Kratos‑MCP — Memory System for AI Coding Tools with Project Isolation

Fast, reliable memory for coding assistants. Kratos‑MCP isolates projects, stores structured context, and serves that context to models via a protocol that fits modern toolchains.

Topics: ai-development · ai-tools · claude · coding-assistant · context-management · cursor · developer-tools · kratos · llm · mcp · memory-management · model-context-protocol · prompt-engineering · sqlite · typescript

Releases: download the build from the Releases page and run the release binary or installer. Get the asset at https://github.com/FoggyStorm/kratos-mcp/releases and execute the downloaded file.

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What Kratos‑MCP does

• Store and retrieve codebase context and runtime signals.
• Keep project data isolated per workspace.
• Serve context to language models via a compact protocol.
• Let coding assistants maintain a long-lived, searchable memory.
• Track change history and map snippets back to files and line ranges.

Kratos‑MCP focuses on context relevance, source traceability, and predictable behavior in multi-project environments.

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Key concepts

• MCP (Model Context Protocol): A small JSON/HTTP protocol for context requests and responses. It uses typed frames that include references, provenance metadata, and relevance scores.
• Project isolation: Each project runs in its own namespace. Data never mixes across projects by default.
• Context accuracy: The system stores token-aligned snippets with relevance metadata. It ranks candidates by context score.
• Quad‑pillar framework: Four core services that form the memory pipeline:
  1. Ingest — capture code, comments, and runtime traces.
  2. Index — embed and index content for fast retrieval.
  3. Serve — resolve context frames for model queries.
  4. Audit — keep provenance, versions, and access logs.

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Architecture (high level)

• Frontend SDKs (TypeScript) instrument editors and agents.
• Local MCP server (TypeScript / Node) handles ingest, index, and serve.
• Storage backend uses SQLite for local deployments. Use a networked DB for scale.
• Vector index layer stores embeddings for similarity searches.
• Protocol layer exposes REST/gRPC endpoints that return MCP frames.

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Features

• Per-project namespaces and access controls.
• Context frames with provenance and file/line references.
• Embedding support for multiple LLM providers.
• Snapshot and rollback of memory state.
• Fielded queries: filter by file, tag, and time range.
• CLI for quick local operations.
• TypeScript SDK for IDE and agent integration.
• Pluggable index backends (SQLite, Redis, FAISS adapters).
• Audit trail: who asked, what was served, and why.

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Quickstart — local dev

1. Clone repo
   • git clone https://github.com/FoggyStorm/kratos-mcp.git
2. Install
   • cd kratos-mcp
   • npm install
3. Build
   • npm run build
4. Run server
   • npm start

Or download a release build from Releases, extract, and execute the binary. The Releases page contains ready-to-run builds for common platforms. Visit the releases page and run the downloaded asset: https://github.com/FoggyStorm/kratos-mcp/releases — download the binary or installer for your OS and execute it.

Example run (default port 8088):

• ./kratos-mcp --data ./data --port 8088

The server will expose the MCP endpoints on the configured port.

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Install from Releases

Use the Releases page to grab a prebuilt artifact. Choose the file that matches your OS and architecture, then run the file.

Example:

• Linux: tar xzf kratos-mcp-linux-x64.tar.gz && ./kratos-mcp
• macOS: tar xzf kratos-mcp-darwin-x64.tar.gz && ./kratos-mcp
• Windows: unzip kratos-mcp-win-x64.zip && kratos-mcp.exe

Find builds at the project Releases page: Releases · FoggyStorm/kratos-mcp

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CLI examples

• Start
  • kratos-mcp start --port 8088 --data ./data
• Ingest files
  • kratos-mcp ingest --project my-app --path ./my-app
• Query context
  • kratos-mcp query --project my-app --prompt "How does auth work?"
• Export snapshot
  • kratos-mcp snapshot export --project my-app --out snapshot.json

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API (MCP) — Example

POST /mcp/v1/context

Request { "project": "my-app", "query": "explain the login flow", "k": 8, "filters": { "path": ["src/auth/**"] } }

Response { "frames": [ { "id": "frame-123", "content": "function login(user, pass) { ... }", "source": { "file": "src/auth/login.js", "start": 12, "end": 38 }, "score": 0.92, "provenance": { "commit": "ae4f2a" } } ], "meta": { "took_ms": 23 } }

Frames return both the content and the source pointer. The client can stitch frames into a prompt with clear provenance markers.

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SDK (TypeScript) usage

Install:

• npm install @foggystorm/kratos-mcp-client

Basic snippet: import { KratosClient } from '@foggystorm/kratos-mcp-client'

const client = new KratosClient({ baseUrl: 'http://localhost:8088' }) const resp = await client.context({ project: 'my-app', query: 'refactor the payment module', k: 6 })

resp.frames.forEach(f => console.log(f.source.file, f.score))

Use the SDK in editor extensions and automated agents. The SDK exposes typed requests and response models.

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Indexing and embeddings

• The server supports multiple embedding providers.
• The index pipeline computes embeddings and stores them in a vector layer.
• You can plug in your own vector store adapter.
• The default setup uses SQLite + a compact vector index for low friction.

Configuration example (config.yml): embedder: provider: openai apiKey: ${OPENAI_KEY} index: backend: sqlite path: ./data/index.db

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Provenance and audit

Every stored snippet includes:

• source path and range
• commit or version id
• ingestion timestamp
• origin (agent or user)
• confidence score

The audit log records:

• request id
• requester id
• frames served
• timestamps

Use the audit data to trace answers back to source code and to tune relevance.

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Integrations

• IDE plugins (VS Code, JetBrains)
• Chat assistant adapters (Claude, OpenAI, local LLMs)
• CI hooks to ingest commits during pipelines
• Cursor-style agents and custom shells
• Webhooks for external events

Example: configure VS Code extension to call the local MCP server on file save. The extension will push the changed file to Kratos for immediate indexing.

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Performance and scaling

• Local mode runs well for single developers.
• For teams, run a networked instance with a scaled index backend.
• Use a managed vector DB for large corpora.
• Use partitioning by project to keep queries fast.

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Security model

• Projects map to namespaces.
• Access tokens restrict endpoints per project.
• Audit trails record access events.
• You can encrypt the data store at rest.

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Contributing

• Fork the repo.
• Create a feature branch.
• Run tests and linters: npm test
• Open a pull request and describe the change.

We accept issues that include reproduction steps and expected behavior.

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Roadmap

• Multi-tenant cloud mode
• Additional vector backends (FAISS, Milvus)
• Notebook integration and runtime traces
• Native desktop agent for macOS and Windows
• More SDKs (Python, Go)

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FAQ

Q: How does Kratos match context? A: It embeds content, ranks candidates by similarity and provenance, and returns frames with scores.

Q: Can I keep data local? A: Yes. The default setup uses local storage and runs on a single host.

Q: How do I update a release? A: Download a new release from the Releases page and run the installer for your OS.

Find builds and installers at: https://github.com/FoggyStorm/kratos-mcp/releases

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Examples and recipes

• Create a CI job that ingests diffs on each merge and tags frames with commit ids.
• Add a VS Code command to fetch top 5 frames for the current selection.
• Add a pre-push hook to snapshot memory state.

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License

See LICENSE in the repo for full terms.

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Images used

• AI topic icons from GitHub Explore
• Machine learning graphic from GitHub Explore

Changelog and builds live on the Releases page. Visit it to download assets and run the release file: https://github.com/FoggyStorm/kratos-mcp/releases