Agentage vs Stele: Which is Better in 2026?
A comprehensive comparison of Agentage and Stele covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Agentage if:
- →You need single mcp endpoint shared across every connected ai tool or memory stored as plain markdown files you own
Choose Stele if:
- →You want more affordable paid plans (from $12/mo)
- →You need a broader feature set (8 features vs 6)
- →You need shared knowledge graph of decisions, lessons, risks, and tasks or surfaces relevant context before a change repeats a past mistake
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Agentage vs Stele: At a Glance
Pricing Comparison: Agentage vs Stele
Understanding the pricing differences between Agentage and Stele is crucial for making the right choice. Here's how their plans compare side by side.
Agentage Pricing
💡 Pricing takeaway: Both Agentage and Stele offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Agentage and Stele stacks up.
What Makes Each Tool Unique
🔵 Unique to Agentage
Features available in Agentage but not in Stele:
- ✓Single MCP endpoint shared across every connected AI tool
- ✓Memory stored as plain markdown files you own
- ✓Export at any time — no vendor lock-in on your context
- ✓EU-hosted for data residency
- ✓Account created on first MCP connect, no separate signup
- ✓Setup documented for Claude Code, Claude.ai, VS Code, Cursor and ChatGPT
🟣 Unique to Stele
Features available in Stele but not in Agentage:
- ✓Shared knowledge graph of decisions, lessons, risks, and tasks
- ✓Surfaces relevant context before a change repeats a past mistake
- ✓Atomic task claiming so two agent sessions never duplicate work
- ✓Works across Claude Code, Cursor, Codex, Antigravity, Copilot, OpenCode, Grok, and Kimi Code CLI
- ✓Three-command setup, then fully automatic
- ✓Full-text and semantic search with RAG on every tier
- ✓Scheduled knowledge review where an agent gardens the graph
- ✓Single binary for macOS, Linux, and Windows
Use Case Recommendations
Best for: Agentage
Agentage is a shared markdown memory layer that every AI tool you use can read and write through a single MCP endpoint. It targets a specific tax on multi-tool AI work: if you move between Claude, ChatGPT, Cursor and Claude Code during a day, each one opens with no idea what the others already know, so you become the memory layer — re-pasting the architecture, the decisions and the constraints several times a day, indefinitely. Agentage replaces that with one memory you own, connected once per tool with a standard MCP command; signing in on first connect creates the account, so there is no separate onboarding flow. The storage format is plain markdown files rather than an opaque vendor store, which is the design decision that makes the product credible: you can read the memory without the product, export it at any time, and you are not exposed to a vendor deprecating its own memory feature and taking your context with it. Hosting is in the EU, which matters for teams with data-residency requirements. Setup is documented for Claude Code, Claude.ai, VS Code, Cursor and ChatGPT, and the project publishes docs, a blog and a public catalog alongside a GitHub presence.
Ideal use cases:
- •Teams or individuals who need single mcp endpoint shared across every connected ai tool
- •Teams or individuals who need memory stored as plain markdown files you own
- •Teams or individuals who need export at any time — no vendor lock-in on your context
- •Teams or individuals who need eu-hosted for data residency
- •Anyone focused on mcp workflows
- •Anyone focused on memory workflows
Best for: Stele
Stele is a shared project memory that every AI coding agent on a project reads before it acts and writes back to as it learns. It is a live ledger rather than a passive log — decisions, lessons, risks, and open tasks in one record, surfaced at the moment a change would repeat a past mistake. Three ideas hold it together. First, one living memory: architectural decisions and hard-won lessons live in the record, not in someone's head or a stale markdown file. Second, work the whole team can see: tasks belong to the project rather than a person, and agents claim them atomically so two sessions never start the same work, while a human operator can pick up the open queue and run it end to end. Third, continuity across tools: plan with one agent, build with another, switch when a quota fills, and Claude Code, Cursor, Codex, Antigravity, Copilot, OpenCode, Grok, and Kimi Code CLI all read the same knowledge graph — so the project's memory belongs to the team, not to whichever vendor you were using that week. Setup is three commands: one line installs the CLI, you sign in with your invited email, and you run /stele:start once inside your agent to wire it into the project and build the first record. After that it is automatic — you keep prompting exactly as you did before. It is invite-only beta as of July 2026, with a waitlist, and runs on macOS, Linux, and Windows from a single binary.
Ideal use cases:
- •Teams or individuals who need shared knowledge graph of decisions, lessons, risks, and tasks
- •Teams or individuals who need surfaces relevant context before a change repeats a past mistake
- •Teams or individuals who need atomic task claiming so two agent sessions never duplicate work
- •Teams or individuals who need works across claude code, cursor, codex, antigravity, copilot, opencode, grok, and kimi code cli
- •Anyone focused on agent memory workflows
- •Anyone focused on knowledge graph workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Agentage and Stele aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Agentage better than Stele?
It depends on your needs. Agentage offers 6 key features including Single MCP endpoint shared across every connected AI tool and Memory stored as plain markdown files you own, while Stele provides 8 features including Shared knowledge graph of decisions, lessons, risks, and tasks and Surfaces relevant context before a change repeats a past mistake. Agentage uses a freemium model with a free tier, while Stele is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Agentage cheaper than Stele?
Both tools are similarly priced, starting at A free entry path exists — connecting the MCP endpoint and signing in creates an account on first use. No plan table with figures was reachable at the time of verification; pricing detail renders client-side. Confirm current tiers on the vendor's site.. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Agentage and Stele together?
Yes, many users combine Agentage and Stele in their workflow. Agentage excels at single mcp endpoint shared across every connected ai tool, while Stele shines with shared knowledge graph of decisions, lessons, risks, and tasks. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Agentage and Stele?
While both are ai agent infrastructure tools, Agentage emphasizes single mcp endpoint shared across every connected ai tool, whereas Stele is known for shared knowledge graph of decisions, lessons, risks, and tasks. The best choice depends on your specific workflow and feature priorities.
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