Codebase Memory

AI 与智能体

by deusdata

持久化的代码库知识图谱,可跨会话保留上下文,在 session 重启或上下文压缩后仍能继续使用。

专治 AI 编程助手“会话失忆”,把代码库沉淀为持久知识图谱,重启或压缩上下文后也能无缝续上开发状态。

2.4kGitHub

什么是 Codebase Memory

持久化的代码库知识图谱,可跨会话保留上下文,在 session 重启或上下文压缩后仍能继续使用。

README

codebase-memory-mcp

GitHub Release License CI Tests Languages Agents Pure C Platform OpenSSF Scorecard SLSA 3 VirusTotal arXiv

The fastest and most efficient code intelligence engine for AI coding agents. Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a single static binary for macOS, Linux, and Windows — download, run install, done.

High-quality parsing through tree-sitter AST analysis across all 155 languages, enhanced with LSP-style hybrid type resolution for Go, C, C++, and TypeScript / JavaScript / JSX / TSX (more languages coming soon) — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 14 MCP tools. Zero dependencies. Plug and play across 11 coding agents.

Research — The design and benchmarks behind this project are described in the preprint Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP (arXiv:2603.27277). Evaluated across 31 real-world repositories: 83% answer quality, 10× fewer tokens, 2.1× fewer tool calls vs. file-by-file exploration.

Security & Trust — This tool reads your codebase and writes to your agent configuration files. That is what it is designed to do. If you prefer to audit before running, the full source is here — every release binary is signed, checksummed, and scanned by 70+ antivirus engines. All processing happens 100% locally; your code never leaves your machine. Found a security issue? We want to know — see SECURITY.md. Security is Priority #1 for us.

<p align="center"> <img src="docs/graph-ui-screenshot.png" alt="Graph visualization UI showing the codebase-memory-mcp knowledge graph" width="800"> <br> <em>Built-in 3D graph visualization (UI variant) — explore your knowledge graph at localhost:9749</em> </p>

Why codebase-memory-mcp

  • Extreme indexing speed — Linux kernel (28M LOC, 75K files) in 3 minutes. RAM-first pipeline: LZ4 compression, in-memory SQLite, fused Aho-Corasick pattern matching. Memory released after indexing.
  • Plug and play — single static binary for macOS (arm64/amd64), Linux (arm64/amd64), and Windows (amd64). No Docker, no runtime dependencies, no API keys. Download → install → restart agent → done.
  • 155 languages — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.
  • 120x fewer tokens — 5 structural queries: ~3,400 tokens vs ~412,000 via file-by-file search. One graph query replaces dozens of grep/read cycles.
  • 11 agents, one commandinstall auto-detects Claude Code, Codex CLI, Gemini CLI, Zed, OpenCode, Antigravity, Aider, KiloCode, VS Code, OpenClaw, and Kiro — configures MCP entries, instruction files, and pre-tool hooks for each.
  • Built-in graph visualization — 3D interactive UI at localhost:9749 (optional UI binary variant).
  • Infrastructure-as-code indexing — Dockerfiles, Kubernetes manifests, and Kustomize overlays indexed as graph nodes with cross-references. Resource nodes for K8s kinds, Module nodes for Kustomize overlays with IMPORTS edges to referenced resources.
  • 14 MCP tools — search, trace, architecture, impact analysis, Cypher queries, dead code detection, cross-service HTTP linking, ADR management, and more.

Quick Start

One-line install (macOS / Linux):

bash
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash

With graph visualization UI:

bash
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash -s -- --ui

Windows (PowerShell):

powershell
# 1. Download the installer
Invoke-WebRequest -Uri https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.ps1 -OutFile install.ps1

# 2. (Optional but recommended) Inspect the script
notepad install.ps1

# 3. Run it
.\install.ps1

Options: --ui (graph visualization), --skip-config (binary only, no agent setup), --dir=<path> (custom location).

Restart your coding agent. Say "Index this project" — done.

<details> <summary>Manual install</summary>
  1. Download the archive for your platform from the latest release:

    • codebase-memory-mcp-<os>-<arch>.tar.gz (macOS/Linux) or .zip (Windows) — standard
    • codebase-memory-mcp-ui-<os>-<arch>.tar.gz / .zip — with graph visualization
  2. Extract and install (each archive includes install.sh or install.ps1):

    macOS / Linux:

    bash
    tar xzf codebase-memory-mcp-*.tar.gz
    ./install.sh
    

    Windows (PowerShell):

    powershell
    Expand-Archive codebase-memory-mcp-windows-amd64.zip -DestinationPath .
    .\install.ps1
    
  3. Restart your coding agent.

The install command automatically strips macOS quarantine attributes and ad-hoc signs the binary — no manual xattr/codesign needed.

</details>

The install command auto-detects all installed coding agents and configures MCP server entries, instruction files, skills, and pre-tool hooks for each.

Graph Visualization UI

If you downloaded the ui variant:

bash
codebase-memory-mcp --ui=true --port=9749

Open http://localhost:9749 in your browser. The UI runs as a background thread alongside the MCP server — it's available whenever your agent is connected.

Auto-Index

Enable automatic indexing on MCP session start:

bash
codebase-memory-mcp config set auto_index true

When enabled, new projects are indexed automatically on first connection. Previously-indexed projects are registered with the background watcher for ongoing git-based change detection. Configurable file limit: config set auto_index_limit 50000.

Keeping Up to Date

bash
codebase-memory-mcp update

The MCP server also checks for updates on startup and notifies on the first tool call if a newer release is available.

Uninstall

bash
codebase-memory-mcp uninstall

Removes all agent configs, skills, hooks, and instructions. Does not remove the binary or SQLite databases.

Features

Graph & analysis

  • Architecture overview: get_architecture returns languages, packages, entry points, routes, hotspots, boundaries, layers, and clusters in a single call
  • Architecture Decision Records: manage_adr persists architectural decisions across sessions
  • Louvain community detection: Discovers functional modules by clustering call edges
  • Git diff impact mapping: detect_changes maps uncommitted changes to affected symbols with risk classification
  • Call graph: Resolves function calls across files and packages (import-aware, type-inferred)
  • Dead code detection: Finds functions with zero callers, excluding entry points
  • Cypher-like queries: MATCH (f:Function)-[:CALLS]->(g) WHERE f.name = 'main' RETURN g.name

Search

  • Semantic search (semantic_query): vector search across the entire graph, powered by bundled Nomic nomic-embed-code embeddings (40K tokens, 768d int8) compiled into the binary — no API key, no Ollama, no Docker. 11-signal combined scoring (TF-IDF, RRI, API/Type/Decorator signatures, AST profiles, data flow, Halstead-lite, MinHash, module proximity, graph diffusion).
  • BM25 full-text search via SQLite FTS5 with cbm_camel_split tokenizer (camelCase / snake_case aware)
  • Structural search (search_graph): regex name patterns, label filters, min/max degree, file scoping
  • Code search (search_code): graph-augmented grep over indexed files only

Cross-service linking

  • HTTP route ↔ call-site matching with confidence scoring
  • gRPC, GraphQL, tRPC service detection with protobuf Route extraction
  • Channel detection (EMITS / LISTENS_ON) for Socket.IO, EventEmitter, and generic pub-sub patterns across 8 languages with constant resolution

Cross-repo intelligence

  • CROSS_* edges link nodes across multiple repos indexed under the same store
  • Multi-galaxy 3D UI layout for cross-repo architecture visualization
  • Cross-repo architecture summary combining services, routes, and dependencies across the indexed fleet

Edge types (selected)

  • CALLS, IMPORTS, DEFINES, IMPLEMENTS, INHERITS
  • HTTP_CALLS, ASYNC_CALLS (cross-service)
  • EMITS, LISTENS_ON (channels)
  • DATA_FLOWS with arg-to-param mapping + field access chains
  • SIMILAR_TO (MinHash + LSH near-clone detection, Jaccard scored)
  • SEMANTICALLY_RELATED (vocabulary-mismatch, same-language, score ≥ 0.80)

Indexing pipeline

  • 155 vendored tree-sitter grammars compiled into the binary
  • Generic package / module resolution — bare specifiers like @myorg/pkg, github.com/foo/bar, use my_crate::foo resolved via manifest scanning (package.json, go.mod, Cargo.toml, pyproject.toml, composer.json, pubspec.yaml, pom.xml, build.gradle, mix.exs, *.gemspec)
  • Infrastructure-as-code indexing — Dockerfiles, Kubernetes manifests, Kustomize overlays as graph nodes
  • LSP-style hybrid type resolution for Go, C, C++, and TypeScript / JavaScript / JSX / TSX (more languages coming) — clean-room reimplementation of tsserver / typescript-go's type resolution algorithms (parameter binding, return-type inference, generic substitution, JSX component dispatch, JSDoc inference for plain JS files)
  • RAM-first pipeline: LZ4 compression, in-memory SQLite, single dump at end. Memory released after.

Distribution & operation

  • Single static binary, zero infrastructure: SQLite-backed, persists to ~/.cache/codebase-memory-mcp/
  • Auto-sync: Background watcher detects file changes and re-indexes automatically
  • Route nodes: REST endpoints are first-class graph entities
  • CLI mode: codebase-memory-mcp cli search_graph '{"name_pattern": ".*Handler.*"}'
  • Available on: npm, PyPI, Homebrew, Scoop, Winget, Chocolatey, AUR, go install

Team-Shared Graph Artifact

Commit a single compressed file to your repo and your teammates skip the reindex.

.codebase-memory/graph.db.zst is a zstd-compressed snapshot of the knowledge graph that lives next to your source. When you index, the artifact is written or refreshed; when a teammate clones the repo and runs codebase-memory-mcp for the first time, the artifact is decompressed and incremental indexing fills in their local diff.

  • Format: SQLite database, indexes stripped, VACUUM INTO compacted, then zstd 1.5.7 compressed (8–13:1 ratio typical)
  • Two tiers:
    • Best (zstd -9 + index strip + VACUUM INTO) — written on explicit index_repository
    • Fast (zstd -3) — written by the watcher for low-latency incremental updates
  • Bootstrap: when no local DB exists but the artifact is present, index_repository imports the artifact first, then runs incremental indexing — avoiding the full reindex cost
  • No merge pain: a .gitattributes line with merge=ours is auto-created on first export, so concurrent edits don't produce conflicts on the binary artifact
  • Optional: never committed unless you want it. Add .codebase-memory/ to .gitignore if you prefer everyone to reindex from scratch.

The result is similar in spirit to graphify's graphify-out/ directory, but as a single compressed file with explicit two-tier export, integrity-checked import, and zero merge friction.

How It Works

codebase-memory-mcp is a structural analysis backend — it builds and queries the knowledge graph. It does not include an LLM. Instead, it relies on your MCP client (Claude Code, or any MCP-compatible agent) to be the intelligence layer.

code
You: "what calls ProcessOrder?"

Agent calls: trace_call_path(function_name="ProcessOrder", direction="inbound")

codebase-memory-mcp: executes graph query, returns structured results

Agent: presents the call chain in plain English

Why no built-in LLM? Other code graph tools embed an LLM for natural language → graph query translation. This means extra API keys, extra cost, and another model to configure. With MCP, the agent you're already talking to is the query translator.

Performance

Benchmarked on Apple M3 Pro:

OperationTimeNotes
Linux kernel full index3 min28M LOC, 75K files → 2.1M nodes, 4.9M edges
Linux kernel fast index1m 12s1.88M nodes
Django full index~6s49K nodes, 196K edges
Cypher query<1msRelationship traversal
Name search (regex)<10msSQL LIKE pre-filtering
Dead code detection~150msFull graph scan with degree filtering
Trace call path (depth=5)<10msBFS traversal

RAM-first pipeline: All indexing runs in memory (LZ4 HC compressed read, in-memory SQLite, single dump at end). Memory is released back to the OS after indexing completes.

Token efficiency: Five structural queries consumed ~3,400 tokens via codebase-memory-mcp versus ~412,000 tokens via file-by-file grep exploration — a 99.2% reduction.

Installation

Pre-built Binaries

PlatformStandardWith Graph UI
macOS (Apple Silicon)codebase-memory-mcp-darwin-arm64.tar.gzcodebase-memory-mcp-ui-darwin-arm64.tar.gz
macOS (Intel)codebase-memory-mcp-darwin-amd64.tar.gzcodebase-memory-mcp-ui-darwin-amd64.tar.gz
Linux (x86_64)codebase-memory-mcp-linux-amd64.tar.gzcodebase-memory-mcp-ui-linux-amd64.tar.gz
Linux (ARM64)codebase-memory-mcp-linux-arm64.tar.gzcodebase-memory-mcp-ui-linux-arm64.tar.gz
Windows (x86_64)codebase-memory-mcp-windows-amd64.zipcodebase-memory-mcp-ui-windows-amd64.zip

Every release includes checksums.txt with SHA-256 hashes. All binaries are statically linked — no shared library dependencies.

Windows note: SmartScreen may show a warning for unsigned software. Click "More info""Run anyway". Verify integrity with checksums.txt.

Setup Scripts

<details> <summary>Automated download + install</summary>

macOS / Linux:

bash
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/scripts/setup.sh | bash

Windows (PowerShell):

powershell
irm https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/scripts/setup-windows.ps1 | iex
</details>

AUR (Arch Linux)

bash
yay -S codebase-memory-mcp-bin
bash
paru -S codebase-memory-mcp-bin

The codebase-memory-mcp-bin package is available at: https://aur.archlinux.org/packages/codebase-memory-mcp-bin

Install via Claude Code

code
You: "Install this MCP server: https://github.com/DeusData/codebase-memory-mcp"

Build from Source

<details> <summary>Prerequisites: C compiler + zlib</summary>
RequirementCheckInstall
C compiler (gcc or clang)gcc --version or clang --versionmacOS: xcode-select --install, Linux: apt install build-essential
C++ compilerg++ --version or clang++ --versionSame as above
zlibmacOS: included, Linux: apt install zlib1g-dev
Gitgit --versionPre-installed on most systems
</details>
bash
git clone https://github.com/DeusData/codebase-memory-mcp.git
cd codebase-memory-mcp
scripts/build.sh                    # standard binary
scripts/build.sh --with-ui          # with graph visualization
# Binary at: build/c/codebase-memory-mcp

Manual MCP Configuration

<details> <summary>If you prefer not to use the install command</summary>

Add to ~/.claude/.mcp.json (global) or project .mcp.json:

json
{
  "mcpServers": {
    "codebase-memory-mcp": {
      "command": "/path/to/codebase-memory-mcp",
      "args": []
    }
  }
}

Restart your agent. Verify with /mcp — you should see codebase-memory-mcp with 14 tools.

</details>

Multi-Agent Support

install auto-detects and configures all installed agents:

AgentMCP ConfigInstructionsHooks
Claude Code.claude/.mcp.json4 SkillsPreToolUse (Grep/Glob/Read reminder)
Codex CLI.codex/config.toml.codex/AGENTS.md
Gemini CLI.gemini/settings.json.gemini/GEMINI.mdBeforeTool (grep/read reminder)
Zedsettings.json (JSONC)
OpenCodeopencode.jsonAGENTS.md
Antigravitymcp_config.jsonAGENTS.md
AiderCONVENTIONS.md
KiloCodemcp_settings.json~/.kilocode/rules/
VS CodeCode/User/mcp.json
OpenClawopenclaw.json
Kiro.kiro/settings/mcp.json

Hooks are advisory (exit code 0) — they remind agents to prefer MCP graph tools when they reach for grep/glob/read, without blocking the tool call.

CLI Mode

Every MCP tool can be invoked from the command line:

bash
codebase-memory-mcp cli index_repository '{"repo_path": "/path/to/repo"}'
codebase-memory-mcp cli search_graph '{"name_pattern": ".*Handler.*", "label": "Function"}'
codebase-memory-mcp cli trace_call_path '{"function_name": "Search", "direction": "both"}'
codebase-memory-mcp cli query_graph '{"query": "MATCH (f:Function) RETURN f.name LIMIT 5"}'
codebase-memory-mcp cli list_projects
codebase-memory-mcp cli --raw search_graph '{"label": "Function"}' | jq '.results[].name'

MCP Tools

Indexing

ToolDescription
index_repositoryIndex a repository into the graph. Auto-sync keeps it fresh after that.
list_projectsList all indexed projects with node/edge counts.
delete_projectRemove a project and all its graph data.
index_statusCheck indexing status of a project.

Querying

ToolDescription
search_graphStructured search by label, name pattern, file pattern, degree filters. Pagination via limit/offset.
trace_call_pathBFS traversal — who calls a function and what it calls. Depth 1-5.
detect_changesMap git diff to affected symbols + blast radius with risk classification.
query_graphExecute Cypher-like graph queries (read-only).
get_graph_schemaNode/edge counts, relationship patterns, property definitions per label. Run this first.
get_code_snippetRead source code for a function by qualified name.
get_architectureCodebase overview: languages, packages, routes, hotspots, clusters, ADR.
search_codeGrep-like text search within indexed project files.
manage_adrCRUD for Architecture Decision Records.
ingest_tracesIngest runtime traces to validate HTTP_CALLS edges.

Graph Data Model

Node Labels

Project, Package, Folder, File, Module, Class, Function, Method, Interface, Enum, Type, Route, Resource

Edge Types

CONTAINS_PACKAGE, CONTAINS_FOLDER, CONTAINS_FILE, DEFINES, DEFINES_METHOD, IMPORTS, CALLS, HTTP_CALLS, ASYNC_CALLS, IMPLEMENTS, HANDLES, USAGE, CONFIGURES, WRITES, MEMBER_OF, TESTS, USES_TYPE, FILE_CHANGES_WITH

Qualified Names

get_code_snippet uses qualified names: <project>.<path_parts>.<name>. Use search_graph to discover them first.

Supported Cypher Subset

query_graph supports: MATCH with labels and relationship types, variable-length paths, WHERE with comparisons/regex/CONTAINS, RETURN with property access and COUNT/DISTINCT, ORDER BY, LIMIT. Not supported: WITH, COLLECT, OPTIONAL MATCH, mutations.

Ignoring Files

Layered: hardcoded patterns (.git, node_modules, etc.) → .gitignore hierarchy → .cbmignore (project-specific, gitignore syntax). Symlinks are always skipped.

Configuration

bash
codebase-memory-mcp config list                          # show all settings
codebase-memory-mcp config set auto_index true           # auto-index on session start
codebase-memory-mcp config set auto_index_limit 50000    # max files for auto-index
codebase-memory-mcp config reset auto_index              # reset to default

Environment Variables

VariableDefaultDescription
CBM_CACHE_DIR~/.cache/codebase-memory-mcpOverride the database storage directory. All project indexes and config are stored here.
CBM_DIAGNOSTICSfalseSet to 1 or true to enable periodic diagnostics output to /tmp/cbm-diagnostics-<pid>.json.
CBM_DOWNLOAD_URL(GitHub releases)Override the download URL for updates. Used for testing or self-hosted deployments.
bash
# Store indexes in a custom directory
export CBM_CACHE_DIR=~/my-projects/cbm-data

Custom File Extensions

Map additional file extensions to supported languages via JSON config files. Useful for framework-specific extensions like .blade.php (Laravel) or .mjs (ES modules).

Per-project (in your repo root):

json
// .codebase-memory.json
{"extra_extensions": {".blade.php": "php", ".mjs": "javascript"}}

Global (applies to all projects):

json
// ~/.config/codebase-memory-mcp/config.json  (or $XDG_CONFIG_HOME/...)
{"extra_extensions": {".twig": "html", ".phtml": "php"}}

Project config overrides global for conflicting extensions. Unknown language values are silently skipped. Missing config files are ignored.

Persistence

SQLite databases stored at ~/.cache/codebase-memory-mcp/. Persists across restarts (WAL mode, ACID-safe). To reset: rm -rf ~/.cache/codebase-memory-mcp/.

Troubleshooting

ProblemFix
/mcp doesn't show the serverCheck .mcp.json path is absolute. Restart agent. Test: echo '{}' | /path/to/binary should output JSON.
index_repository failsPass absolute path: index_repository(repo_path="/absolute/path")
trace_call_path returns 0 resultsUse search_graph(name_pattern=".*PartialName.*") first to find the exact name.
Queries return wrong project resultsAdd project="name" parameter. Use list_projects to see names.
Binary not found after installAdd to PATH: export PATH="$HOME/.local/bin:$PATH"
UI not loadingEnsure you downloaded the ui variant and ran --ui=true. Check http://localhost:9749.

Language Support

155 languages, all parsed via vendored tree-sitter grammars compiled into the binary. Benchmarked against 64 real open-source repositories (78 to 49K nodes):

TierScoreLanguages
Excellent (>= 90%)Lua, Kotlin, C++, Perl, Objective-C, Groovy, C, Bash, Zig, Swift, CSS, YAML, TOML, HTML, SCSS, HCL, Dockerfile
Good (75-89%)Python, TypeScript, TSX, Go, Rust, Java, R, Dart, JavaScript, Erlang, Elixir, Scala, Ruby, PHP, C#, SQL
Functional (< 75%)OCaml, Haskell

Also supported (not yet benchmarked): Ada, Agda, Apex, Assembly (NASM), Astro, AWK, Beancount, BibTeX, Bicep, Bitbake, Blade, Cairo, Cap'n Proto, Clojure, CMake, COBOL, Common Lisp, Crystal, CSV, CUDA, D, Devicetree, Diff, .env, Elm, Emacs Lisp, F#, Fennel, Fish, FORM, Fortran, FunC, GDScript, .gitattributes, .gitignore, Gleam, GLSL, GN, Go module, Go template, GraphQL, Hare, HLSL, Hyprlang, INI, ISPC, Janet, Jinja2, JSDoc, JSON, JSON5, Jsonnet, Julia, Just, Kconfig, KDL, Lean 4, Linker Script, Liquid, LLVM IR, Luau, Magma, Makefile, Markdown, MATLAB, Mermaid, Meson, Move, Nickel, Nim, Nix, Odin, Pascal, Pkl, PO (gettext), Pony, PowerShell, Prisma, .properties, Protobuf, Puppet, PureScript, Racket, Regex, requirements.txt, ReScript, RON, reStructuredText, Scheme, Slang, Smali, Smithy, Solidity, SOQL, SOSL, Squirrel, SSH config, Starlark, Svelte, Sway, SystemVerilog, TableGen, Tcl, Teal, Templ, Thrift, TLA+, Typst, Verilog, VHDL, Vim script, Vue, WGSL, WIT, Wolfram, XML, Zsh.

Architecture

code
src/
  main.c              Entry point (MCP stdio server + CLI + install/update/config)
  mcp/                MCP server (14 tools, JSON-RPC 2.0, session detection, auto-index)
  cli/                Install/uninstall/update/config (10 agents, hooks, instructions)
  store/              SQLite graph storage (nodes, edges, traversal, search, Louvain)
  pipeline/           Multi-pass indexing (structure → definitions → calls → HTTP links → config → tests)
  cypher/             Cypher query lexer, parser, planner, executor
  discover/           File discovery (.gitignore, .cbmignore, symlink handling)
  watcher/            Background auto-sync (git polling, adaptive intervals)
  traces/             Runtime trace ingestion
  ui/                 Embedded HTTP server + 3D graph visualization
  foundation/         Platform abstractions (threads, filesystem, logging, memory)
internal/cbm/         Vendored tree-sitter grammars (155 languages) + AST extraction engine

Security

Every release binary is verified through a multi-layer pipeline before publication:

  • VirusTotal — all binaries scanned by 70+ antivirus engines (zero detections required to publish)
  • SLSA Level 3 — cryptographic build provenance generated by GitHub Actions; verify with gh attestation verify <file> --repo DeusData/codebase-memory-mcp
  • Sigstore cosign — keyless signatures on all artifacts; bundles included in every release
  • SHA-256 checksumschecksums.txt published with every release; verified by both install scripts before extraction
  • CodeQL SAST — blocks release pipeline if any open alerts remain
  • Zero runtime dependencies — no transitive supply chain; all libraries vendored at compile time

v0.6.1 VirusTotal scans

BinarySHA-256VirusTotal
linux-amd647e6624b345f994afb901...0/72 ✅
linux-arm64ac2498c45235c1bf37f8...0/72 ✅
darwin-arm643e72c8cb364c431d99f1...0/72 ✅
darwin-amd647836878876c8956f6413...0/72 ✅
windows-amd64d773be23ed0823d58677...0/72 ✅

Scan links for every release are also included in the GitHub Release notes automatically.

License

MIT

常见问题

Codebase Memory 是什么?

持久化的代码库知识图谱,可跨会话保留上下文,在 session 重启或上下文压缩后仍能继续使用。

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Sequential Thinking 是让 AI 通过动态思维链解决复杂问题的参考服务器。

这个服务器展示了如何让 Claude 像人类一样逐步推理,适合开发者学习 MCP 的思维链实现。但注意它只是个参考示例,别指望直接用在生产环境里。

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PraisonAI

编辑精选

by mervinpraison

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PraisonAI 是一个支持自反思和多 LLM 的低代码 AI 智能体框架。

如果你需要快速搭建一个能 24/7 运行的 AI 智能体团队来处理复杂任务(比如自动研究或代码生成),PraisonAI 的低代码设计和多平台集成(如 Telegram)让它上手极快。但作为非官方项目,它的生态成熟度可能不如 LangChain 等主流框架,适合愿意尝鲜的开发者。

AI 与智能体
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