Agentic KB

Agentic Engineering Knowledge Base

Persistent knowledge and context infrastructure for agent systems

Agentic-KB is a persistent, cross-referenced engineering knowledge system for agentic AI, autonomous software delivery, agent memory, evaluations, orchestration, and AI engineering patterns.

It contains 1,000+ compiled articles and exposes the knowledge through a Wikipedia-style web UI, CLI, graph/search interfaces, and an MCP server.

The core idea is simple:

Useful agent memory should be durable, inspectable, attributable, and continuously maintained—not trapped in one chat window or rebuilt from raw context on every run.

Quickstart

Requires Node 24.x.

git clone https://github.com/jaydubya818/Agentic-KB.git
cd Agentic-KB
npm install

Browse the knowledge base in a browser

cd web && npm install && npm run dev

Open http://localhost:3002 for Wikipedia-style search, article rendering, backlinks, and graph navigation.

Query it from the terminal

node cli/kb.js search "multi-agent orchestration"
node cli/kb.js query "What is the best pattern for a supervisor-worker system?"
node cli/kb.js read concepts/tool-use
node cli/kb.js list concepts

The CLI and web server both use http://localhost:3002 by default. Set KB_API_URL only when the API is running at a different address.

CLI command reference

This command list mirrors node cli/kb.js help and is checked for drift by the test suite.

Network commands use KB_API_URL (default http://localhost:3002) and KB_API_TIMEOUT_MS. Private scopes require PRIVATE_PIN. Repository sync uses GITHUB_PAT unless --token is supplied. Query and compile operations require ANTHROPIC_API_KEY.

Expose it to an agent runtime over MCP

node mcp/server.js

Point any MCP client at that process to get bounded, policy-checked knowledge tools instead of raw filesystem access. See mcp/README.md.

Run the tests

npm test

Beyond RAG

Agentic-KB does not treat the knowledge base as a pile of documents behind semantic search.

Raw sources move through an explicit compilation and maintenance process into a persistent wiki:

Raw sources
    ↓
Ingestion / normalization
    ↓
Compilation
    ↓
Cross-referenced knowledge
    ↓
Lint / graph / contradiction checks
    ↓
Queryable wiki + CLI + MCP
    ↓
Agent and human workflows

The compile step is deliberate, incremental, logged, and auditable. Retrieval remains useful, but the durable asset is maintained knowledge rather than transient context assembly.

What it includes

• 1,000+ agentic-engineering articles
• concepts, patterns, frameworks, entities, recipes, and evaluations
• persistent operational memory
• cross-referenced wiki links and backlinks
• graph-oriented navigation and maintenance
• CLI query and maintenance workflows
• MCP access for agent runtimes
• source citations and contradiction markers
• incremental compilation state
• ingestion ledgers and durable receipts
• private/public knowledge boundaries
• linting, stale-content detection, and graph-maintenance checks
• agent-driven capture and maintenance workflows

Why this matters for AI-native engineering

As agent systems become more autonomous, context engineering becomes infrastructure.

A durable knowledge layer can help agents and operators answer:

• What do we already know about this system?
• Which source supports this claim?
• Is the knowledge current or stale?
• Does another source contradict it?
• Which concepts and systems are related?
• What was learned from previous execution?
• Which knowledge is safe to expose to a given agent?
• What should become durable memory versus temporary context?

The objective is not unlimited memory. It is useful, governed, high-signal context.

Interfaces

Web

Wikipedia-style browsing, search, article rendering, backlinks, graph-oriented navigation, and maintenance workflows.

CLI

Command-line access for ingestion, compilation, querying, verification, and maintenance.

MCP

Agent-facing tools expose bounded knowledge operations so external agent runtimes can query the KB without treating the filesystem as an unrestricted authority surface.

Knowledge lifecycle

Agentic-KB distinguishes raw input from compiled knowledge and private/canonical state.

Important design principles include:

1. Raw content is untrusted input.
2. Compilation is an explicit state transition.
3. Sources and citations should survive synthesis.
4. Contradictions should be visible rather than silently resolved.
5. Writes should be atomic and recoverable.
6. Private knowledge must not leak through reports, indexes, or git.
7. Agent access should be policy-bounded.
8. Maintenance should be continuously testable.

Reliability and security work

The repository includes extensive correctness and maintenance coverage around areas such as:

• atomic writes
• SSE/event-stream failure handling
• graph and backlink correctness
• private-layer exclusions
• PIN-gated operations
• webhook authentication
• MCP error propagation
• citation preservation
• contradiction signaling
• ingestion idempotency
• file-descriptor safety
• supply-chain pinning and install-script restrictions

The latest maintenance cycle reports 503 passing tests.

Relationship to autonomous software delivery

Agentic-KB is the knowledge/context layer in a broader autonomous-engineering architecture.

Mission Control governs intent, WorkOrders, execution, verification, evidence, and delivery decisions.

Agentic Pi Harness explores governed worker execution and knowledge-access boundaries.

Agentic-KB provides durable knowledge those systems can query without turning transient model context into the system of record.

Mission / WorkOrder
       ↓
Agent runtime / harness
       ↓
bounded context request
       ↓
    Agentic-KB
       ↓
source-backed knowledge
       ↓
execution + evidence

Technical themes

• context engineering
• agent memory
• knowledge graphs
• MCP
• retrieval and synthesis
• provenance and citations
• contradiction detection
• incremental compilation
• durable state
• privacy boundaries
• operational memory
• agent-access policy
• knowledge maintenance automation

Status

Active and continuously maintained. The project combines a large compiled knowledge corpus with working web, CLI, MCP, graph, ingestion, linting, and maintenance paths. Current development emphasizes correctness, privacy boundaries, durable operations, and making the knowledge layer safer and more useful for autonomous agent systems.

License

MIT — see LICENSE.