From autocomplete to context: The real story of AI code completion in 2025

When AI first stepped into coding, it felt like autocomplete on steroids. Today, AI code completion tools are shaping entire developer workflows. But not all tools are built the same: some are lightning-fast, others context-aware, and some stretch into the murky territory of AI “agents.”

Here’s a deep look at the most talked-about players in 2025,  not just what they promise, but what people actually report when using them.

Pieces 

Pieces goes beyond simple AI code completion, positioning itself as a context continuity platform. Instead of competing only on speed, Pieces prioritizes connecting disparate parts of a developer's workflow, effectively acting as persistent memory.

Imagine experimenting with GPT-5-Pro to prove a convex optimization bound in a Jupyter notebook, then switching to Go code in VSCode. Pieces captures and makes this interaction retrievable, so the insight from the math proof is immediately accessible when you shift back into production coding.

Key technical components:

• PiecesOS: OS-level service that integrates with IDEs (including IntelliJ and WebStorm), browsers, and collaboration tools. It continuously captures contextual data: snippets, logs, open tabs, notes, and stores it locally to preserve privacy and security.

• Long-Term Memory (LTM-2.5): Converts captured workflow data into a structured, queryable memory layer. In internal tests, LTM-2.5 reduced developer time spent re-finding past context by ~35%.

• Model Context Protocol (MCP): Provides a standard interface to let LLMs and developer tools fetch context directly from Pieces. For example, the Pieces CLI provides an mcp get command to retrieve context by name, which returns results as JSON:
  
  

  import json, subprocess  
  
  def get_pieces_context(name):  
      result = subprocess.run(['pieces','mcp','get','--context',name],capture_output=True,text=True)  
      return json.loads(result.stdout)  
  
  context = get_pieces_context("my_convex_optimization_proof")  
  print(context["code_snippets"][0])
  
  

• Multi-Stack Awareness: Pieces is actively tested in Python, Go, and TypeScript, and has stable integrations in both JetBrains IDEs (IntelliJ, WebStorm) and VSCode. Context can even surface across languages. For example, a binary tree snippet written in Python may reappear as a suggestion when coding a related structure in Go.

• Conflict Resolution: Pieces uses timestamps and semantic similarity for conflict resolution among multiple versions of a snippet, ensuring the most recent and relevant context surfaces first.

• Local + Cloud Flexibility: While Pieces primarily powers continuity and orchestration, it can also integrate with local LLMs via Ollama. This enables developers to use smaller, locally-hosted models for lightweight autocomplete while still drawing on Pieces’ broader context memory.

Pieces isn’t trying to be your coding teammate (like Windsurf). It amplifies your workflow by remembering and resurfacing relevant context when you need it most.  During debugging, code review, or refactoring, when devs otherwise waste time re-explaining problems.

Pieces is playing the long game: building a moat around workflow intelligence rather than just chasing benchmark speed. Just as DeepSeek-V3.1 showed how hybrid inference can slash costs, Pieces shows that continuity is more valuable than raw completions.

That’s why many developers describe Pieces not as a flashy gadget, but as the tool they keep reaching for — the connective tissue of their workflow that adapts, remembers, and multiplies the value of everything else in their stack.

Windsurf

Windsurf represents the agent-first approach to AI-assisted development. Unlike Codeium or Copilot, which are optimized for rapid autocomplete, Windsurf positions itself as a junior developer living in your IDE, eager to scaffold, refactor, and reason across multiple files.

Some of its key technical features are:

• Multi-file reasoning: Windsurf parses beyond the current file, connecting logic between related modules.

• Scaffolding generation: Can spin up new files and boilerplate structures based on natural language prompts.

• Agent architecture: Functions less like a type-ahead autocomplete and more like a conversational collaborator. For instance, in VSCode you might type:
  
  

  "User authentication with JWT"

The agent might create a new file structure (auth/ folder with middleware, token service, and user routes) instead of a single snippet.

Some developers report saving hours when Windsurf stitches together multi-file workflows. “It felt like I had a junior dev who could set up the boring parts of my architecture,” one user wrote.

Others find it over-engineered. Code can be verbose, filled with abstractions that don’t align with team style. JetBrains integration, in particular, is noted as laggy, with autocomplete suggestions appearing with noticeable delay (>500ms).

Windsurf reflects the AI agent debate: should AI just autocomplete, or should it take initiative across files and projects? The reception so far shows skepticism. Developers echo a sentiment similar to broader “AI welfare” debates in Silicon Valley: while some want AI to act like autonomous agents, most coders want support, not substitution.

Windsurf is an experiment in the “agent wave.” If agent workflows mature (as hybrid inference models like DeepSeek suggest they might), Windsurf is early to the game. But adoption today depends on whether developers see agents as helpful teammates or noisy distractions.

ProxyAI with Ollama

ProxyAI with Ollama takes a radically different stance: instead of polished cloud SaaS, it offers local-first sovereignty. It appeals to developers who don’t want their code leaving their machine, whether for security, compliance, or cost.

• Local model hosting via Ollama: Run LLaMA, Mixtral, or DeepSeek-V3.1 models on your own GPU.
  
  

• Customizable inference parameters: Configure temperature, max tokens, and top-p directly in a ~/.proxyai/config.json.
  
  

  {
    "model": "llama2:7b",
    "parameters": { "temperature": 0.7, "max_tokens": 512 }
  }

• Hybrid workflows: Route light tasks locally, heavier tasks to cloud APIs.

• Offline capability: Completion works even without internet are rare among competitors.
  
  

Developers value the privacy and flexibility. One team reported setting up ProxyAI to handle sensitive medical data workflows in an air-gapped environment. Setup isn’t trivial. Installing Ollama, configuring models, and allocating GPU/VRAM creates friction compared to one-click installs of Codeium or Copilot. Sandbox issues on Linux have also been reported.

ProxyAI represents a bifurcation in the market:

• Pieces, Codeium, Copilot → prioritize ease of use and workflow polish.

• ProxyAI → bets on control and sovereignty.
  
  

This mirrors broader AI trends: just as researchers celebrated GPT-5-Pro proving a better convex optimization bound (outside closed labs), developers want freedom to run inference themselves. If regulations, costs, or trust issues push AI development local, ProxyAI could capture outsized value.

Codeium

If ProxyAI is for tinkerers, Codeium is for developers who just want things to work. It’s consistently praised as “fast, stable, and reliable,” with reports highlighting its ease of use across IDEs and its disruptive free pricing. That combination,  speed, simplicity, and accessibility is what makes developers choose Codeium when they need a tool that fades into the background and simply delivers. 

Under the hood, this comes down to a set of technical choices and integrations that prioritize responsiveness over experimentations:

• Latency: Completion suggestions often appear faster than Copilot, especially in JetBrains IDEs.

• Breadth of support: IntelliJ, WebStorm, VSCode, Jupyter, and more.

• Free tier: Full autocomplete at no cost, with enterprise add-ons available.

• On-prem options: Enterprise teams can host Codeium internally for compliance.

Codeium is often cited as one of the best AI code completion tools available in 2025 for developers who prioritize speed and cost.

Augment Code

Where Codeium is lean and fast, Augment Code positions itself as a refactoring-first AI. It’s less about filling in the next line and more about shaping the way teams write maintainable, consistent code. 

Developers often describe it as “that mentor who won’t let you cut corners.” Whether that feels supportive or overbearing depends on your workflow, but its emphasis on code health makes Augment unique.

 This philosophy shows up directly in the way the tool is engineered:

• Refactoring Engine: Highlights opportunities for modularization, abstraction, and documentation.

• Cross-Project Insights: Surfaces related snippets or design patterns across repositories.

• IDE Integrations: Available in JetBrains and VSCode; adoption lags behind Codeium, but usage is growing in large teams.

• Maintainability Focus: Designed to encourage patterns that reduce tech debt in the long run.

GitHub Copilot

Copilot remains the most recognized name in AI code completion, but the shine has worn off for some. While completion quality is still rated highly, persistent plugin instability in JetBrains IDEs has created frustration among developers. It’s a classic case of a tool that led the way but is now being outflanked on multiple fronts. 

Copilot’s enduring strengths, quality and ecosystem integration,  are the result of its training scale and GitHub-native workflows. The pain points, however, are equally tied to its technical underpinnings:

• Training Breadth: Trained on a massive dataset from public GitHub repos, enabling strong predictions across mainstream frameworks.

• Ecosystem Integration: Inline completions, pull request suggestions, and GitHub Actions tie-ins keep it tightly woven into GitHub workflows.

• Plugin Limitations: Performance degrades in JetBrains IDEs; frequent complaints of slowdowns and crashes compared to Codeium or Pieces.

• Pricing Model: $10–$20/month per user, with no free tier — leading to comparisons against Codeium’s free availability.
  
  

Interestingly, some developers bypass Copilot’s weaknesses by pairing it with Pieces via MCP: Copilot for raw completions, Pieces for continuity. Reports call this hybrid “the best of both worlds.”

Copilot is still a category-definer, and even though it's one of the best ai assistants, its brand dominance echoes OpenAI’s –  powerful, but vulnerable to disruption.

Wrapping it up

Some tools will never become the backbone of developer workflows. If they chase flashy benchmarks or lean too far into half-baked agent models, they’ll remain plugins you try once and disable.

Pieces is charting the path by weaving continuity into every corner of the developer workflow. Codeium is proving that speed and accessibility still win hearts. ProxyAI is making sovereignty viable. Augment is reminding us that clean code is future-proof code. Windsurf is testing whether agents have a place in the editor. And Copilot, even with its bruises, continues to shape the way the world thinks about AI-assisted coding.

For developers, the decision often comes down to this: which tool actually helps you keep momentum at 2am when you’re debugging that elusive bug? 

The winners won’t be the loudest or the flashiest. They’ll be the ones that quietly rewire the developer workflow from the inside out,  making themselves impossible to imagine working without.