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What is BetterContext?
BetterContext is the enterprise ASI memory system and second brain for AI agents, providing persistent workspace context, guardrails, and knowledge graph recall.
Modern Large Language Models (LLMs) are stateless. When agents like Claude, Cursor, ChatGPT, or autonomous harnesses operate across codebases, tickets, and cloud infrastructure, they suffer from context fragmentation, high token costs, and catastrophic hallucinations.
BetterContext acts as an external cognitive layer. It indexes your repositories, workplace documentation, and operational telemetry into an automatically generated Causal Knowledge Graph, ensuring every agent prompt is grounded in verified, de-confounded truth with sub-300ms recall latency.
Core Building Blocks
BetterContext ships with six fundamental cognitive systems designed for production agent deployment:
Deterministic entity relationship graph that connects files, commits, chats, and operational decisions.
Isolates true root causes and do(X) interventions from spurious statistical correlations.
Captures human workflows, reviews, and fixes into reusable procedural skills for agents.
Role-based data access controls and automated PII/secret scrubbing across departments.
Native Model Context Protocol support for Claude Desktop, Cursor, Antigravity, and VS Code.
240+ workplace integrations (GitHub, Slack, Jira, Notion) executing locally with zero key storage.
How does it work? (at a glance)
BetterContext transforms raw workspace activity into structured, actionable intelligence through a continuous closed-loop pipeline:
1. Continuous Ingestion: Files, commits, Slack discussions, and Linear tickets are ingested in real-time.
2. Entity & Causal Extraction: Micro-models resolve dependencies, de-confound variables, and compute confidence half-life decay.
3. Synergistic Context Packing: When an agent invokes a task, BetterContext selects non-redundant, high-relevance nodes.
4. Closed-Loop Feedback: Agent output success and human edits feed back into the graph, continuously improving organizational recall.
- Zero Hallucinations: Agents cite verified knowledge graph nodes rather than guessing from ungrounded prompt memories.
- Sub-300ms Recall: High-throughput caching and localized vector graph traversal keep interactive coding agents blazing fast.
- Full Harness Independence: Works out-of-the-box with Claude Code, Cursor, Codex, Gemini CLI, and custom agents.
Why add memory to your agent?
Simply expanding token context windows to 1M+ or 2M+ tokens fails in production due to the lost-in-the-middle problem, extreme API latency, and exponential cost curves. When models are flooded with raw text dumps, accuracy drops and instructions are overlooked.
By delegating context retrieval to BetterContext:
- Cost Efficiency: Agents receive 2k-8k tokens of hyper-relevant context instead of 200k tokens of unparsed noise, saving 80-95% in token costs.
- Consistency: Your team's architectural decisions, API conventions, and brand rules persist permanently across all developer sessions.
- Cross-Agent Handoff: Research conducted by a research agent is immediately available to a coding agent via the Unified Results Space.
Why BetterContext over Vector RAG?
Traditional RAG tools rely solely on cosine similarity across chunked vector embeddings. This causes severe blind spots: vector search cannot understand temporal changes, causal relationships, or negative constraints.
BetterContext is a Causal Context Operating System. It evaluates whether an action directly drives an outcome ($P(Y|do(X))$), applies confidence half-life decay to prune outdated facts, and enforces strict security ACLs at query time.
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