Pieces for Developers

AI coding assistant with Long-Term Memory engine that captures context from IDEs, browsers, and collaboration tools, running locally with multi-LLM support.

IDE Extensions

Pieces for Developers: A Claude Code Alternative for AI Coding with Long-Term Memory

Pieces for Developers is an AI coding assistant and developer productivity platform built by Pieces Technologies, featuring a proprietary Long-Term Memory (LTM) engine that captures and surfaces context from browsers, IDEs, collaboration tools, and terminal sessions. It runs primarily locally on the developer's machine, supports a wide range of LLMs including models from Anthropic, OpenAI, and local Ollama deployments, and is free for individual use. As a Claude Code alternative, Pieces targets developers who lose context when context-switching across tools and want persistent, cross-surface AI memory that survives between sessions.

Pieces for Developers vs. Claude Code: Quick Comparison

	Pieces for Developers	Claude Code
Type	AI Copilot + Long-Term Memory engine	CLI Agent (terminal-based)
Pricing	Free (Individual) / Teams: contact for pricing	Usage-based via Anthropic API
LLM choice	OpenAI, Anthropic, Ollama (local), and more	Claude 3.5/3.7 Sonnet only
Offline / local models	Yes — via Ollama	No
Open source	No	No
Long-term memory	Yes — LTM captures 9 months of context	No — session-scoped only
Multi-file edits	Yes	Yes
Runs locally	Yes — desktop app required	No — cloud API

Key Strengths

Long-Term Memory Across All Developer Surfaces: Pieces' LTM engine passively captures context from browsers, code editors, Slack, Jira, terminal sessions, and other collaboration tools. This memory persists for 9 months, allowing the AI to answer questions like "why did I change this function last week?" or "what was that Stack Overflow link I found for this bug?" without the developer needing to remember or re-search. This is qualitatively different from Claude Code, which has no memory between sessions.
Local-First Processing for Privacy: Pieces processes data locally where possible and explicitly states it will never use your data for AI training. The desktop app runs on your machine, and the LTM can be configured to keep sensitive context off the cloud entirely. This privacy architecture makes Pieces viable for developers working on sensitive or proprietary codebases.
Multi-LLM Flexibility with Local Model Support: Pieces supports LLMs from OpenAI, Anthropic, and local models via Ollama. Developers can choose the model that best fits their task or privacy requirements, including fully local inference with no API calls. Claude Code is restricted to Anthropic's Claude models exclusively.
Free for Individual Developers: The Individual plan is completely free with no credit card required, includes 9 months of LTM context, and provides basic AI copilot assistance. This makes Pieces accessible for solo developers who want persistent memory capabilities without a subscription.

Known Limitations

Desktop App Dependency: Pieces requires installing and running a local desktop application, unlike cloud-first tools like Claude Code that work with just a terminal and an API key. This adds friction for developers who prefer minimal local installation or work on remote servers and cloud development environments.
Teams Pricing Is Not Public: While the Individual plan is free, the Teams plan pricing requires contacting sales. This lack of transparency makes it difficult for teams to budget without an exploratory sales call, in contrast to Claude Code's predictable per-token API pricing.
LTM Is Passive, Not Proactive: Pieces' Long-Term Memory captures context automatically, but the quality of recall depends on what was captured during active sessions. Developers who don't use Pieces consistently across all their tools will have incomplete memory, reducing the value of the LTM feature compared to developers who integrate it deeply into their entire workflow.

Best For

Pieces for Developers is the best Claude Code alternative for developers who struggle with context loss across tools and sessions — those who frequently search for previously found code snippets, reference earlier decisions, or need to explain past changes to an AI assistant. It is particularly strong for individual developers working on long-running projects where accumulated context provides compounding value over time. Teams working on shared codebases with cross-tool collaboration (Slack, Jira, GitHub) also benefit from the shared memory features available in the Teams plan.

Pricing

Individual: Free — 9 months of individual context, basic AI copilot assistance, email support, Windows/macOS/Linux
Teams: Contact for pricing — 9 months of shared team context, bring your own model or choose preferred LLMs (OpenAI, Anthropic, Ollama), priority phone and email support

Prices are subject to change. Check the official Pieces pricing page for current details.

Technical Details

Models supported: OpenAI models, Anthropic Claude, Ollama local models, and others
Context window: 9 months of LTM context (developer-scoped)
IDE / platform: VS Code plugin, JetBrains plugin, browser extensions, desktop app (Windows, macOS, Linux)
Offline / local models: Yes — via Ollama integration
Codebase indexing: Yes — cross-surface passive capture (IDEs, browsers, collaboration tools)
API access: Not publicly documented for external use
Open source: No
Data processing: Local where possible; no training on user data

How It Compares to Claude Code

Claude Code is a powerful interactive CLI agent with strong multi-file editing and code execution capabilities, but it operates within a single session with no memory of previous sessions. Every Claude Code session starts fresh. Pieces for Developers is fundamentally different: it is built around persistent, cross-session memory as its core feature, with AI assistance as a secondary capability layer. For developers who value the ability to ask "what did I do on this problem three weeks ago?" or "why is this function structured this way?", Pieces offers something Claude Code cannot. For raw agentic task execution — making changes across a codebase, running tests, refactoring — Claude Code is more capable and direct.

Conclusion

Pieces for Developers is a strong Claude Code alternative for developers who prioritize long-term context retention and cross-tool memory over pure agentic task execution power. It is free for individuals, runs locally for privacy, and supports multiple LLM providers including local models. Developers who feel Claude Code's session-scoped context is too limiting for complex, long-running projects will find Pieces' LTM capabilities uniquely valuable. For short, well-scoped coding tasks that fit in a single session, Claude Code remains more capable as an execution agent.

Sources

FAQ

Is Pieces for Developers free?

Yes. The Individual plan is completely free and includes 9 months of Long-Term Memory context, basic AI copilot assistance, and IDE integrations. The Teams plan requires contacting sales for pricing.

Does Pieces for Developers work with VS Code?

Yes. Pieces has a native VS Code extension that integrates LTM context and AI copilot capabilities directly into the editor. JetBrains IDEs and browser extensions are also available for cross-surface context capture.

How does Pieces compare to Claude Code?

Claude Code is an interactive terminal agent focused on executing code changes within a single session. Pieces is built around persistent Long-Term Memory that captures context across all developer tools over months. Pieces wins on memory continuity; Claude Code wins on agentic task execution depth.

Does Pieces send my code to the cloud?

Pieces processes data locally where possible. The company states it will never use your data for AI model training. The Long-Term Memory can be configured for local-only processing. Using local models via Ollama eliminates cloud API calls entirely.

What is Long-Term Memory (LTM) in Pieces?

LTM is Pieces' proprietary system that passively captures developer context from browsers, IDEs, terminal sessions, and collaboration tools (Slack, Jira, etc.). This context is stored locally for up to 9 months and made searchable via AI, allowing developers to retrieve past decisions, code snippets, and research without manual bookmarking or note-taking.