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AI agents die mid-task when context fills — losing every decision and WHY it was made. agent-recall-ai fixes this with Decision Anchors: critical reasoning is protected from compression and never pruned. Zero-code for Claude Code: pip install agent-recall-ai && agent-recall-ai install-hooks Works with OpenAI, Anthropic, LangChain, LangGraph, CrewAI. Includes live dashboard, PII redaction, and cost monitors.
Hey Product Hunt! 👋 I'm Srinath, and I built agent-recall-ai.
The backstory: I was doing a complex auth refactor with Claude Code. 2 hours in, the context window filled and the session died. Every decision, every constraint, every "why" — gone. Starting from scratch felt wrong. So I built structured auto-save for AI agents.
The key insight is Decision Anchors — messages containing words like "decided", "rejected", "because", "constraint" are mathematically protected from context compression. The reasoning chain survives forever, no matter how long the session runs.
Zero-code setup for Claude Code users:
pip install agent-recall-ai
agent-recall-ai install-hooks
Two commands. Every session protected from that point on.
Would love to hear from anyone building long-running agents — what's your biggest pain point when sessions die? Happy to answer any questions about the architecture or design decisions!
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About agent-recall-ai on Product Hunt
“Your AI agent never starts over again”
agent-recall-ai was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #135 on the daily leaderboard. AI agents die mid-task when context fills — losing every decision and WHY it was made. agent-recall-ai fixes this with Decision Anchors: critical reasoning is protected from compression and never pruned. Zero-code for Claude Code: pip install agent-recall-ai && agent-recall-ai install-hooks Works with OpenAI, Anthropic, LangChain, LangGraph, CrewAI. Includes live dashboard, PII redaction, and cost monitors.
agent-recall-ai was featured in Open Source (68.4k followers), Developer Tools (512.4k followers), Artificial Intelligence (468.5k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 194.2k products, making this a competitive space to launch in.
Who hunted agent-recall-ai?
agent-recall-ai was hunted by Srinath S. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Srinath S