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पब्लिशर की अपनी कमांड। PlugMCP एंडपॉइंट नहीं।
Host a copy with our keys and logs.
npx -y [email protected]Claude Code में लोकल कमांड के लिए इंस्टॉल लिंक नहीं है। नीचे दी गई कमांड कॉपी करें और टर्मिनल में चलाएँ।
claude mcp add keshrath-agent-knowledge -- npx -y agent-knowledge@3.0.0टर्मिनल में चलाएँ।
{ "mcpServers": { "keshrath-agent-knowledge": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
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PlugMCP पर चलाएँ
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
लॉन्चपब्लिशर से
Cross-session memory and recall for AI coding assistants -- works with Claude Code, Cursor, OpenCode, Cline, Continue.dev, and Aider out of the box. Git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing.
Cross-session memory and recall for AI agents — git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing
Benchmark: R@5 = 97.2% (sparse) / 98.8% (hybrid) on longmemeval_s and 86.0% (sparse) / 88.4% (hybrid) on the harder longmemeval_m split — the public LongMemEval academic benchmark (Wu et al. 2024, ICLR 2025), full 500 questions per split, no LLM, no API key, runs entirely offline. +8.6pp to +13.2pp R@5 over the paper's official flat-bm25 baseline in apples-to-apples reproduction. Full per-category table, reproduction instructions, and paper-comparison details in bench/README.md.
In Claude Code, the agent-knowledge plugin adds the /knowledge pane: the knowledge base beside the conversation. How it works, with pictures: the user manual.
AI coding sessions are ephemeral. When a session ends, everything it learned -- architecture decisions, debugging insights, project context -- is gone. The next session starts from scratch.
agent-knowledge solves this with two complementary systems:
Sessions from all major AI coding assistants are auto-discovered -- if a tool is installed, its sessions appear automatically.
No configuration needed. Additional session roots can be added via the AGENT_KNOWLEDGE_EXTRA_SESSION_ROOTS env var (comma-separated paths).
knowledge_analyze(action: "stale_by_code_activity") cross-references file paths mentioned in each entry body against filesModified in recent session summaries. Pairs with a symbol-presence precision layer: identifiers the entry quotes (inline backticks + fenced blocks) are checked in the touched file; if they still exist, confidence downweights ×0.3. Entries with evergreen: true are exempt.knowledge_analyze(action: "search_gaps") surfaces zero-result queries over the last since_days, grouped by token-Jaccard similarity. The clearest signal for "what entries should I write next?".knowledge(action: "wakeup") assembles a multi-section bundle (identity → active_tasks → recent_decisions → known_gotchas → last_session_summary → top_weighted → semantic_fallback) within a token budget (default 800, override via token_budget or AGENT_KNOWLEDGE_WAKEUP_BUDGET). Unused section budget redistributes to later sections.minScore, minRecallCount, minUniqueQueries). Runs automatically in background, on demand via knowledge_admin(action: "promote"), or benchable offline via npm run bench:promote. Emits an auditable .dreams/YYYY-MM-DD.md diary every run.SessionAdapter interfaceknowledge, knowledge_search, knowledge_session, knowledge_graph, knowledge_analyze, knowledge_admin)evergreen: true in frontmatter exempts an entry from decay in ranking AND makes it append-only under promotion. The /knowledge pane marks them.author: <string> frontmatter, kept with the entry and returned by the REST API.calls, imports, inherits edge types for code structure; directed BFS traversal (outbound/inbound/both); bulk_link for efficient ingestion; unlink_by_origin for clearing stale code edges before re-ingest; code: prefixed node IDs distinguish code from knowledgevalid_from / valid_to validity windows; as_of queries return point-in-time snapshots; invalidate action marks facts as ended without deleting themcategory_mode: "boost" so a wrong category guess down-ranks instead of discarding the right answerAGENT_KNOWLEDGE_INDEX_VERBATIM=falseknowledge_admin(action: "config") for runtime setup, persisted at XDG/AppData location/knowledge pane in Claude Code -- browse and search the knowledge base beside the conversation (agent-knowledge plugin)knowledge_analyze(action: "consolidate") for batch dedup scanningknowledge_analyze(action: "reflect") surfaces unconnected entries and generates structured prompts for the agent to identify new graph connectionsextracted (user-written) or inferred (auto-distilled, 0.85× search rank multiplier); confidence_score field carries the model's certainty 0-1knowledge_analyze actions god_nodes (most-connected entries), bridges (cross-category connectors), gaps (isolated entries)knowledge_analyze(action: "brief") returns a cached ~200 token summary (core concepts, active projects, recent decisions, stale and gap counts) for session-start orientationorigin (manual, auto-link, distill, reflect) so analysis can distinguish user judgment from automated heuristicsfreshness: { body_age_days, last_accessed, access_count, verified_at, verification_age_days, evergreen }. Agent reads the trust signal and decides; we impose no policy demotion.SessionStart auto-wakeup + ingest-freshness check, UserPromptSubmit first-prompt targeted injection, PreCompact memory-flush nudge + distill, SessionEnd distill. Six hook scripts total, all fail-open, each toggleable via an AGENT_KNOWLEDGE_* env var. See docs/HOOKS.md.~/.claude/projects/*/memory/, similar in other IDEs), route durable user facts and feedback to agent-knowledge instead. Auto-memory is machine-local and invisible to other machines; agent-knowledge is git-synced, cross-machine, searchable, and surfaces in wakeup. See the Claude Code integration note in docs/USER-MANUAL.md.The knowledge-ingest skill populates or updates the knowledge base from a codebase directory. It uses tree-sitter for zero-token structural extraction (classes, functions, imports, call graphs, rationale comments), then clusters files into subsystems and creates knowledge entries + graph edges via existing MCP tools. Subsequent runs are incremental — only changed files are reprocessed.
/knowledge-ingest ./my-project
Uses the Agent Skills standard — works with Claude Code, OpenCode, Cursor, Codex CLI, and Gemini CLI. See Ingestion Guide for details.
Supported languages: TypeScript, JavaScript, Python, Go, Rust, Java, C, C++.
npm install -g agent-knowledge
git clone https://github.com/keshrath/agent-knowledge.git
cd agent-knowledge
npm install && npm run build
Add to your MCP client config (Claude Code, Cline, etc.):
{
"mcpServers": {
"agent-knowledge": {
"command": "npx",
"args": ["agent-knowledge"]
}
}
}
The first MCP process also serves a local REST API on 127.0.0.1:3423 (what the hooks, the /knowledge pane and agent-tasks call); when it exits, another running session takes the port over.
See Setup Guide for client-specific instructions (Claude Code, Cursor, Windsurf, OpenCode).
claude plugin marketplace add keshrath/agent-knowledge
claude plugin install agent-knowledge@agent-knowledge
The plugin brings the MCP server, the knowledge-ingest skill and the /knowledge pane (Claude Code 2.1.289+).
Response shape: {mode: "general" | "scoped", sessions, knowledge}. Scoped mode returns knowledge: [] by design.
Scopes: errors, plans, configs, tools, files, decisions, all.
Search knobs:
mmr: true applies Maximal Marginal Relevance re-ranking (kills near-duplicate clusters in top-K). mmr_lambda 0-1, default 0.7.category_mode: "boost" (default) gives matching-category entries a 1.25× score multiplier instead of dropping non-matches. Pass "filter" for hard-filter behavior.explain: true attaches score_components: {bm25, decay, maturity, confidence, category_boost, mmr_penalty} to every knowledge hit.Knowledge types: related_to, supersedes, depends_on, contradicts, specializes, part_of, alternative_to, builds_on
Code structure types: calls, imports, inherits
Traverse directions: outbound (source→target), inbound (target→source), both (default, undirected)
Every project-level candidate is scored on six signals (relevance 0.30, frequency 0.24, query-diversity 0.15, recency 0.15, consolidation 0.10, conceptual-richness 0.06) and gated on minScore ≥ 0.5, minRecallCount ≥ 2, minUniqueQueries ≥ 2. All three gates must pass. Background auto-promotion is controlled by the same auto_distill config flag; invoke on demand with knowledge_admin(action: "promote").
promote_mode: "explain" (default) — score + gate candidates, write diary, DO NOT touch the KB.promote_mode: "apply" — promote candidates that pass, write diary, git-commit.~/agent-knowledge/.dreams/YYYY-MM-DD.md with per-candidate signal breakdowns and gate outcomes. The .-prefixed dir is git-tracked but excluded from list/search.evergreen: true frontmatter are never overwritten by promotion — activity is appended.Write-bench harness: npm run bench:promote — offline replay with auto-labeling by "referenced in later sessions". Compares gated promoter to a naive "ship all" baseline, reports precision / recall / F1. Use it to gate signal-weight or threshold changes before rolling them out.
Loopback only (127.0.0.1), JSON only. Every request goes through a guard: a foreign Host or any Origin header gets 403, a request body must be application/json. A :path with a slash is sent encoded (notes%2Fx.md).
graph LR
subgraph Storage
KB[(Knowledge Base<br/>~/agent-knowledge<br/>Git Repository)]
end
subgraph Session Sources
CC[(Claude Code<br/>JSONL)]
CU[(Cursor<br/>JSONL)]
OC[(OpenCode<br/>SQLite)]
CL[(Cline<br/>JSON)]
CD[(Continue.dev<br/>JSON)]
AI[(Aider<br/>MD / JSONL)]
end
subgraph agent-knowledge
KM[Knowledge Module<br/>store / search / git]
AD[Session Adapters<br/>auto-discovery]
SE[Search Engine<br/>TF-IDF + Fuzzy]
DS[REST API<br/>127.0.0.1:3423]
MCP[MCP Server<br/>stdio]
end
subgraph Clients
AG[Agent Sessions]
PN["/knowledge pane<br/>hooks, agent-tasks"]
end
KB <-->|git pull/push| KM
CC --> AD
CU --> AD
OC --> AD
CL --> AD
CD --> AD
AI --> AD
AD --> SE
KM --> MCP
SE --> MCP
KM --> DS
SE --> DS
MCP --> AG
DS --> PN
Entries and code symbols can be connected via typed, weighted edges stored in a dedicated edges SQLite table. Eleven relationship types are supported — 8 for knowledge edges and 3 for code structure:
Knowledge: related_to, supersedes, depends_on, contradicts, specializes, part_of, alternative_to, builds_on
Code structure: calls, imports, inherits
knowledge_graph(action: "link") creates or updates an edge (with optional strength 0-1)knowledge_graph(action: "unlink") removes edges (optionally filtered by type)knowledge_graph(action: "list") lists edges for an entry or relationship typeknowledge_graph(action: "traverse") performs directed BFS traversal from a starting entry. Supports direction (outbound, inbound, both) and rel_type filterknowledge_graph(action: "bulk_link") batch-creates edges in a single transaction (for code graph ingestion)knowledge_graph(action: "unlink_by_origin") deletes all edges with a specific origin (for clearing stale code edges before re-ingest)Code structure edges are created by the knowledge-ingest skill during codebase ingestion. They use code: prefixed node IDs:
code:src/auth/middleware.ts # file node
code:src/auth/middleware.ts::validateToken # symbol node
Query examples:
# Who calls validateToken?
knowledge_graph({ action: "traverse", entry: "code:src/auth.ts::validateToken", direction: "inbound", rel_type: "calls", depth: 3 })
# What breaks if I change this function?
knowledge_graph({ action: "traverse", entry: "code:src/auth.ts::validateToken", direction: "inbound", rel_type: "calls", depth: 5 })
# Combined: callers + knowledge context (decisions, design rationale)
knowledge_graph({ action: "traverse", entry: "code:src/auth.ts::validateToken", depth: 2 })
When knowledge with action: "write" creates or updates an entry, it automatically finds the top-3 most similar existing entries via cosine similarity and creates related_to edges for any pair scoring above 0.7.
Each knowledge entry has a confidence score tracked in the entry_scores SQLite table. Search results are ranked using:
finalScore = baseRelevance * 0.5^(daysSinceLastAccess / 90) * maturityMultiplier
Entries mature automatically based on access count:
Frequently accessed entries rise in search rankings; stale entries decay over time.
TF-IDF Ranking -- results scored by term frequency-inverse document frequency. Rare terms boost relevance. Global index cached for 60 seconds.
Fuzzy Matching -- Levenshtein edit distance with sliding window. Configurable threshold (default 0.7).
Scoped Recall via knowledge_search with the scope parameter:
POST /api/knowledge accepts { category, filename, content } and runs the full write pipeline: git pull → file write → embedding index → auto-link → git push → duplicate check. Returns { path, autoLinks?, duplicateWarnings?, git } with status 201.
This enables HTTP-based writes from other services without an MCP connection.
agent-tasks has a built-in KnowledgeBridge that auto-pushes learning and decision artifacts to agent-knowledge on task completion. Entries land in decisions/ with frontmatter tags (agent-tasks, project name, artifact type), are auto-indexed with embeddings, and auto-linked to similar entries. No configuration needed: while any session runs agent-knowledge, its REST API on 127.0.0.1:3423 answers.
npm test # 604 tests across 39 files
npm run test:watch # Watch mode
npm run lint # ESLint on src/ and tests/
npm run typecheck # tsc --noEmit
npm run check # typecheck + lint + format + test
All env vars live under the AGENT_KNOWLEDGE_* prefix. No host name is baked in — the adapter registry auto-detects installed AI coding hosts (.claude, .cursor, .codex, .aider, .continue, OpenCode) without configuration.
Project-scoped overrides win over the standard keys. Set either; the scoped form lets you run agent-knowledge with a different key than the rest of your environment.
Cross-session memory and recall for AI agents — git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing
{ "mcpServers": { "keshrath-agent-knowledge": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}टूल
अभी टूल सूची नहीं है। रिमोट सर्वर को पहले हेल्थ चेक के बाद सूची मिलती है।
FAQ
इस पेज पर Cursor, Claude Desktop, Claude Code या VS Code के लिए stdio कॉन्फ़िग कॉपी करें। यह पब्लिशर का इंस्टॉल इस्तेमाल करता है, PlugMCP प्रॉक्सी नहीं।
इस रिकॉर्ड में कोई ज़रूरी एनवायरनमेंट वेरिएबल दर्ज नहीं है।
Agent Knowledge stdio पर लोकल चलता है।
पिछली जाँच 2026-10-28 को हुई। Cross-session memory and recall for AI agents — git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing