Mistral OCR 4 extracts text from PDFs, DOCs and more while also returning bounding boxes, block types and per-word confidence. It supports 170 languages, runs in a single container for self-hosted deployments, and outperforms rivals on human and automated benchmarks.

• Mistral OCR 4 doesn't just extract text—it returns bounding boxes, block types (titles, tables, equations, signatures), and per-word confidence scores, and can be self-hosted in a single container for data-sovereignty needs
• Beat every tested competitor in human evaluations across 600+ documents/12+ languages with a 72% average win rate, and scored 85.20 on OlmOCRBench and 93.07 on OmniDocBench
• Roughly 4x faster than some enterprise OCR providers, and 8x cheaper with 17x lower latency than leading agentic document parsers on finance datasets with charts/figures
• Priced at $4 per 1,000 pages via API ($2 with batch discount) or $5 per 1,000 pages through the no-code Document AI interface

Stash is an open-source service that adds continuous memory to any AI model by storing and organizing session data in PostgreSQL with pgvector. It transforms raw conversations into structured facts, relationships, and patterns, so agents recall preferences, avoid repeated errors, and track long-term goals. Integration via MCP makes it model-agnostic, self-hostable, and free of vendor lock-in.

• Stash gives AI persistent memory by storing conversations in PostgreSQL/pgvector and running a 9-stage pipeline that turns raw episodes into facts, relationships, causal links, patterns, contradictions, goals, and failure detection
• Unlike RAG's static document search, Stash learns automatically from conversations and outcomes without manual tagging, improving over time
• It's model-agnostic and vendor-lock-in-free via MCP integration, working with Claude, GPT, or local models
• Setup requires just three commands (clone repo, configure .env, docker compose up), with self-hosting keeping data local

Stash lets your AI agent keep memory across conversations, so you don’t have to repeat context every time. It’s an open-source, self-hosted layer that uses Postgres, pgvector, and an eight-stage consolidation pipeline to turn raw observations into structured knowledge. It plugs into any MCP-compatible agent with a single Docker command.

• Stash gives AI agents persistent memory across sessions using Postgres + pgvector, run via a self-hosted Docker stack (no cloud dependency).
• A background service runs an eight-stage consolidation pipeline (fact extraction, relationship mapping, causal links, goal tracking, failure patterns, hypothesis checks, confidence decay) that only processes new entries, keeping the knowledge base efficient as it grows.
• It works with any MCP-compatible agent (OpenAI Agents, Claude Desktop, Continue, Cursor, Windsurf, Ollama, OpenRouter) via a single Docker command setup.
• It's open source (Apache 2.0), letting users customize consolidation rules, swap models, or plug in custom retrieval logic.