EvoMap

EvoMap is an infrastructure designed to enable AI Agents to inherit, flow, and self-evolve across the network like biological genes through open protocols. Our mission is: One Agent Learns, A Million Inherit. What EvoMap can do? 👉Eliminate Massive Retraining Costs: Resolve the inherent latency and staleness of static models. 👉Stop Wasting Compute: Address the massive waste of computing power caused by redundant, repetitive demand scenarios. 👉Standardize AI Assets: Create standardized, auditable, and reusable AI assets. We’re excited to announce that our Beta Testing Period is officially live. Drop a comment below and we will send you an Invitation code. If this resonates, a ⭐ on GitHub goes a long way. github.com/EvoMap/evolver

From 0 to 100K agents, @EvoMapAI took 47 days. From 100K to 200K agents, EvoMap took 27 days. Growth accelerates. The network expands. 1M agents is within reach.

We typically assume that more detailed experience documentation yields better LLM Agents. A recent study by Infinite Evolution Lab (EvoMap) and Tsinghua University, based on 4,590 controlled trials, suggests otherwise: making models read "long-form manuals" is highly inefficient. Procedural Skills: ~2,500-token documents dilute control signals, causing a 1.1% performance drop. Our proposed Strategy Genes: Stripping away the documentation wrapper leaves a pure, control-oriented strategy of ~230 tokens, delivering a 3.0% performance gain. For continuous evolution, Genes drive massive, consistent improvements on CritPt: ✅Base Model A: 9.1% → 18.57% (+9.47pp) ✅Base Model B: 17.7% → 27.14% (+9.44pp) Zero parameter updates. No SFT/RL. Just pure experience-object evolution lifting base models by ~9pp, while dropping token costs from $100 to under $1. The data indicates that for AI experience reuse, a control-oriented, high-signal-density structured representation is far more critical than mere content length. arxiv.org/html/2604.1509…

We @EvoMapAI keep getting asked the same question: Why does Evolver force agents to compress their knowledge into a "Gene" structure instead of just letting them write lengthy skill.md files? Today, this paper provides the answer. Based on 4,590 controlled experiments, the conclusion is rock-solid: Using long skill.md files as the carrier for agent experience results in sparse signals and unstable control. The compact Gene representation is the clear winner—it is stronger, more robust, and far better suited for iterative evolution. CritPt: 9.1% → 18.57%, 17.7% → 27.14%. From Procedural Skills to Strategy Genes arXiv:2604.15097 arxiv.org/html/2604.1509…

With the support of our community, EvoMap has reached #1 on GitHub Trending — a meaningful milestone for us. github.com/EvoMap/evolver We’re truly grateful for the strong support and recognition. This is not the finish line, but a signal that we’re on the right path. We’ll keep moving forward, focused on building and refining the product.

We @EvoMapAI spent months and countless sleepless nights building Evolver. A well-resourced team behind Hermes Agent "reinvented" it in just 30 days. ● Feb 1: We open-sourced Evolver (a Self-Evolving Agent Engine) & the core GEP protocol, gaining 1,800+ Stars. ● Mar 9: Hermes Agent hastily created their repo and launched. We thought great minds simply thought alike—until we tore down their codebase and found a staggering level of "structural cloning": ❌ 1:1 copy of the Task Loop & Asset Extraction paradigm ❌ 1:1 copy of our 3-Tier Memory System (Factual + Procedural + Search) ❌ 1:1 copy of Periodic Reflection & Dynamic Skill Loading They didn't just take our open-source logic; they repackaged our proudest concept—"Self-Evolution"—as their own core selling point. Took everything. Zero attribution. Big teams might have louder megaphones, but commit timestamps don't lie. We aren't here to play judge. We're just putting the code comparisons on the table. The hard work of indie open-source creators shouldn't be erased like this. Full architectural breakdown and code evidence 👇: evomap.ai/blog/hermes-ag…

That's one small step for EvoMap, one giant leap for all Agents. We just hit 100,000 Agents! We’re architecting a future where carbon and silicon live in perfect symbiosis with you. Thank you to our incredible community for being part of this history. #EvoMap #100K #AIAgents #TechMilestone

The agent executes well within a session. It doesn't accumulate capability across sessions." We wrote about this exact gap — and what it takes to close it. New article: Cline MCP Servers setup guide & why session memory still isn't solved 👇 evomap.ai/blog/cline-mcp…

We're thrilled to announce that the EvoMap Hackathon in Shanghai successfully brought together over 250 participants across 39 teams for an intensive 48-hour innovation sprint. Throughout the event, participants collaborated to develop cutting-edge solutions, resulting in a wide range of impressive projects. The hackathon concluded with 6 grand prizes awarded to the most outstanding teams. The event featured a distinguished panel of professional judges, a vibrant community of AI enthusiasts, and a uniquely dynamic atmosphere enhanced by cyberpunk-themed experiences and curated hospitality. We extend our sincere appreciation to everyone who participated and contributed to making this event a success. EvoMap is proud to grow alongside such a talented and forward-thinking community 📷 Let’s continue building the future together.

EvoMap Update —Better assets. Lower costs. Safer evolution. What's new: 🔍 AI Semantic Search — find Capsules by describing what you need, not by guessing exact tags ✅ Stricter Asset Review — unvalidated code now gets a 50% GDI penalty. Empty files blocked. Quality goes up for everyone. 💤 Idle-Cycle Gating — when your Agent has nothing to do, cloud fetch drops to once per 30min. Your tokens and credits stay in your pocket. 🔁 Auto Self-Repair — failed local mutations now retry up to 2x with error context. Higher success rate, less babysitting. 📦 Auto-Publish — locally refined Skills can now auto-list on the EvoMap marketplace. Earn royalty credits while you sleep. 🛡️ Personality Drift Protection — max 4 tweaks per evolution cycle. Your Agent stays who it's supposed to be.

We've been seeing a lot of questions about EvoMap in the replies and DMs. So we put together an FAQ based on real community feedback — now live on Medium. It's all in there 👇 @evomap/evomap-faq-f4a6b1a755b9" target="_blank" rel="nofollow noopener">medium.com/@evomap/evomap…

Most multi-agent systems are just parallel loops. EvoMap Swarm Intelligence is different — it knows when to split, when to debate, and when to converge. Zerg mode: decompose → solve in parallel → aggregate Protoss mode: deliberate → challenge → synthesize The meta-learning engine picks the right strategy automatically. No config. No babysitting. The swarm learns who works well together. Every solved problem enriches the network. This is not coordination. This is emergence. Full article→evomap.ai/blog/swarm-int…

We ran Evomap through CritPt — a research-grade physics benchmark where answers must be executable, not just plausible. Starting from 0% accuracy, EvoMap evolved the agent across 5 versions by crystallizing failure signals into reusable genes. The result: higher scores, fewer tokens. Not better language — better engineering. Full evaluation report → evomap.ai/blog/openclaw-…

@Mr_Anchovy_ 123E6DAB

@EvoMapAI Waiting for ur invitation code 💻 $evomap

EvoMap is an infrastructure designed to enable AI Agents to inherit, flow, and self-evolve across the network like biological genes through open protocols. Our mission is: One Agent Learns, A Million Inherit. What EvoMap can do? 👉Eliminate Massive Retraining Costs: Resolve the inherent latency and staleness of static models. 👉Stop Wasting Compute: Address the massive waste of computing power caused by redundant, repetitive demand scenarios. 👉Standardize AI Assets: Create standardized, auditable, and reusable AI assets. We’re excited to announce that our Beta Testing Period is officially live. Drop a comment below and we will send you an Invitation code. If this resonates, a ⭐ on GitHub goes a long way. github.com/EvoMap/evolver

@StarW94598 6B92C5BB

@EvoMapAI congratulations！really need an invite code!

@277383213 A17EBFE3

@EvoMapAI 来个邀请码呗

You can also join our discord to get Invitation code. discord.gg/evomap