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Agentic Framework Landscape: When Each Framework Fits

ADK, Agno, Browser Use, Eigent, Letta, and mem0 occupy different layers of the agent stack — runtime, action space, memory, desktop product — so framework selection is a task-shape match, not a feature-count comparison.

The Selection Axes

A framework compresses your work when its primitives match your task signature and fights you when they do not. Five axes carry most of the decision:

  • Task horizon — single turn, single session, or workflows that pause for days and resume from checkpoint.
  • Action space — tools and code only, browser DOM, full desktop, or all of the above.
  • Memory model — none, ephemeral within session, server-stateful agent identity, or a memory layer composed over any runtime.
  • Deployment shape — Python/Node library, control-plane runtime with an HTTP surface, or a packaged desktop app.
  • Language and ecosystem fit — Python-first, polyglot SDKs, or TypeScript/Electron.

A feature-by-feature matrix flatters whichever framework is doing the comparison. The axes above decide whether you spend the next quarter shipping the agent or rewriting around the wrong primitive.

Per-Framework Fit

ADK (Google)

Code-first Python framework — Python 3.11+, Apache 2.0 (ADK README). The 2.0 release centres on two primitives: a graph-based Workflow Runtime with routing, fan-out/fan-in, loops, retry, state, human-in-the-loop, and nested workflows, and a Task API for structured agent-to-agent delegation. Workflows are durable: from Python 1.16, a resumable workflow persists checkpoints via DatabaseSessionService (SQLite or Cloud SQL) and resumes from an Invocation ID after interruption (ADK Resume Agents).

Fits when: multi-agent workflows with explicit state transitions, long-running processes needing pause/resume across days, Google Cloud or Gemini-first stacks.

Skip when: single-agent short tasks where a while loop suffices, non-Python stacks.

Agno

Python SDK positioned as "build, run, and manage agent platforms" (Agno README). The runtime ships 50+ HTTP/SSE/WebSocket endpoints, OpenTelemetry tracing, JWT-based RBAC for multi-tenant isolation, cron scheduling, and pre-built Slack/Telegram/WhatsApp interfaces. Third-party benchmarks report ~2 μs agent instantiation and ~3.75 KiB per agent — roughly four orders of magnitude lighter than LangGraph (Analytics Vidhya, 2025). The micro-overhead matters only when instantiating thousands of agents per request.

Fits when: production agent platforms needing multi-tenant isolation, RBAC, and observability out of the box.

Skip when: single embedded agent inside a larger app — a control plane is overkill.

Browser Use

Python (3.11+) agent that constrains the action space to a Playwright-controlled browser with a DOM-aware action vocabulary (Browser Use README). Benchmarked on 100 real-world browser tasks (browser-use/benchmark). DOM-based actions are more robust on standard layouts than coordinate-based computer-use models, which "don't yet work reliably enough in production" (Helicone: Browser Use vs Computer Use vs Operator).

Fits when: the task is genuinely web-shaped — scraping behind logins, form filling, multi-step flows requiring DOM reasoning.

Skip when: the same task is reachable via an API (always cheaper), the work spans the desktop OS, or per-step LLM costs at scale outweigh a hybrid Playwright+AI approach (Scrapfly: Stagehand vs Browser Use).

Eigent

Open-source desktop multi-agent application built on CAMEL-AI, packaged as a TypeScript/Electron app (Eigent README). Local model integration (vLLM, Ollama, LM Studio), SSO/RBAC, MCP integration, fully standalone deployment with no cloud account. The unit of distribution is an installed desktop app, not a library.

Fits when: end-user multi-agent productivity tooling, on-premise local deployment is a hard requirement (regulated industries, air-gapped environments).

Skip when: building an agent into another application — Eigent is the application, not a library.

Letta (formerly MemGPT)

Stateful agent runtime — Python/TypeScript SDKs and REST API — built around server-side agent identity (Letta README). Agents are created with explicit memory_blocks (human, persona) and an agent ID; subsequent requests address the same persistent agent. The model edits its own memory by calling memory_replace, memory_insert, memory_rethink tools — the "self-editing memory" model inherited from MemGPT (Letta blog: Agent Memory). Sleep-time compute uses idle periods to reorganise memory (smeuse: MemGPT/Letta stateful agents).

Fits when: the agent must persist a coherent identity across sessions and users; conversational apps with cross-session continuity.

Skip when: single-session tools where state has nowhere to be read back from — adoption adds a stateful service for no benefit and exposes you to the active Letta/Zep/mem0 benchmark dispute (Atlan: Zep vs Mem0).

mem0

Memory layer, not a runtime — Python and Node SDKs (mem0 README). Composes over any agent harness: you keep your loop and tools, mem0 handles extraction, storage, and retrieval. The April 2026 algorithm uses single-pass ADD-only extraction with entity linking and multi-signal retrieval (semantic + BM25 + entity), reporting 91.6 on LoCoMo and 94.8 on LongMemEval (Mem0 research). The trade-off is real: an independent comparison measured mem0 at ~67% vs full-context ~73% — a ~6% accuracy gap as the price for token efficiency (MindStudio: Mem0 vs OpenAI built-in memory).

Fits when: you have a working agent and want cross-session memory without rewriting; recall-heavy workloads with relaxed accuracy needs (personalisation, support history).

Skip when: memory accuracy is load-bearing (medical, legal, compliance) — full-context with retrieval over authoritative sources beats lossy summarisation; or the agent is single-session.

Why It Works

Framework primitives are designed for a specific task signature. ADK's graph-based Workflow Runtime works for long-running multi-agent processes because durable execution and explicit state transitions are exactly what workflows that pause for days need (ADK Resume Agents). Letta's server-stateful model works for cross-session identity because LLMs follow explicit memory-edit tool contracts more reliably than they manage arbitrary long-form context (Letta: Agent Memory). mem0's memory layer works for recall-heavy workloads because retrieval-augmented memory is linear-cost per turn instead of quadratic, paying for the accuracy gap with token economics (Mem0 research). The wrong framework wastes effort because its primitives are tuned for a different task — you reimplement what it does badly and route around what you do not need.

When This Backfires

  • No framework is the right answer. LangChain — a framework vendor — itself argues that abstractions can "obfuscate and make it hard to ensure the LLM has appropriate context at each step" (LangChain: How to think about agent frameworks). For a single-session chatbot or short-horizon code agent, a 100-line while loop and a tracing call often beats any of the six.
  • Single-session, short-horizon agent — adopting Letta or layering mem0 adds infrastructure and an accuracy gap for state that nothing reads back. Stateless is the right answer.
  • Non-Python primary stack — Agno, ADK, Browser Use are Python-first; mem0 and Letta ship Node SDKs but most of the six force the language choice. A Go or Rust team adopting them stands up a Python sidecar service that complicates ops more than it simplifies agent code.
  • Computer-control beyond the browser — Browser Use is browser-only; Eigent is desktop-packaged but Electron-bound. OS-level automation (native apps, SSH workflows, system-wide tasks) requires a different harness shape, typically Anthropic's Computer Use or local OS scripting.
  • Picking on benchmark headlines — the Letta–Zep–mem0 leaderboard dispute signals immaturity in evaluation methodology rather than a clear memory winner (Atlan: Zep vs Mem0). If your selection criterion is "best benchmark this quarter", you will re-pick every quarter.
  • High-stakes accuracy with mem0 — the ~6% accuracy gap is acceptable for personalisation chat, not for compliance-critical recall (MindStudio).

Example

A team building a code-review agent for internal PRs starts by asking the five axes:

  • Task horizon: one PR per invocation, single session — no checkpointing needed.
  • Action space: read repo, call tests, post comments — tools only, no browser.
  • Memory model: review conventions per repo persist; cross-PR review state does not.
  • Deployment shape: library inside an existing CI service, not a control plane.
  • Language: existing CI is Go.

None of the six fits naturally. ADK's Workflow Runtime is overkill for single-PR scope; Browser Use solves the wrong problem; Letta and mem0 add stateful infrastructure for state nothing reads back; Eigent is a desktop product; Agno is Python in a Go shop. The right answer is a direct Anthropic or Gemini SDK call from Go, with repo conventions loaded as AGENTS.md on each invocation — the same shape this site uses (AGENTS.md standard).

Compare with a customer-support agent handling cross-session conversation history for thousands of users: horizon is long, memory model is server-stateful per user, deployment is a control plane. Letta is now the closest fit; mem0 is the lighter alternative if accuracy can absorb the gap.

Key Takeaways

  • The six frameworks sit at different layers — runtime (ADK, Agno), action space (Browser Use), memory (mem0), stateful agent runtime (Letta), desktop product (Eigent) — so selection is a task-shape match, not a feature comparison.
  • Decide on five axes: task horizon, action space, memory model, deployment shape, language/ecosystem fit. The axes carry the decision; benchmarks rarely do.
  • No framework is often the right answer for single-session, short-horizon agents — production teams ship directly on model SDKs plus tracing.
  • Picking on benchmark headlines locks you into vendor methodology disputes that turn over quarterly; pick on integration shape instead.
Last reviewed: 2026-05-27
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