https://github.com/agent-field/agentfield

Build, run and scale AI agents like API and microservices - observable,auditable and identity-aware from day one.
https://github.com/agent-field/agentfield
agent agent-auth agent-authentication agent-indentity agent-scaling agentic-ai ai ai-backend aiagent anthropic cloud-native genai go kubernetes llm multiagent multiagent-systems python rag typescript
Last synced: 11 days ago
JSON representation
Build, run and scale AI agents like API and microservices - observable,auditable and identity-aware from day one.
Host: GitHub
URL: https://github.com/agent-field/agentfield
Owner: Agent-Field
License: apache-2.0
Created: 2025-11-05T02:04:44.000Z (7 months ago)
Default Branch: main
Last Pushed: 2026-05-28T22:34:22.000Z (17 days ago)
Last Synced: 2026-05-29T00:16:43.458Z (17 days ago)
Topics: agent, agent-auth, agent-authentication, agent-indentity, agent-scaling, agentic-ai, ai, ai-backend, aiagent, anthropic, cloud-native, genai, go, kubernetes, llm, multiagent, multiagent-systems, python, rag, typescript
Language: Go
Homepage: http://www.agentfield.ai
Size: 256 MB
Stars: 2,014
Watchers: 21
Forks: 321
Open Issues: 85
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Security: SECURITY.md
- Support: SUPPORT.md
- Agents: AGENTS.md
Awesome Lists containing this project

README

          


# AgentField — The AI Backend

### **Build and scale AI agents like APIs. Deploy, observe, and prove.**

*AI has outgrown chatbots and prompt orchestrators. Backend agents need backend infrastructure.*

[![Stars](https://img.shields.io/github/stars/Agent-Field/agentfield?style=flat&logo=github&logoColor=e8e5dc&color=0c0b09&labelColor=8b7355)](https://github.com/Agent-Field/agentfield/stargazers)

[![License](https://img.shields.io/badge/license-Apache%202.0-0c0b09.svg?style=flat&labelColor=8b7355)](LICENSE)

[![Downloads](https://img.shields.io/endpoint?url=https%3A%2F%2Fgist.githubusercontent.com%2Fsantoshkumarradha%2Fd98e2ad73502b4075f6a5f0ae4f5cae5%2Fraw%2Fbadge.json&style=flat&logo=download&logoColor=e8e5dc&color=0c0b09&labelColor=8b7355&cacheSeconds=3600)](https://github.com/Agent-Field/agentfield)

[![Coverage](https://img.shields.io/endpoint?url=https%3A%2F%2Fgist.githubusercontent.com%2Fsantoshkumarradha%2F433fb09c2d54c3c2589125cfd3eb14a2%2Fraw%2Fbadge.json&style=flat&logo=github&logoColor=e8e5dc&color=0c0b09&labelColor=8b7355&cacheSeconds=3600)](https://github.com/Agent-Field/agentfield/actions/workflows/coverage.yml)

[![Last Commit](https://img.shields.io/github/last-commit/Agent-Field/agentfield?style=flat&logo=git&logoColor=e8e5dc&color=0c0b09&labelColor=8b7355)](https://github.com/Agent-Field/agentfield/commits/main)

[![Discord](https://img.shields.io/badge/discord-join%20us-0c0b09.svg?style=flat&labelColor=8b7355&logo=discord&logoColor=e8e5dc)](https://discord.gg/aBHaXMkpqh)

**[Docs](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-docs)** · **[Quick Start](https://agentfield.ai/docs/learn/quickstart?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-quickstart)** · **[Python SDK](https://agentfield.ai/docs/reference/sdks/python?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-python-sdk)** · **[Go SDK](https://agentfield.ai/docs/reference/sdks/go?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-go-sdk)** · **[TypeScript SDK](https://agentfield.ai/docs/reference/sdks/typescript?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-typescript-sdk)** · **[REST API](https://agentfield.ai/docs/reference/sdks/rest-api?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-rest-api)** · **[Examples](#built-with-agentfield)** · **[Discord](https://discord.gg/aBHaXMkpqh)**













AgentField is an open-source control plane that lets you build AI agents callable by any service in your stack - frontends, backends, other agents, cron jobs - just like any other API. You write agent logic in Python, Go, or TypeScript. AgentField turns it into production infrastructure: routing, coordination, memory, async execution, and cryptographic audit trails. Every function becomes a REST endpoint. Every agent gets a cryptographic identity. Every decision is traceable.



https://github.com/user-attachments/assets/9fb7b1cf-26de-4b9b-9ba2-917252cc26ec

_{One prompt → a running containerized production ready multi-agent backend. No glue code, start using the agent API!}



## Build production agents with a prompt.

**Describe the system in one line. Get a production-ready multi-agent backend.** Works in Claude Code, Codex, Gemini CLI, OpenCode, Aider, Windsurf, and Cursor.

```bash

curl -fsSL https://agentfield.ai/install.sh | bash

```

Then in your coding agent, paste any spec with /agentfield :

```text

/agentfield Build a claims processor with risk scoring, pattern detection,

and human approval for low-confidence decisions.

```

You get a Docker Compose stack wired up end-to-end — the agent, the control plane, and a production ready REST API endpoint you can paste and `curl` into a terminal to try it. [See it in action →](https://agentfield.ai/docs/learn/build-with-claude-code?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-prompt-to-production)

## The DX you get

*Best in class Python (or Go / TypeScript) DX. With least intrusive abstraction. No DSL, no YAML, no graph wiring.*

```python

from agentfield import Agent, AIConfig

from pydantic import BaseModel

app = Agent(

    node_id="claims-processor",

    version="2.1.0",# Canary deploys, A/B testing, blue-green rollouts

    ai_config=AIConfig(model="anthropic/claude-sonnet-4-20250514"),

)

class Decision(BaseModel):

    action: str# "approve", "deny", "escalate"

    confidence: float

    reasoning: str

@app.reasoner(tags=["insurance", "critical"])

async def evaluate_claim(claim: dict) -> dict:

    # Structured AI judgment - returns typed Pydantic output

    decision = await app.ai(

        system="Insurance claims adjuster. Evaluate and decide.",

        user=f"Claim #{claim['id']}: {claim['description']}",

        schema=Decision,

    )

    if decision.confidence < 0.85:

        # Human approval - suspends execution, notifies via webhook, resumes when approved

        await app.pause(

            approval_request_id=f"claim-{claim['id']}",

            approval_request_url=f"https://internal.acme.com/approvals/claim-{claim['id']}",

            expires_in_hours=48,

        )

    # Route to the next agent - traced through the control plane

    await app.call("notifier.send_decision", input={

        "claim_id": claim["id"],

        "decision": decision.model_dump(),

    })

    return decision.model_dump()

app.run()

# This single line exposes: POST /api/v1/execute/claims-processor.evaluate_claim

# The agent auto-registers with the control plane, gets a cryptographic identity, and every

# execution produces a verifiable, tamper-proof audit trail.

```

> **What you just saw:** `app.ai()` calls an LLM and returns structured output. `app.pause()` suspends for [human approval](https://agentfield.ai/docs/build/execution/human-in-the-loop?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-human-in-the-loop). `app.call()` routes to other agents through the control plane. `app.run()` auto-exposes everything as REST. [Read the full docs →](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-read-full-docs)

Prefer to scaffold by hand? (Python / Go / TypeScript / Docker)

```bash

af init my-agent --defaults                            # Scaffold agent

cd my-agent && pip install -r requirements.txt

af server          # Terminal 1 → Dashboard at http://localhost:8080

python main.py     # Terminal 2 → Agent auto-registers

```

```bash

# Call your agent

curl -X POST http://localhost:8080/api/v1/execute/my-agent.demo_echo \

  -H "Content-Type: application/json" \

  -d '{"input": {"message": "Hello!"}}'

```

```bash

# Go

af init my-agent --defaults --language go && cd my-agent && go run .

# TypeScript

af init my-agent --defaults --language typescript && cd my-agent && npm install && npm run dev

# Docker (control plane only)

docker run -p 8080:8080 agentfield/control-plane:latest

```

[Deployment guide →](https://agentfield.ai/docs/reference/deploy?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-deploy) for Docker Compose, Kubernetes, and production setups.

## How AgentField fits in your stack

Most agent tools help you **write** agent logic. AgentField is what **runs** it in production — the operating layer that makes agents callable by software, durable across failures, governed by policy, and provable by audit.

|  | **Frameworks**
_{LangChain · CrewAI · PydanticAI · OpenAI Agents SDK} | **Workflow engines**
_{Temporal · Airflow} | **Visual builders**
_{n8n · Zapier} | **AgentField** |

|---|:-:|:-:|:-:|:-:|

| Build agent logic (prompts, tools, structured output) | ● | — | — | ● |

| Callable production ready REST APIs out-of-box | — | ◐ | ● | ● |

| Async + retries + webhooks | — | ● | ◐ | ● |

| Memory scopes (global · agent · session · run) | ◐ | — | — | ● |

| Service discovery + cross-agent calls | — | — | — | **●** |

| Distributed agents | — | — | — | **●** |

| Tamper-proof, verifiable audit per execution | — | — | — | **●** |

| Harness orchestration (Claude Code · Codex · CLI) | — | — | — | **●** |

| Identity and Access Management (IAM) for agents | — | — | — | ● |

| Fleet observability (DAGs · metrics · traces) | — | ◐ | — | ● |

| Multi-language SDKs (Python · Go · TypeScript) | ◐ | ● | — | ● |

_{● full · ◐ partial · — not the focus}

**Use a framework when you're proving behavior.** Use AgentField when agents need to be production systems — callable by software, coordinating across services, surviving failures, and governed under audit.

[Full comparison & decision guide →](https://agentfield.ai/docs/learn/vs-frameworks?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-vs-frameworks)

## What You Get

**Build** - Python, Go, or TypeScript. Every function becomes a REST endpoint.

- **[Reasoners & Skills](https://agentfield.ai/docs/build/building-blocks/reasoners?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-reasoners)** - `@app.reasoner()` for AI judgment, `@app.skill()` for deterministic code

- **[Structured AI](https://agentfield.ai/docs/reference/sdks/python?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-structured-ai)** - `app.ai(schema=MyModel)` → typed Pydantic/Zod output from any LLM

- **[Harness](https://agentfield.ai/docs/build/intelligence/harness?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-harness)** - `app.harness("Fix the bug")` dispatches multi-turn tasks to Claude Code, Codex, Gemini CLI, or OpenCode

- **[Cross-Agent Calls](https://agentfield.ai/docs/build/coordination/cross-agent-calls?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-cross-agent-calls)** - `app.call("other-agent.func")` routes through the control plane with full tracing

- **[Discovery](https://agentfield.ai/docs/reference/sdks/python?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-discovery)** - `app.discover(tags=["ml*"])` finds agents and capabilities across the mesh. `tools="discover"` lets LLMs auto-invoke them.

- **[Memory](https://agentfield.ai/docs/build/coordination/shared-memory?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-memory)** - `app.memory.set()` / `.get()` / `.search()` - KV + vector search, four scopes, no Redis needed

**Run** - Production infrastructure for non-deterministic AI.

- **[Async Execution](https://agentfield.ai/docs/build/execution/async?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-async-execution)** - Fire-and-forget with webhooks, SSE streaming, retries. No timeout limits - agents run for hours or days.

- **[Human-in-the-Loop](https://agentfield.ai/docs/build/execution/human-in-the-loop?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-human-in-the-loop)** - `app.pause()` suspends execution for human approval. Crash-safe, durable, audited.

- **[Canary Deployments](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-canary-deployments)** - Traffic weight routing, A/B testing, blue-green deploys. Roll out agent versions at 5% → 50% → 100%.

- **[Observability](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-observability)** - Automatic workflow DAGs, Prometheus `/metrics`, structured logs, execution timeline.

**Govern** - IAM for AI agents. Identity, access control, and audit trails - built in.

- **[Cryptographic Identity](https://agentfield.ai/docs/build/governance/identity?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-crypto-identity)** - Every agent gets a W3C DID (decentralized identifier) - not a shared API key. Agents authenticate to each other the way services authenticate with mTLS, but with cryptographic signatures that travel with the agent.

- **[Verifiable Credentials](https://agentfield.ai/docs/build/governance/credentials?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-verifiable-credentials)** - Tamper-proof receipt for every execution. Offline-verifiable: `af vc verify audit.json`.

- **[Policy Enforcement](https://agentfield.ai/docs/build/governance/policy?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-policy-enforcement)** - Tag-based policy gates with cryptographic verification. "Only agents tagged 'finance' can call this" - enforced by infrastructure, not prompts.

[See the full production-ready feature set →](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-full-features)







▼ Click to expand full capabilities


#### AI & LLM

| Feature | How |

|---|---|

| Structured output (Pydantic/Zod) | `app.ai(schema=MyModel)` |

| Multi-turn coding agents | `app.harness("task", provider="claude-code")` |

| LLM auto-discovers agents and tools | `app.ai(tools="discover")` |

| Multimodal (text, image, audio) | `app.ai("Describe", image_url="...")` |

| Streaming responses | `app.ai("...", stream=True)` |

| 100+ LLMs via LiteLLM | `AIConfig(model="anthropic/claude-sonnet-4-20250514")` |

| Temperature, max tokens, format | `app.ai(..., temperature=0.2)` |

#### Agent Mesh & Discovery

| Feature | How |

|---|---|

| Cross-agent calls with tracing | `app.call("agent.func", input={...})` |

| Discover agents by tag (wildcards) | `app.discover(tags=["ml*"])` |

| Discover by health status | `app.discover(health_status="active")` |

| Agent routers (namespacing) | `AgentRouter(prefix="billing")` |

| Auto context propagation | Workflow, session, actor IDs forwarded |

| Parallel agent execution | `asyncio.gather(app.call(...), ...)` |

| Auto-registration on startup | Service mesh with zero config |

#### Execution Engine

| Feature | How |

|---|---|

| Sync execution (REST) | `POST /api/v1/execute/{agent}.{func}` |

| Async (fire-and-forget) | `POST /api/v1/execute/async/{agent}.{func}` |

| Webhooks + HMAC-SHA256 signing | `AsyncConfig(webhook_url="...", secret="...")` |

| SSE streaming (real-time) | `/api/v1/execute/stream/{id}` |

| No timeout limits (hours/days) | Control plane allows unlimited duration |

| Execution polling | `GET /api/v1/executions/{id}` |

| Batch status checks | `POST /api/v1/executions/batch-status` |

| Progress updates mid-execution | Intermediate payloads during long tasks |

| Auto retries + exponential backoff | Transparent - control plane handles |

| Backpressure + queue depth limits | Fair scheduling, circuit breakers |

| Durable queue (PostgreSQL) | Atomic lease-based processing |

#### Memory (Distributed State)

| Feature | How |

|---|---|

| Key-value storage | `app.memory.set(key, value)` / `.get(key)` |

| Vector search (semantic) | `app.memory.search(embedding, top_k=5)` |

| Four scopes | Global, agent, session, run |

| Reactive memory events | `@app.memory.on_change("order_*")` |

| Metadata filtering | Filter stored values by metadata |

| Zero dependencies | Built into control plane - no Redis |

#### Human-in-the-Loop

| Feature | How |

|---|---|

| Durable pause/resume | `await app.pause(reason="...")` |

| Approval workflows with UI | `approval_request_url` for reviewers |

| Configurable timeouts | `expires_in_hours=24` + auto-escalation |

| Crash-safe state | Survives agent restarts |

#### Canary Deployments & Versioning

| Feature | How |

|---|---|

| Traffic weight routing | 5% → 50% → 100% rollouts |

| A/B testing | 50/50 splits with `X-Routed-Version` |

| Blue-green deployments | Instant weight switch, zero downtime |

| Per-version health tracking | Unhealthy versions auto-removed |

| Agent lifecycle states | pending → starting → ready → degraded → offline |

#### Identity & Governance

| Feature | How |

|---|---|

| Cryptographic identity per agent | Auto-generated W3C DID + Ed25519 keys |

| Verifiable Credentials | Tamper-proof receipt per execution |

| Offline VC verification | `af vc verify audit.json` |

| Tag-based access policies | ALLOW/DENY rules on caller → target tags |

| Cryptographically signed requests | Ed25519 signatures on cross-agent calls |

| VC hierarchy (3 tiers) | Platform → Node → Function control |

| Agent notes (audit log) | `app.note("Decision", tags=["critical"])` |

| Non-repudiation | Cryptographic proof of actions |

| Permission request workflows | Auto-created when access denied |

#### Observability & Fleet Management

| Feature | How |

|---|---|

| Automatic DAG visualization | Workflow graphs in dashboard |

| Prometheus metrics | `/metrics` out of the box |

| Structured JSON logging | Automatic from SDK |

| Execution timeline | Chronological decision trace |

| Health checks (K8s-ready) | `/health`, `/ready` endpoints |

| Correlation IDs | `X-Workflow-ID`, `X-Execution-ID` |

| Workflow DAG API | `GET /api/v1/workflows/{id}/dag` |

| Agent heartbeat monitoring | Auto health status transitions |

#### Harness (Multi-turn Coding Agents)

| Feature | How |

|---|---|

| 4 providers | Claude Code, Codex, Gemini CLI, OpenCode |

| Schema-constrained output | `schema=ResultModel` (Pydantic/Zod) |

| Cost capping | `max_budget_usd=3.0` |

| Turn limiting | `max_turns=100` |

| Tool access control | `tools=["Read", "Write", "Bash"]` |

| Environment injection | `env={"KEY": "value"}` |

| System prompt override | `system_prompt="..."` |

| Multi-layer output recovery | Cosmetic repair → retry → full retry |

#### Connector API (Fleet Management)

| Feature | How |

|---|---|

| Remote agent management | `/connector/reasoners` |

| Version traffic control | `/connector/.../weight` |

| Bearer token auth | `AGENTFIELD_CONNECTOR_TOKEN` |

| Air-gapped deployment | Outbound WebSocket only |

#### Developer Experience

| Feature | How |

|---|---|

| CLI scaffolding | `af init my-agent --defaults --language python\|go\|typescript` |

| Local dev with dashboard | `af server` → http://localhost:8080 |

| Hot reload | `af dev` auto-detects changes |

| Auto-REST from decorators | Every `@app.reasoner()` → `POST /api/v1/execute/...` |

| Python, Go, TypeScript SDKs | Native patterns per language |

| MCP server integration | `af add --mcp --url ` |

| Config storage API | `POST /api/v1/configs/:key` - database-backed |

| Docker + Kubernetes ready | Stateless control plane, horizontal scaling |

[Explore all features in detail →](https://agentfield.ai/docs/learn/features?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-explore-features)

## Built With AgentField

  

    

      

        

      

      


      Autonomous Engineering Team

      


      _{One API call spins up PM, architect, coders, QA, reviewers - hundreds of coordinated agents that plan, build, test, and ship.}

      



      View project →

    

    

      

        

      

      


      Deep Research Engine

      


      _{Recursive research backend. Spawns parallel agents, evaluates quality, generates deeper agents, and recurses -10,000+ agents per query.}

      



      View project →

    

  

  

    

      

        

      

      


      Reactive MongoDB Intelligence

      


      _{Atlas Triggers + agent reasoning. Documents arrive raw and leave enriched - risk scores, pattern detection, evidence chains.}

      



      View project →

    

    

      

        

      

      


      Autonomous Security Audit

      


      _{250 coordinated agents trace every vulnerability source-to-sink and adversarially verify each finding. Confirmed exploits, not pattern flags.}

      



      View project →

    

  

  

    

      

        

      

      


      CloudSecurity AF

      


      _{AI-native cloud infrastructure security scanner that performs shift-left attack path analysis directly from IaC, prioritizing the most dangerous risk chains before deployment.}

      



      View project →

    

    

      

        

      

      


      Agentic PR Reviewer

      


      _{Builds a custom review strategy for every PR - spawns parallel reviewer agents with runtime-crafted prompts, adversarially challenges its own findings, and posts evidence-grounded inline comments.}

      



      View project →

    

  

[See all examples →](https://www.agentfield.ai/examples?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-see-all-examples)

Built something with AgentField? [Submit your project to be featured on the examples page](https://github.com/Agent-Field/agentfield/issues/new?template=community-project.md).

## See It In Action








_{Real-time workflow DAGs · Execution traces · Agent fleet management · Audit trails}



## Architecture







The control plane is a stateless Go service. Agents connect from anywhere - your laptop, Docker, Kubernetes. They register capabilities, the control plane routes calls between them, tracks execution as DAGs, and enforces policies. [Full architecture docs →](https://agentfield.ai/docs/learn/architecture?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-architecture)

## Learn More

The thinking behind AgentField - essays on AI backends, harness orchestration, and the infrastructure production agents actually need.

  

    

      

        

      

      


      What is harness orchestration?

      


      _{The atomic unit of intelligence is climbing from the model call to the autonomous harness - and what changes when it does.}

      



      Read post →

    

    

      

        

      

      


      Part 1: The Black Box

      


      _{Treating harnesses like Claude Code and Codex as autonomous, embodied, persistent computational entities.}

      



      Read post →

    

  

  

    

      

        

      

      


      Part 2: Engineering the Membrane

      


      _{Shaping the boundary surface of a harness across four engineerable dimensions: workspace, drift, verifier placement, and recovery budget.}

      



      Read post →

    

    

      

        

      

      


      The AI Backend

      


      _{Our thesis: in five years every serious software company will run an AI backend - a reasoning layer that makes the decisions that used to be hardcoded.}

      



      Read post →

    

  

  

    

      

        

      

      


      IAM for AI Backends

      


      _{Agents need identity, not API keys - how decentralized identifiers and verifiable credentials make agent-to-agent delegation auditable and accountable.}

      



      Read post →

    

    

    

  

### Documentation

- **[vs Agent Frameworks](https://agentfield.ai/docs/learn/vs-frameworks?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-vs-frameworks)** - How AgentField compares to LangChain, CrewAI, and workflow engines

- **[Full Documentation](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-full-docs)**

## Community



[![Discord](https://img.shields.io/badge/Join%20Discord-d4a24a?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/aBHaXMkpqh)

[![Twitter](https://img.shields.io/badge/Follow%20on%20X-0c0b09?style=for-the-badge&logo=x&logoColor=white)](https://x.com/agentfield_ai)

**[GitHub Issues](https://github.com/Agent-Field/agentfield/issues)** · **[Documentation](https://agentfield.ai/docs/learn?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-community-docs)** · **[Examples](https://agentfield.ai/docs/learn/examples?utm_source=github-readme&utm_campaign=github-readme&utm_id=github-readme-community-examples)**



## License

[Apache 2.0](LICENSE)
ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/agent-field/agentfield

Awesome Lists containing this project

README

▼ Click to expand full capabilities
