claude-context vs codebase-memory-mcp
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Semantic code search server that lets Claude Code and other AI agents pull relevant code from a whole repo.
Open source · MIT
A local tool that maps your code, docs and infrastructure into a graph so AI coding agents can query it.
The short answer
Choose claude-context if you need…
- Developers working in large codebases where loading everything into an agent's context is impractical
- People already using Claude Code, Cursor, Gemini CLI, Codex CLI or similar MCP-compatible agents
- Teams comfortable creating OpenAI and Zilliz Cloud accounts and managing API keys
Choose codebase-memory-mcp if you need…
- Developers who use an MCP-capable AI coding agent on a large codebase
- Engineers who want to find where a feature lives and how calls connect across files
- Teams that want to estimate which code and tests a change could affect
- People who prefer keeping source analysis on their own machine
Side-by-side
| claude-context | codebase-memory-mcp | |
|---|---|---|
| Type | Dev tool | Dev tool |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12.7k | 48k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | See the README |
| Main language | TypeScript | C |
| Last code update | 4 months ago | 4 days ago |
What each one is
claude-context
Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents. It indexes your codebase into a vector database so the agent can find relevant code by meaning and load only that code into its context, instead of loading whole directories on every request. It uses an OpenAI embedding model and a Zilliz Cloud (Milvus) vector database.
codebase-memory-mcp
codebase-memory-mcp is a server that reads a code repository and builds a persistent local knowledge graph of its symbols, files, documentation and infrastructure files. It exposes this graph through the Model Context Protocol (MCP) so an AI coding agent can ask structural questions, such as what calls a function or what depends on a file, instead of reading many files. Indexing and queries run on your own machine, and the README says no hosted service or API key is needed.
Best for
claude-context
- Developers working in large codebases where loading everything into an agent's context is impractical
- People already using Claude Code, Cursor, Gemini CLI, Codex CLI or similar MCP-compatible agents
- Teams comfortable creating OpenAI and Zilliz Cloud accounts and managing API keys
codebase-memory-mcp
- Developers who use an MCP-capable AI coding agent on a large codebase
- Engineers who want to find where a feature lives and how calls connect across files
- Teams that want to estimate which code and tests a change could affect
- People who prefer keeping source analysis on their own machine
Strengths
claude-context
- Uses semantic search, so it can find relevant code without the agent doing many rounds of file discovery
- Setup is a single configuration entry for each supported MCP client, documented in the README
- MIT licensed and actively maintained, with a large user base by star count
codebase-memory-mcp
- Runs locally with no hosted service or API key needed for indexing and queries, per the README
- Ships as a native binary; the README says no Docker or language runtime is required
- Integrates with many agent clients through an installer that writes their configuration
Not ideal for
claude-context
- People who want a fully local setup, since the README describes OpenAI embeddings and a Zilliz Cloud vector database rather than a local option
- Small projects where the agent can already read all the code without search
- Anyone unwilling to send code embeddings to third-party services
codebase-memory-mcp
- Users who want a hosted service or cloud-managed indexing, since the README describes local processing only
- People without an MCP-capable agent or CLI experience, since setup assumes some terminal comfort
- Projects needing change-aware test selection for languages beyond the supported C/C++ harnesses, since Smart CI is described as experimental and limited to supported C/C++ test harnesses
Trade-offs
claude-context
- Requires paid-or-signup accounts for an OpenAI API key and a Zilliz Cloud vector database, so ongoing costs and a third-party dependency are involved
- Code is sent to external embedding and vector services; review your privacy requirements before use
- The README is truncated here, so details on indexing behavior, limits and pricing are not confirmed in this analysis
codebase-memory-mcp
- CBM Lens, the visual companion, is in open beta (v0.0.1), and Smart CI is labeled experimental
- Support varies by agent client, so you should check the integration matrix for yours
- The README is truncated, so details such as full language coverage and exact limits are not confirmed here
Getting started
claude-context
Add the MCP server to Claude Code
claude mcp add claude-context -e OPENAI_API_KEY=sk-your-openai-api-key -e MILVUS_ADDRESS=your-zilliz-cloud-public-endpoint -e MILVUS_TOKEN=your-zilliz-cloud-api-key -- npx @zilliz/claude-context-mcp@latestcodebase-memory-mcp
Install the latest release (macOS / Linux)
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bashStart CBM Lens after installation
codebase-memory-mcp daemon start --open --port=9749Frequently asked questions
What is the difference between claude-context and codebase-memory-mcp?
claude-context: Semantic code search server that lets Claude Code and other AI agents pull relevant code from a whole repo. codebase-memory-mcp: A local tool that maps your code, docs and infrastructure into a graph so AI coding agents can query it. claude-context suits developers working in large codebases where loading everything into an agent's context is impractical, while codebase-memory-mcp suits developers who use an mcp-capable ai coding agent on a large codebase.
Are claude-context and codebase-memory-mcp free?
claude-context: Free and open source. codebase-memory-mcp: Free and open source.
Which is easier to set up, claude-context or codebase-memory-mcp?
claude-context is easier to get started with (rated beginner); codebase-memory-mcp is rated intermediate and usually needs more setup.
Is claude-context or codebase-memory-mcp more popular?
codebase-memory-mcp has more GitHub stars (48k) than claude-context (12.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to claude-context and codebase-memory-mcp?
Other options people compare them with include code-review-graph, wshobson/agents, Botpress, Desktop Commander, Portkey AI Gateway.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.