# LocusGraph Builder Documentation

> LocusGraph - Structured Agent Knowledge that turns agent experience into compounding skills.

## Docs

- [Coding Agent Integration](/workflows/coding-agent): Terminal commands, file edits, error tracking, and user intent — captured as structured agent knowledge.
- [Memory-Augmented RAG](/workflows/memory-augmented-rag): Combining document retrieval with structured agent knowledge.
- [Multi-Agent Collaboration](/workflows/multi-agent): Shared graphs, scoped contexts, agent-to-agent knowledge.
- [Workflows Overview](/workflows/overview): How agents put Structured Agent Knowledge to work in practice. Every workflow in this section sits on top of the same loop and the same graph.
- [Session & Long-Term Memory](/workflows/session-and-long-term): Managing session lifecycle and durable structured agent knowledge.
- [Single Agent Loop](/workflows/single-agent): One agent, structured knowledge compounding across sessions.
- [FAQ & Troubleshooting](/support/faq): Direct answers to the most common LocusGraph questions.
- [Python SDK](/sdks/python): The official Python client for LocusGraph. Use it to wire your Python agent, LangChain pipeline, or backend service into Structured Agent Knowledge: store typed events, retrieve validated knowledge, and generate insights from one client.
- [Rust SDK](/sdks/rust): The official Rust client for LocusGraph. Use it to embed Structured Agent Knowledge into performance-sensitive agents, backend services, or CLI tools: store typed events, retrieve validated knowledge, and generate insights from one blocking client.
- [TypeScript SDK](/sdks/typescript): The official TypeScript client for LocusGraph. Use it to wire your Node.js or LangChain agent into Structured Agent Knowledge: store typed events, retrieve validated knowledge, browse contexts, and generate insights — all from one client.
- [Claude Code](/mcp/claude-code): Connect Claude Code to LocusGraph over MCP. Once connected, Claude Code reads and writes the same Structured Agent Knowledge graph your other agents use — events stored from one client are immediately retrievable from any other. Use the production auth endpoint:
- [Connecting MCP Clients](/mcp/clients): Connect any MCP client that supports remote HTTP MCP plus OAuth 2.0 discovery. The same connection gives every client access to the same Structured Agent Knowledge graph — no per-client integration work.
- [Model Context Protocol (MCP)](/mcp/overview): MCP is the open protocol for connecting AI agents to external tools and data. LocusGraph uses MCP to expose Structured Agent Knowledge to any compatible client over a single OAuth-protected endpoint.
- [MCP Resources](/mcp/resources): MCP resources expose read-only Structured Agent Knowledge for browsing without making tool calls. They are the "look around before you act" surface of the graph.
- [MCP Tools](/mcp/tools): LocusGraph currently exposes four MCP tools through the region server, with OAuth enforced at the auth worker's `/mcp` endpoint. Together they cover the full structured agent knowledge loop: write events, read knowledge, browse contexts, and reason over what the agent has learned.
- [LangChain Integration](/integrations/langchain): Connect Structured Agent Knowledge to LangChain agents with `LocusGraphMemory` and `LocusGraphRetriever`. Both adapters are LLM agnostic — you can swap models without losing the knowledge graph behind them.
- [Common Patterns](/guides/common-patterns): Proven patterns for turning agent experience into structured agent knowledge with LocusGraph.
- [Environment Variables](/guides/environment-variables): Every configuration option available for LocusGraph SDKs. Two variables are enough to point any SDK at your Structured Agent Knowledge graph.
- [Error Handling](/guides/error-handling): What breaks, why it breaks, and how to handle it across every SDK. Treat each error as data: many of these failures are exactly the kind of event your agent should store back into LocusGraph so it stops repeating them.
- [Authentication](/getting-started/authentication): Every request to LocusGraph requires an agent secret passed as a Bearer token. The secret identifies which agent is reading and writing your Structured Agent Knowledge graph, so treat it like any other production credential.
- [Introduction](/getting-started/introduction): LocusGraph turns what your agents learn into structured knowledge that compounds, so events become patterns, patterns become skills, and every session starts smarter.
- [Quickstart](/getting-started/quickstart): Get LocusGraph running in under five minutes: install, store a knowledge event, retrieve it.
- [2. What is Agent Structured Knowledge?](/foundations/agent-structured-knowledge): Agent Structured Knowledge is the durable layer of what an agent has learned from work it has actually performed. It is not a transcript, scratchpad, or pile of embeddings. It is knowledge with shape.
- [9. Context Engineering Misconceptions and LocusGraph's Role](/foundations/context-engineering-misconceptions): Context engineering is a discipline, not a product category. Saying "we do context engineering" is like saying "we do software engineering" — true, but not specific enough to explain what someone should buy.
- [6. How LocusGraph Gives Your Agent Evolving Skills](/foundations/evolving-skills): LocusGraph gives agents a way to actually learn from repeated work. Not "remember more" — *learn*.
- [3. The Graduation Plane](/foundations/graduation-plane): The core differentiator in LocusGraph is **graduation**. Events do not just get stored — they get admitted, linked, scored, and promoted up a four-stage plane.
- [4. LLM Agnostic](/foundations/llm-agnostic): LocusGraph is LLM agnostic because the knowledge layer lives outside the model. The same Structured Agent Knowledge can be used with different LLMs, IDEs, agents, and orchestration frameworks without locking your knowledge to one provider.
- [7. Own Your IP as a Skill](/foundations/own-your-ip): The way your team solves problems is intellectual property. Your debugging habits, architectural preferences, review standards, deployment patterns, customer context, and hard-won corrections should not live only inside one model session.
- [8. Tokens in Current IDEs vs LocusGraph](/foundations/tokens-in-ides): In current AI IDE workflows, tokens are spent stuffing the prompt with chat history, file excerpts, tool results, summaries, and vector-retrieved snippets. It works for the current turn but it is expensive and temporary. The same context has to be reintroduced again later.
- [5. How is LocusGraph Different from Other Memory Systems?](/foundations/vs-memory-systems): Most memory systems retrieve similar text. LocusGraph evolves knowledge.
- [1. What is LocusGraph?](/foundations/what-is-locusgraph): LocusGraph is a typed knowledge graph with an admission pipeline, confidence scoring, and semantic retrieval. It is designed to sit between an AI agent and its LLM, replacing dumb chat-history or vector-search memory with structured knowledge that compounds.
- [Managing Context Windows](/context-engineering/context-windows): Agent context windows are finite. Every token counts. Structured Agent Knowledge keeps the right learnings in front of the model and the rest out of the prompt.
- [Designing Memory Schemas](/context-engineering/memory-schemas): A good schema makes structured agent knowledge discoverable. A bad one buries it.
- [Context Engineering](/context-engineering/overview): Context engineering is the discipline of choosing what knowledge your agent stores, how it is shaped, and what gets retrieved at the right moment. LocusGraph is the Structured Agent Knowledge layer that makes that discipline practical.
- [Memory Relevance & Retrieval](/context-engineering/relevance-and-retrieval): Semantic search, confidence scoring, and filters work together to surface validated knowledge — not just similar text. This is where Structured Agent Knowledge actively beats vector recall.
- [Context Scoping Strategies](/context-engineering/scoping-strategies): Scoping controls what structured agent knowledge your agent sees in a given retrieval. Tighter scoping means more relevant, higher-confidence results.
- [Contexts & Graphs](/concepts/contexts-and-graphs): Contexts and graphs are how LocusGraph organizes structured agent knowledge. Graphs isolate workspaces. Contexts group related learnings inside a workspace.
- [Event Kinds](/concepts/event-kinds): Event kinds tell LocusGraph what type of agent knowledge you are storing. The kind drives how it gets typed, linked, scored, and graduated.
- [Memories & Events](/concepts/memories-and-events): Events are the atomic unit of structured agent knowledge. Every learning enters LocusGraph as an event and graduates from there.
- [Memory Links](/concepts/memory-links): Links connect loci inside LocusGraph. They are the typed relationships that drive how knowledge gets retrieved, scored, and graduated from event to pattern to skill.
- [Payload Structure](/concepts/payload-structure): Payloads carry the actual knowledge inside an event. They are the smallest unit of structured agent knowledge that you control directly, so shape them well.
- [Sources](/concepts/sources): Sources identify who created an event. They control how LocusGraph weights and links the resulting knowledge so validated, human-confirmed learnings stay at the front.
- [Generate Insights](/api/generate-insights): Reason over your Structured Agent Knowledge to surface patterns and recommendations. This is where validated knowledge is synthesized into something the agent can act on — not just retrieved.
- [Get Context](/api/get-context): Retrieve a single context from your Structured Agent Knowledge graph. Use this when you already know exactly which context you want — for example to inspect a specific `skill:` or `error:` bucket.
- [List Contexts](/api/list-contexts): Browse and search contexts stored in your Structured Agent Knowledge graph. Useful for orientation: see which skills, errors, projects, or sessions the agent has accumulated knowledge under.
- [Response Format](/api/response-format): All LocusGraph API responses are returned as JSON. Each endpoint returns a small, well-shaped payload — designed to drop straight into a prompt without extra parsing — because Structured Agent Knowledge is supposed to make context windows smaller, not larger.
- [Retrieve Memories](/api/retrieve-memories): Run a semantic search over your Structured Agent Knowledge. Results are filtered by scope, ranked by semantic similarity, then re-ranked by confidence — so you get validated knowledge, not just similar text.
- [Store Event](/api/store-event): Record a knowledge event in your Structured Agent Knowledge graph. Every event passes through the admission pipeline before becoming a locus, so this endpoint is the front door for everything that will eventually graduate into a skill.