Amy by Jellyfish vs Pi Coding Agent: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Amy by Jellyfish and Pi Coding Agent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Amy by Jellyfish
Jellyfish
An always-on AI talent sourcer for staffing agencies that turns client briefs into searches, evidence-backed matches, outreach and managed replies.
Key features
- Client Brief Translation: Turns hiring conversations, job descriptions and hiring-manager tradeoffs into a living search plan, separating must-haves from flexible requirements.
- Search Beyond LinkedIn: Researches GitHub, Google Scholar, open-source projects, technical communities and the open web to find candidates who are not in the same database everyone else uses.
- Evidence-Backed Matching: Explains every match with role-specific evidence — technical depth, relevant scale, leadership, current intent — that recruiters can review and challenge.
- Multi-Channel Outreach Sequences: Runs personalized email, LinkedIn connections, follow-ups and InMail on a scheduled cadence, personalized from the candidate's own work.
- Candidate Inbox Management: Reads replies, answers routine questions, summarizes threads and routes qualified candidates to the right recruiter.
- Hiring-Manager Coordination: Keeps progress and evidence ready to share so recruiters do not rebuild context before every client update.
- Adjustable Autonomy: Run the full workflow on autopilot or keep approval gates on candidates, messages or client updates, changeable at any time.
- Feedback Learning Loop: Every hiring-manager decision and recruiter note reshapes the next search, improving match quality over time.
Best for
- Agency Sourcing Desk Coverage: Keep searches, inboxes and follow-ups moving across roles, markets and time zones without adding sourcer headcount.
- Hard-to-Fill Technical Roles: Find engineers and researchers through their GitHub, publications and community work when job titles and resumes miss them.
- Client Intake to Shortlist: Convert a fresh client brief into a qualified, evidence-backed candidate shortlist without manual Boolean search cycles.
- Outreach at Volume: Run personalized multi-touch sequences across many candidates while keeping each message grounded in that person's actual work.
- Reply Triage: Hand routine candidate questions and scheduling back-and-forth to the agent so recruiters only handle qualified conversations.
- Client Reporting: Produce weekly hiring-manager updates with the evidence and pipeline state already attached.
Pi Coding Agent
Earendil Works (earendil-works)
A terminal-based, extensible TypeScript coding agent and toolkit for building agentic developer workflows.
Key features
- Unified LLM Providers: Abstracts communication with multiple LLM providers and supports provider authentication via /login or environment variables (e.g., ANTHROPIC_API_KEY), letting the agent switch between hosted and local models.
- Built-in Coding Tools: Ships a production-ready set of built-in tools for file operations, search (grep/find), shell execution (bash), read/write/edit, and sharing code/snippets to streamline common developer tasks inside the agent loop.
- Session Persistence & Compaction: Persists full session history to JSONL files and performs automatic in-memory compaction (summarization) when context approaches model window limits while preserving full logs on disk.
- TypeScript Extensibility: Extension system and resource loader for adding custom TypeScript extensions, skills, prompt templates, themes, and pi packages to extend agent capabilities and add custom tools.
- Programmatic SDK: Exposes programmatic APIs (createAgentSession, createAgentSessionRuntime, InteractiveMode, SessionManager) to embed agent sessions into other tooling or CI environments.
- Terminal UI & Editor Integration: Provides a terminal UI (pi-tui) and an Emacs frontend with keybindings, syntax highlighting, and navigation designed for interactive coding workflows.
- Security & Containerization Guidance: Explicitly documents permission model (runs with user/process permissions) and offers recommended containerization/sandbox patterns for stronger isolation in production or CI.
- Unified LLM provider layer (pi-ai) abstracting multiple model providers and authentication (supports API keys and /login flows)
- Agent orchestration loop (pi-agent-core) enabling tool calling and turn management
- Production-ready coding agent runtime (pi-coding-agent) with built-in tools (file ops, shell commands, read/write/edit/grep/find/ls, etc.)
- Programmatic SDK: createAgentSession, createAgentSessionRuntime, createAgentSessionServices, InteractiveMode, SessionManager and factory hooks
- Terminal UI (pi-tui) and CLI entrypoint (pi) for interactive sessions
- Session persistence to JSONL + automatic context compaction to manage long histories and model context windows
- Extensibility via TypeScript extensions, custom tools, skills, prompts, themes, and resource loaders (DefaultResourceLoader)
- Integrations: Emacs frontend, GitHub Action for running Pi in CI/CD, examples and extension patterns in repo
- Security / deployment guidance: no built-in permission sandboxing (runs with user permissions) — recommends containerization/sandboxing patterns
- Supply-chain hardening practices for npm (save-exact, lockfile policies, audit commands) and release tooling
Best for
- Interactive Terminal Pair-Programming: Use Pi in a project directory to iteratively write, refactor, and test code using built-in file and shell tools without leaving the terminal.
- Custom Agent Frameworks: Build bespoke agent behaviors and pipelines by composing pi-agent-core, adding custom tools or skills, and orchestrating multi-agent workflows (e.g., planner/builder/reviewer chains).
- CI/CD Automation: Run Pi inside CI (via community GitHub Actions) to automate code generation, PR descriptions, or repository maintenance tasks as part of build pipelines.
- Local Model & Hybrid Deployment: Connect Pi to local LLMs (Ollama, vLLM, etc.) or hosted providers, enabling offline or hybrid workflows for sensitive code or data while following containerization recommendations.
- Embedding in Editors and Integrations: Integrate Pi with Emacs or other editor workflows to provide chat-driven code navigation, edits, and file exploration from within the editor.
- Open Source Session Sharing & Research: Share anonymized OSS agent sessions to improve agent tooling and model behavior by contributing real-world agent interactions and failure/fix examples.
- Interactive terminal coding assistance and REPL-style development sessions
- Automating repository tasks and review workflows in CI/CD using the pi GitHub Action
- Embedding a coding assistant into developer tools or interfaces (e.g., Emacs integration)
- Building custom agentic workflows and tools that combine shell, file, and API operations
- Running local or remote LLM-backed coding agents (supports local LLM configuration and provider fallbacks)
- Prototyping or shipping production agent systems with session persistence and context compaction