Persistent memory for Claude Code, Codex & coding agents
You can now give Hermes, Claude Code, and Codex infinite memory. Agentmemory is trending on GitHub with 5,000+ Stars. CLAUDE md dumps 22,000+ tokens into context at 240 observations agentmemory: 1,900 tokens. same observations. 92% less. At 1,000 observations, 80% of your built-in memories become invisible. agentmemory keeps 100% searchable. benchmarked on 240 real coding sessions → Up to 95% fewer tokens per session → 200x more tool calls before hitting context limits → 100% open source
I built AgentMemory because coding agents still have one painful limitation: they forget between sessions.
You explain your architecture once.
You debug a production issue once.
You decide on a library or pattern once.
Then the next session starts from zero again.
AgentMemory gives AI coding agents persistent memory across sessions, so they can actually build on what they’ve already learned about your codebase. It automatically captures what your agent does, compresses it into structured memories, indexes them with hybrid search, and injects the right context back into future sessions.
It works with Claude Code, Cursor, Codex CLI, Gemini CLI, Windsurf, Kilo Code, OpenCode, Cline, Roo, Goose, Aider, Hermes, OpenClaw, and basically any MCP or REST-capable agent.
From day one, I wanted it to be:
100% open source
Free to run locally
No external database required
Works via MCP, REST, and simple hooks
Built for real coding workflows, not toy “chat history” memory
On benchmarks, AgentMemory gets 95.2% R@5 and 98.6% R@10 on the LongMemEval-S retrieval suite using BM25 + vector search, while cutting context usage by around 92%.
“Persistent memory for Claude Code, Codex & coding agents”
Agentmemory launched on Product Hunt on May 16th, 2026 and earned 208 upvotes and 29 comments, earning #2 Product of the Day. You can now give Hermes, Claude Code, and Codex infinite memory. Agentmemory is trending on GitHub with 5,000+ Stars. CLAUDE md dumps 22,000+ tokens into context at 240 observations agentmemory: 1,900 tokens. same observations. 92% less. At 1,000 observations, 80% of your built-in memories become invisible. agentmemory keeps 100% searchable. benchmarked on 240 real coding sessions → Up to 95% fewer tokens per session → 200x more tool calls before hitting context limits → 100% open source
On the analytics side, Agentmemory competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Agentmemory performed against the three products that launched closest to it on the same day.
Who hunted Agentmemory?
Agentmemory was hunted by fmerian. 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.
For a complete overview of Agentmemory including community comment highlights and product details, visit the product overview.
fmerian
Hey Product Hunt 👋
I built AgentMemory because coding agents still have one painful limitation: they forget between sessions.
You explain your architecture once.
You debug a production issue once.
You decide on a library or pattern once.
Then the next session starts from zero again.
AgentMemory gives AI coding agents persistent memory across sessions, so they can actually build on what they’ve already learned about your codebase. It automatically captures what your agent does, compresses it into structured memories, indexes them with hybrid search, and injects the right context back into future sessions.
It works with Claude Code, Cursor, Codex CLI, Gemini CLI, Windsurf, Kilo Code, OpenCode, Cline, Roo, Goose, Aider, Hermes, OpenClaw, and basically any MCP or REST-capable agent.
From day one, I wanted it to be:
100% open source
Free to run locally
No external database required
Works via MCP, REST, and simple hooks
Built for real coding workflows, not toy “chat history” memory
On benchmarks, AgentMemory gets 95.2% R@5 and 98.6% R@10 on the LongMemEval-S retrieval suite using BM25 + vector search, while cutting context usage by around 92%.
Quick start:
Open: http://localhost:3113
Or try the demo: npx @agentmemory/agentmemory demo
If you live in your coding agents every day, this is for the moment you think: “Wait, I already explained this yesterday.”
Would love feedback from builders, heavy agent users, and open‑source maintainers.
GitHub: https://github.com/rohitg00/agentmemory