chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Pairings
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Open-source projects shipping the rest of the AI coding agent context layer. agentmemory ships persistent session memory. The projects below ship code-graph indexing, multi-agent build pipelines with dashboards, and broader knowledge graphs across non-code assets. Stack them and your agent gets a fuller picture in fewer tool calls.
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## [codegraph](https://github.com/colbymchenry/codegraph) — pre-indexed code knowledge graph (MCP)
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What it does: builds a SQLite-backed code knowledge graph (symbols, call edges, route handlers, full-text search) and exposes it through an MCP server with 8 tools (`codegraph_search`, `codegraph_context`, `codegraph_callers`, `codegraph_callees`, `codegraph_impact`, `codegraph_node`, `codegraph_status`, `codegraph_files`). File watcher keeps the index fresh. Their published benchmark (across VS Code, Django, Tokio, OkHttp, Gin, Alamofire, Excalidraw) shows agents finishing the same architecture question in **~35% less cost, ~70% fewer tool calls** when codegraph is wired in.
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Recipe with agentmemory — both as MCP servers on the same agent:
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```jsonc
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// ~/.claude.json or your agent's MCP config
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{
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"mcpServers": {
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"codegraph": {
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"type": "stdio",
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"command": "codegraph",
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"args": ["serve", "--mcp"]
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},
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"agentmemory": {
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"type": "stdio",
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"command": "npx",
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"args": ["@agentmemory/mcp"]
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}
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}
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}
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```
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Question routing that falls out:
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| Question | Tool the agent reaches for |
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|---|---|
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| "What does `shipctl helm install` call?" | `codegraph_callees` |
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| "Where is `auth_check` defined?" | `codegraph_node` |
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| "Which routes hit the user controller?" | `codegraph_callers` on the controller node |
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| "What did we decide last week about retries?" | `memory_smart_search` |
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| "Why did we pick async-std?" | `memory_recall` |
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| "Fix the auth bug from the post-mortem" | `memory_smart_search` → post-mortem session, then `codegraph_node` → current `auth.rs` |
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## [Understand Anything](https://github.com/Lum1104/Understand-Anything) — multi-agent code-graph pipeline + dashboard
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What it does: a Claude Code plugin (also installable on 13+ other agents) runs a multi-agent pipeline over a project and produces an interactive web dashboard at `understand-anything.com`. Architecture layers, business-flow domain view, guided tours, persona-adaptive UI, diff impact analysis, framework-aware routes across 14 frameworks. The graph commits to `.understand-anything/knowledge-graph.json` so teammates skip the pipeline.
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Slash commands:
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- `/understand` — build the graph (incremental on re-run)
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- `/understand-dashboard` — open the interactive web dashboard
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- `/understand-chat` — ask anything about the codebase
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- `/understand-diff` — analyze impact of current changes
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- `/understand-explain` — deep-dive a file or function
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- `/understand-onboard` — generate an onboarding guide
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- `/understand-domain` — extract business domains, flows, steps
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- `/understand-knowledge` — analyze an LLM wiki knowledge base
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Recipe with agentmemory:
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```bash
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# Day 1 — new team member joins
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/understand # builds the graph
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/understand-dashboard # opens the visual map
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/understand-onboard # generates onboarding guide
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# Week 2 — same engineer hits a bug
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/understand-explain src/auth.rs # architecture context
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# agentmemory MCP surfaces the post-mortem and the past 3 fixes touching auth.rs
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```
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The graph teaches you the codebase. agentmemory remembers what you and the agent already did inside it. The two planes don't overlap.
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## [Graphify](https://github.com/safishamsi/graphify) — broader knowledge graph across code, docs, PDFs, images, videos
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What it does: a single slash command (`/graphify .`) maps a whole project — application code, SQL schemas, R scripts, shell scripts, docs, PDFs, papers, images, videos — into one queryable knowledge graph. Output is three files: an interactive `graph.html`, a markdown `GRAPH_REPORT.md` with highlights and suggested questions, and the full `graph.json`. Also ships `graphify export callflow-html` for Mermaid call-flow architecture pages.
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Runs as a skill on Claude Code, Codex, OpenCode, Cursor, Gemini CLI, Copilot CLI, VS Code Copilot Chat, Aider, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, and Google Antigravity.
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Recipe with agentmemory:
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```bash
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/graphify . # one graph: code + docs + PDFs + images
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```
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This is the broadest sweep across artifacts that live alongside the code. agentmemory then captures everything the agent does while exploring that graph — the questions you asked, the conclusions, the decisions — so the next session opens with both the graph and the conversation history available.
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## How the four projects line up
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Four planes, four consumers, four update models. None of them try to do what the others do.
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| Project | Plane | Surface | Consumer | Update model |
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|---|---|---|---|---|
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| [agentmemory](https://github.com/rohitg00/agentmemory) | session history (observations, decisions, preferences) | MCP + REST + hooks | agent | live (observations stream) |
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| [codegraph](https://github.com/colbymchenry/codegraph) | code structure (symbols, call edges, routes) | MCP server | agent | live (FS watcher) |
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| [Understand Anything](https://github.com/Lum1104/Understand-Anything) | code structure + business domain + architecture | plugin (slash commands) + web dashboard | **human** + agent | on-demand `/understand` |
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| [Graphify](https://github.com/safishamsi/graphify) | code + docs + PDFs + images + videos | skill (slash command) | human + agent | on-demand `/graphify` |
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## Question types each project handles best
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| Question shape | Best tool |
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|---|---|
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| "What's the architecture of this repo?" | Understand-Anything dashboard or Graphify `graph.html` |
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| "Where is symbol X defined? Who calls it?" | codegraph (`codegraph_node`, `codegraph_callers`) |
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| "What does this PDF spec say about the rate limit?" | Graphify |
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| "Why did we pick X over Y three sessions ago?" | agentmemory (`memory_smart_search`) |
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| "What did we ship on April 8?" | agentmemory (`memory_timeline`) |
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| "How does the payment flow work in this codebase?" | Understand-Anything (`/understand-chat`) |
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| "Trace impact of changing `Foo::bar`" | codegraph (`codegraph_impact`) or Understand-Anything (`/understand-diff`) |
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| "What preferences has the team locked in?" | agentmemory |
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## Suggested install order for a brand-new project
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1. **agentmemory** — observe and persist from day one, even before the codebase has structure. Run `npx @agentmemory/agentmemory connect` and pick your agent.
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2. **codegraph** — once code lands, agent queries answer from the index instead of grepping. Run `npx @colbymchenry/codegraph`.
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3. **Understand Anything** *or* **Graphify** — when the codebase passes a few thousand LOC or starts shipping docs and PDFs alongside code, generate the graph for visual exploration and onboarding. Run `/plugin install understand-anything` or `uv tool install graphifyy && graphify install`.
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All four are local-first (no data leaves the machine for code-graph workloads). Stacking them costs nothing extra at the network boundary.
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## Cross-project benchmark idea
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`eval/runner/adapters/` accepts new adapters against the same coding-agent-life-v1 corpus and the published LongMemEval `_s` benchmark. A `codegraph` adapter or an `understand-anything` adapter or a `graphify` adapter would let us publish a side-by-side scorecard showing which project owns which question class. The win for the ecosystem is precise framing: each project gets credit for what it does best, with reproducible numbers from a shared harness.
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If you build any of those adapters, open a PR against `agentmemory` with the adapter file under `eval/runner/adapters/` and a scorecard under `docs/benchmarks/`. The scaffold and contract live in [`eval/README.md`](../../eval/README.md).
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