Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Agent memory on your own Postgres that flags superseded facts and abstains instead of guessing.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
RECALL_SERVING_DSN, RECALL_TENANT, RECALL_TRUST_MODE, RECALL_EMBEDDER, RECALL_TABLE, RECALL_MCP_TOOLS비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"re-call": {
"command": "uvx",
"args": [
"recall-rag"
],
"env": {
"RECALL_SERVING_DSN": "your-value",
"RECALL_TENANT": "your-value",
"RECALL_TRUST_MODE": "your-value",
"RECALL_EMBEDDER": "your-value",
"RECALL_TABLE": "your-value",
"RECALL_MCP_TOOLS": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add re-call --env RECALL_SERVING_DSN=your-value --env RECALL_TENANT=your-value --env RECALL_TRUST_MODE=your-value --env RECALL_EMBEDDER=your-value --env RECALL_TABLE=your-value --env RECALL_MCP_TOOLS=your-value -- uvx recall-rag실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"re-call": {
"command": "uvx",
"args": [
"recall-rag"
],
"env": {
"RECALL_SERVING_DSN": "your-value",
"RECALL_TENANT": "your-value",
"RECALL_TRUST_MODE": "your-value",
"RECALL_EMBEDDER": "your-value",
"RECALL_TABLE": "your-value",
"RECALL_MCP_TOOLS": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"re-call": {
"type": "stdio",
"command": "uvx",
"args": [
"recall-rag"
],
"env": {
"RECALL_SERVING_DSN": "your-value",
"RECALL_TENANT": "your-value",
"RECALL_TRUST_MODE": "your-value",
"RECALL_EMBEDDER": "your-value",
"RECALL_TABLE": "your-value",
"RECALL_MCP_TOOLS": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
RECALL_TENANT envRECALL_TRUST_MODE envRECALL_EMBEDDER envRECALL_TABLE envRECALL_MCP_TOOLS env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 GiulioDER 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: RECALL_SERVING_DSN
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.14.0)은 42일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Memory that abstains instead of guessing.
RE-call is agent memory in a local SQLite file or your own PostgreSQL with pgvector. Each result carries a verdict, confidence, and provenance, while unsupported questions are refused instead of answered by the nearest match.
What it is · For agents · How it works · Start with Lite · Install and integrate
RE-call is agent memory in a local SQLite file or your own PostgreSQL database. It indexes source documents and keeps validity, lineage, confidence, and provenance attached to every result.
Vector search returns nearby text. RE-call also checks whether that text is current, supported, and trustworthy enough for the query. A superseded claim is marked superseded; a result that does not clear the calibrated trust gate becomes ABSTAIN with a reason. Declared supersession makes the current memory win over stale but similar memory.
The default path uses local embeddings plus hybrid dense and keyword retrieval. PostgreSQL uses its full text search; Lite uses SQLite FTS5. It needs no memory-layer LLM call. Additional retrieval, reasoning, and structured-fact modules are optional.
For the agent reading this, RE-call for agents explains the experience loop and how to use persistent memory as a trustworthy past rather than an unverified scratchpad.
On PostgreSQL, RE-call builds an immutable, calibrated generation and serves queries against that pinned generation. Lite binds calibration to its single-file corpus. Every search, including the reasoning and fact paths, goes through trusted search and the trust gate.
The diagram is interactive on the project site: click any box to see the source lines it describes. A step by step view of one recall_search call follows a single search through its refusal, abstention and answer paths. Both are generated from the sources in docs/diagrams.
Text version of the diagram, with each box linked to its file
Solid arrows are the default path. Dashed arrows are opt in or reasoning only: the reranker, graph expansion, and the answer provider. Each box links to the file that implements it.
flowchart LR
subgraph group_entry["Entry points"] node_cli["recall CLI commands"] node_mcp["MCP server tool handlers"] node_sdk["Agent SDK in process"] end
subgraph group_build["Generation build"] node_manifest["Source manifest immutable input"] node_generations["Generation manager build, validate, promote"] node_parser["Parse and chunk documents to chunks"] node_embedder["Embedder local or hosted API"] node_graph_build["Semantic graph entities, relations"] node_calibration["Calibration certified threshold"] end
subgraph group_serve["Trusted retrieval"] node_trusted_search["Trusted search query orchestrator"] node_gen_store["Generation store pinned generation"] node_retriever["Hybrid retriever dense, full text, RRF"] node_reranker["Reranker opt in"] node_trust_gate{{"Trust gate verdict or ABSTAIN"}} end
subgraph group_reason["Reasoning"] node_reasoner["Reasoning query evidence bundle, plan"] node_graph_expansion["Graph expansion one hop, before trust"] node_answer["Answer provider opt in LLM"] end
subgraph group_facts["Provenance and facts"] node_provenance["Provenance controller fact review"] node_ledger["Fact ledger append only"] end
node_caller(("Agent or user caller")) node_postgres[("PostgreSQL + pgvector generations, chunks, graph")]
node_caller -->|"runs"| node_cli node_caller -->|"tool calls"| node_mcp node_caller -->|"imports"| node_sdk node_sdk -->|"reuses handlers"| node_mcp node_cli -->|"generation build"| node_generations node_mcp -->|"ingest uploads"| node_generations node_generations -->|"reads sources"| node_manifest node_generations -->|"parses, chunks"| node_parser node_generations -->|"embeds chunks"| node_embedder node_generations -->|"builds graph"| node_graph_build node_generations -->|"requires certified"| node_calibration node_generations -->|"writes generation"| node_postgres node_graph_build -->|"writes graph"| node_postgres node_cli -->|"calibrate, publish"| node_calibration node_calibration -->|"stores threshold"| node_postgres node_cli -->|"search"| node_trusted_search node_mcp -->|"search, evidence"| node_trusted_search node_trusted_search -->|"pins generation"| node_gen_store node_gen_store -->|"reads active"| node_postgres node_trusted_search -->|"retrieves"| node_retriever node_retriever -->|"embeds query"| node_embedder node_retriever -->|"dense, full text"| node_gen_store node_retriever -.->|"reorders"| node_reranker node_trusted_search -->|"evaluates"| node_trust_gate node_mcp -->|"reasoning query"| node_reasoner node_reasoner -->|"retrieves"| node_trusted_search node_trusted_search -.->|"expands pre trust"| node_graph_expansion node_graph_expansion -.->|"same generation graph"| node_postgres node_reasoner -.->|"cited answer"| node_answer node_mcp -->|"apply fact"| node_provenance node_provenance -->|"fresh search"| node_trusted_search node_provenance -->|"appends"| node_ledger node_ledger -->|"stores"| node_postgres
click node_cli "https://github.com/GiulioDER/RE-call/blob/master/recall/cli.py" click node_mcp "https://github.com/GiulioDER/RE-call/tree/master/recall_mcp" click node_sdk "https://github.com/GiulioDER/RE-call/blob/master/recall_agent/memory.py" click node_manifest "https://github.com/GiulioDER/RE-call/blob/master/recall/manifest.py" click node_generations "https://github.com/GiulioDER/RE-call/blob/master/recall/generations.py" click node_parser "https://github.com/GiulioDER/RE-call/blob/master/recall/document.py" click node_embedder "https://github.com/GiulioDER/RE-call/blob/master/recall/embeddings.py" click node_graph_build "https://github.com/GiulioDER/RE-call/blob/master/recall/semantic_graph/_build.py" click node_calibration "https://github.com/GiulioDER/RE-call/blob/master/recall/calibration_v2.py" click node_trusted_search "https://github.com/GiulioDER/RE-call/blob/master/recall/trust.py" click node_gen_store "https://github.com/GiulioDER/RE-call/blob/master/recall/generation_store.py" click node_retriever "https://github.com/GiulioDER/RE-call/blob/master/recall/retriever.py" click node_reranker "https://github.com/GiulioDER/RE-call/blob/master/recall/rerank.py" click node_trust_gate "https://github.com/GiulioDER/RE-call/blob/master/recall/trust.py" click node_reasoner "https://github.com/GiulioDER/RE-call/blob/master/recall/reasoning.py" click node_graph_expansion "https://github.com/GiulioDER/RE-call/blob/master/recall_mcp/graph_expansion.py" click node_answer "https://github.com/GiulioDER/RE-call/blob/master/recall/answer_provider.py" click node_provenance "https://github.com/GiulioDER/RE-call/blob/master/recall/provenance_controller.py" click node_ledger "https://github.com/GiulioDER/RE-call/blob/master/recall/fact_ledger.py" click node_postgres "https://github.com/GiulioDER/RE-call/tree/master/recall/migrations"
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Ordinary recall search follows the direct path. Explicit reasoning accepts graph_expansion: auto is the default and resolves to bounded one hop expansion for every nonempty query; off keeps direct retrieval only; one-hop forces the graph path. The CLI uses --graph-expansion auto|off|one-hop; the MCP tool uses graph_expansion="auto"|"off"|"one_hop". Graph neighbors are generation bound, direct candidates remain first, and expanded candidates must clear the same trust boundary before they can support a cited answer.
The opt in choices attach to different points in the system:
| Optional capability | Where it fits | What it adds |
|---|---|---|
| Hosted embedder | Build and query | Remote model calls for embeddings. Query and corpus text may leave the environment. |
| Learned sparse retrieval, SPLADE | Hybrid retrieval | A learned term weighted retrieval leg in addition to dense vectors and Postgres full text. |
| Reranker | After candidate fusion | Reorders the fused candidates with a cross encoder. |
| Evidence Graph version one | Explicit reasoning retrieval | Adds bounded, generation-bound structural neighbors to reasoning retrieval. See graph_expansion above for controls; graph candidates pass through trust before a cited answer can use them. |
| Structured fact application | Evidence cards | Lets a reviewed fact pass through the provenance controller into the append only ledger. |
For details, see the architecture writeup, provenance controller, and API reference.
For one project's memory on your machine, start with RE-call Lite. It stores memory in .recall/memory.db, refreshes changed memos automatically, and needs no Docker or database server.
Install 0.15.0 or later: Lite ships in the PyPI wheel from 0.15.0; earlier wheels do not contain it. Follow the Lite installation guide to create an environment and install the package, then run this from your own project with an existing folder of notes:
recall앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요