PlatformPilot
PlatformPilot is a company brain for AI-first teams. It captures how your company actually works, your decisions, playbooks, and tribal knowledge, and turns it into a living memory your team and your AI agents can use to answer questions and take action across all your tools.
Unlike search tools that only retrieve, PlatformPilot reasons across your systems, shows the why behind every answer, and acts on your own playbooks, in your own cloud, getting sharper every time it is used.
It connects to your stack through the Model Context Protocol (MCP), so it works as a shared memory layer inside the tools your team already uses, including Claude Code, Claude Desktop, and OpenAI-based agents. Memory evolves as you work.
- Living memory that learns from outcomes, not just stores notes
- Reasoning across all your tools. We support +200 tools.
- Plain-language search over your team's decisions, playbooks, and history
- Self-organizing knowledge
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Engram
Engram is a fully managed memory and context service purpose-built to help AI agents remember, learn, and improve over time. Instead of treating memory as an ever-growing pile of raw conversations and events, it turns noisy interaction data into structured, durable, and evolving memories. Applications can send raw text, complete conversations, or pre-extracted facts through a REST API or Python SDK without preprocessing. Engram then runs asynchronous pipelines that extract relevant information, transform it by deduplicating and reconciling it with existing knowledge, and commit a clean memory state without blocking the application’s main workflow. It resolves inconsistencies, adapts to changing preferences and time-evolving facts, and keeps context relevant and efficient. Agents can retrieve ranked memories in real time through vector similarity, BM25 keyword search, or hybrid retrieval, reducing the need to resend entire conversation histories.
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Hindsight
Hindsight is an agent memory system built to create smarter AI agents that learn over time instead of starting every conversation from zero. Most agent memory systems focus on recalling conversation history, but Hindsight is focused on making agents learn, not just remember. It gives AI agents persistent long-term memory using biomimetic data structures, helping them retain facts, recall relevant context, and reflect on experience as part of reasoning. Hindsight is designed for agents that need to understand who a user is, what has been discussed, what preferences have emerged, what decisions were made, and how behavior should adapt across sessions. It provides three core operations: retain, recall, and reflect. Retain stores new information, recall retrieves the right memories when needed, and reflect helps agents synthesize observations, form mental models, and learn from prior interactions.
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Qdrant
Qdrant is a high-performance, composable vector search engine built in Rust for production-grade semantic, hybrid, and agentic workloads.
Combine dense vectors, sparse vectors, metadata filters, multi-vector representations, and custom scoring as primitives at query time. Written in Rust for memory efficiency, SIMD optimization, and predictable performance without garbage collection pauses. No wrappers, no bolt-ons, no legacy compromises — just a custom HNSW implementation and storage engine built specifically for vector workloads.
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