RAG Frameworks · Agent-Field

agentfield

AgentField is an open-source control plane that lets you build AI agents in Python, Go, or TypeScript and deploy them as scalable REST APIs. It handles agent orchestration, routing, async execution, memory, and observability without requiring queue setup or broker configuration.

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Source: GitHub — github.com/Agent-Field/agentfield

2.3k

GitHub stars

369

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Agent-Field/agentfield
Owner	Agent-Field
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	2.3k
Forks	369
Open issues	83
Latest release	v0.1.103 (2026-07-07)
Last updated	2026-07-07
Source	https://github.com/Agent-Field/agentfield

What agentfield is

Go-based control plane that exposes agent functions as REST endpoints, manages fan-out concurrency via asyncio/goroutines, provides cryptographic identity and audit trails, and supports multi-agent workflows with structured LLM outputs via Pydantic schemas. Integrates with Anthropic Claude, Gemini, and other LLM providers.

Quickstart

Get the agentfield source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Agent-Field/agentfield.git2cd agentfield3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-agent workflows with fan-out and synchronization

Recursive or parallel agent decomposition (e.g., research agent breaking questions into sub-questions). The control plane handles distributed tracing, retries, and result aggregation without explicit queue management.

Insurance claims, content moderation, or approval-based workflows

Human-in-the-loop scenarios where agents pause for approval via webhook, then resume. Built-in audit trails and identity-aware execution satisfy compliance and governance requirements.

Rapid agent prototyping from prompt specifications

The prompt-to-production flow via Harness Orchestration (Claude Code, Cursor, etc.) enables teams to generate multi-agent backends from natural language descriptions with Docker Compose stacks.

Implementation considerations

Agent code is written in plain Python/Go/TypeScript functions; no DSL or YAML graph wiring, but async patterns (asyncio.gather, concurrency primitives) must be understood for correct fan-out behavior.
Each agent registers with the control plane and receives a cryptographic identity; credentials, key rotation, and identity-aware access control are implicit but operationalization details require review.
Structured LLM outputs rely on Pydantic schemas; schema mismatches or LLM hallucinations can cause runtime errors; error handling and fallback strategies should be planned.
Pause/approval workflows suspend execution and emit webhooks; external approval services must be resilient and integrate with the control plane's callback mechanism.
Recursive agent calls fan out through the control plane; unbounded recursion (e.g., missing depth limits) can exhaust resources; depth caps or circuit breakers are developer responsibility.

When to avoid it — and what to weigh

Simple, single-agent inference tasks — If you only need to wrap a single LLM call as an API, lighter frameworks (FastAPI + direct LLM client) require less operational overhead.
Mature, feature-complete agent orchestration with vendor lock-in acceptable — Established platforms (e.g., Anthropic's build-in APIs, LangGraph, Temporal) may offer more extensive integrations and ecosystem maturity for production at scale.
Strict offline or air-gapped deployment requirements — AgentField's documentation references cloud-based LLM providers (Anthropic, Gemini); local-only or fully disconnected deployment constraints are not clearly addressed.
Teams unfamiliar with Go, async Python, or Kubernetes — Production deployment assumes comfort with containerization and cloud-native DevOps; the SDK abstracts complexity, but operational runbooks may require Go expertise.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license permitting commercial use, modification, and distribution, provided the full license text and copyright notice are included and no trademark rights are granted.

Apache-2.0 permits commercial deployment without explicit permission. However, review the full LICENSE file for indemnification clauses, disclaimer of warranties, and liability limitations. If modifying the source, derivative works must also carry the Apache-2.0 license. No proprietary licensing or SLA terms are evident in the provided data.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Agents receive cryptographic identities and all executions produce audit trails; tamper-proof logging is claimed but not detailed in the excerpt. No security audit results or CVE history provided. LLM API keys and agent credentials must be managed securely (likely via environment or secrets manager). Recursive agent calls and approval webhooks introduce network-based attack surfaces; input validation, rate limiting, and CSRF protection should be verified. Identity-aware access control is mentioned but enforcement mechanisms are not clarified.

Alternatives to consider

LangGraph (LangChain)

Mature Python-first agent orchestration with graph-based workflows, extensive LLM integrations, and strong community. More feature-rich but requires explicit YAML/code graph definition.

Temporal / Cadence

Distributed workflow orchestration engine with strong durability, retry, and state management guarantees. Heavier operational overhead but battle-tested at scale; language-agnostic.

Anthropic's API + direct SDKs

Lightweight, vendor-integrated approach for teams already committed to Anthropic. No separate control plane, lower infrastructure overhead, but less multi-agent orchestration support.

Software development agency

Build on agentfield with DEV.co software developers

Review the full GitHub repository, run the quick-start locally, and assess fit against your team's DevOps and security requirements. Confirm commercial support, SLA, and managed-service options with the maintainers.

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agentfield FAQ

Do I need a broker or queue system (Redis, RabbitMQ) to run AgentField?

No; the control plane handles queuing, retries, and coordination internally. However, distributed deployment of the control plane itself (HA, failover) may introduce queueing dependencies; this is not detailed in the excerpt.

What LLM providers are supported?

Documentation references Anthropic Claude, Google Gemini, and others via model identifiers. The excerpt shows examples with Claude but does not list all integrated providers; refer to SDK documentation for completeness.

Can I run AgentField on-premises or offline?

The control plane itself runs on your infrastructure (Kubernetes, Docker, etc.), but agents call external LLM APIs (Anthropic, Gemini). Fully offline deployment is not addressed in the provided data; requires review.

Is there a commercial support or managed service option?

Not stated in the provided data. The project is open-source under Apache-2.0; refer to agentfield.ai or contact the maintainers for commercial terms, SLAs, or managed hosting options.

Software developers & web developers for hire

DEV.co helps companies turn open-source tools like agentfield into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your rag frameworks stack.

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