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docs: add comparison page
Signed-off-by: Jose Alekhinne <alekhinejose@gmail.com>
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docs/comparison.md

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---
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# / Context: https://ctx.ist
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# ,'`./ do you remember?
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# `.,'\
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# \ Copyright 2026-present Context contributors.
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# SPDX-License-Identifier: Apache-2.0
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icon: lucide/git-compare
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---
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![ctx](images/ctx-banner.png)
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## Similar Tools and How `ctx` Differs
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There are many tools in the AI ecosystem that touch *parts* of the context
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problem:
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* Some manage prompts.
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* Some retrieve data.
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* Some provide runtime context objects.
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* Some offer enterprise platforms.
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`ctx` focuses on a different layer entirely.
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This page explains where `ctx` fits, and where it **intentionally** does not.
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---
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## The Core Distinction
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Most tools treat context as **input**.
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`ctx` treats context as **infrastructure**.
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That single difference explains nearly all of `ctx`'s design choices.
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| Question | Most tools | ctx |
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|--------------------------|---------------------------|------------------|
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| Where does context live? | In prompts or APIs | In files |
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| How long does it last? | One request / one session | Across time |
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| Who can read it? | The model | Humans and tools |
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| How is it updated? | Implicitly | Explicitly |
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| Is it inspectable? | Rarely | Always |
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---
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## Prompt Management Tools
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Examples include:
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* prompt templates
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* reusable system prompts
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* prompt libraries
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* prompt versioning tools
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These tools help you *start* a session.
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They do not help you *continue* one.
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### How they differ from `ctx`
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Prompt tools:
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* inject text at session start
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* are ephemeral by design
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* do not evolve with the project
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`ctx`:
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* persists knowledge over time
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* accumulates decisions and learnings
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* makes the context part of the repository itself
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Prompt tooling and `ctx` are complementary; not competing.
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Yet, they operate at different layers.
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---
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## Retrieval-Augmented Generation (RAG)
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RAG systems typically:
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* index documents
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* embed text
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* retrieve chunks dynamically at runtime
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They are excellent for:
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* large knowledge bases
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* static documentation
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* reference material
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### How they differ from `ctx`
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RAG answers questions like:
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> “What information might be relevant right now?”
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`ctx` answers a different question:
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> “What have we already decided, learned, or committed to?”
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Here are some key differences:
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| RAG | ctx |
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|-----------------------|-----------------------|
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| Statistical relevance | Intentional relevance |
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| Embedding-based | File-based |
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| Opaque retrieval | Explicit structure |
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| Runtime query | Persistent memory |
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`ctx` does not replace RAG.
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Instead, it defines a persistent context layer that RAG can optionally augment.
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> RAG belongs to the **data plane**; ctx defines the **context control plane**.
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It focuses on **project memory**, not knowledge search.
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---
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## Agent Frameworks
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Agent frameworks often provide:
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* task loops
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* tool orchestration
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* planner/executor patterns
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* autonomous iteration
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These systems are powerful, but they typically assume that:
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* memory is external
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* context is injected
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* state is transient
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### How they differ from `ctx`
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Agent frameworks answer:
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> “How should the agent act?”
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`ctx` answers:
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> “What should the agent remember?”
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Without persistent context, agents tend to:
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* rediscover decisions
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* repeat mistakes
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* lose architectural intent
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This is why `ctx` pairs well with loop-based workflows such as the
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[Ralph Wiggum technique](https://ghuntley.com/ralph/):
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* **Ralph** provides iteration
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* **ctx** provides continuity
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Together, loops become cumulative instead of forgetful.
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---
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## SDK-Level Context Objects
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Some SDKs expose "context" objects that exist:
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* inside a process
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* during a request
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* for the lifetime of a call chain
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These are extremely useful and completely different.
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SDK context objects:
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* are in-memory
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* disappear when the process ends
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* are not shared across sessions
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`ctx`:
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* survives process restarts
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* survives new chats
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* survives new days
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They share a name, not a purpose.
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---
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## Enterprise Context Platforms
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Enterprise platforms often provide:
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* centralized context services
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* dashboards
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* access control
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* organizational knowledge layers
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These tools are designed for:
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* teams
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* governance
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* compliance
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* managed environments
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`ctx` is intentionally:
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* local-first
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* file-based
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* dependency-free
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* CLI-driven
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* developer-controlled
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It does not require:
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* a server
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* a database
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* an account
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* a SaaS backend
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`ctx` optimizes for *individual and small-team workflows* where context should
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live next to code; **not** behind a service boundary.
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---
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## When `ctx` Is a Good Fit
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`ctx` works best when:
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* you want AI work to compound over time
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* architectural decisions matter
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* context must be inspectable
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* humans and AI must share the same source of truth
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* Git history should include *why*, not just *what*
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---
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## When `ctx` Is Not the Right Tool
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`ctx` is probably not what you want if:
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* you only need one-off prompts
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* you rely exclusively on RAG
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* you want autonomous agents without a human-readable state
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* you require centralized enterprise control
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* you want black-box memory systems
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These are valid goals; just different ones.
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---
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## Summary
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Many tools help AI *think*.
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`ctx` helps AI *remember*.
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**NOT** by storing thoughts,
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**BUT** by preserving intent.

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<svg xmlns="http://www.w3.org/2000/svg" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" class="lucide lucide-git-compare" viewBox="0 0 24 24"><circle cx="18" cy="18" r="3"/><circle cx="6" cy="6" r="3"/><path d="M13 6h3a2 2 0 0 1 2 2v7M11 18H8a2 2 0 0 1-2-2V9"/></svg>
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ctx and Similar Tools
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