BondAI is a tool for developing AI agent systems, with memory management, error handling, search, and integrations.
Open source
24 alternativesHindsight
Hindsight adds a memory component to AI agents so they can retain information across interactions.
About Hindsight
Hindsight is an open source memory system for AI agents that retains information across interactions. It provides retain, recall, and reflect operations, with concepts including memory types, observations, mental models, knowledge pages, and banks.
It runs as a server via Docker with a local API and UI, and connects to 25+ LLM providers including hosted services, local models, and OpenAI-compatible endpoints. A documentation skill is available for coding assistants such as Claude Code and Cursor.
Key features
- Retains information across agent interactions for later recall
- Supports retain, recall, and reflect memory operations
- Connects to 25+ LLM providers, including local models
- Runs via Docker with local API and UI endpoints
- Integrates with coding agents and MCP setups
- Provides documentation skill for Claude Code and Cursor
Best for: Developers building AI agents that need memory across interactions.
Good for
- Open source repository
- Works with 25+ LLM providers including local models
- Publishes live benchmark accuracy, latency and cost data
Hindsight at a glance
- Category
- AI & Machine Learning › AI Agents & Automation
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Hindsight FAQ
How can Hindsight be run locally?
Using Docker with configured LLM credentials, exposing an API on port 8888 and a UI on port 9999.
Which LLM providers are supported?
25+ providers via HINDSIGHT_API_LLM_PROVIDER, including hosted services, local options like Ollama, LM Studio and llama.cpp, OpenAI-compatible endpoints, and LiteLLM gateways.
Where are benchmark results available?
Live results including per-model accuracy, latency and cost are published at benchmarks.hindsight.vectorize.io.
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