commit.

npx @cachly-dev/mcp-server@latest autopilot

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Last checked: 7/28/2026, 7:54:59 PM

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