pi-go

A terminal-based coding agent built on Google ADK Go. It connects to multiple LLM providers, runs sandboxed tools, integrates LSP, and ships with a process-based subagent system.

!pi-go TUI

Features

• Multi-provider LLM — Claude (Anthropic), GPT/O-series (OpenAI), Gemini (Google), Mistral, Grok (xAI), Azure OpenAI, OpenRouter, OpenCode, and Ollama (local or cloud) for models
• Sandboxed tools — read, write, edit, shell, grep, find, tree, and git operations. All tools are restricted to the project directory via os.Root.
• Interactive TUI — Bubble Tea v2 with Markdown rendering (Glamour), slash commands, and theming
• Session persistence — JSONL append-only event logs with branching, compaction, and resume
• Model roles — Named configurations (default, smol, slow, plan, commit) selectable via CLI flags
• Subagents — Process-based multi-agent system with types: explore, plan, designer, reviewer, task, quick_task
• LSP — JSON-RPC client for Go, TypeScript/JS, Python, and Rust, with auto-format and diagnostics hooks
• AI Git tools — Repository overview, file diffs, hunk parsing, and LLM-generated conventional commits (/commit)
• RPC server — Unix socket JSON-RPC 2.0 for IDE/editor integration
• Memory Palace — 4-layer contextual memory with SQLite storage, semantic embeddings (all-MiniLM-L6-v2), temporal knowledge graph, and project/conversation miners
• Extensions — Hooks (shell callbacks), skills (.SKILL.md instructions), and Model Context Protocol (MCP) servers
• Plugin marketplaces — Install skill bundles from any Claude Code-compatible plugin marketplace, with version tracking and updates (pi plugin)
• Skills audit — Security scanning for hidden Unicode characters, BiDi attacks, and supply-chain threats in skill files (pi audit)

Architecture

cmd/pi/             Entry point — CLI parsing, output mode selection
internal/
├── agent/          ADK agent setup, retry logic, runner
├── cli/            Cobra CLI flags, output modes (interactive, print, json, rpc)
├── config/         Global and project config (roles, hooks, MCP, themes)
├── audit/          Security scanner for skills (hidden Unicode, supply-chain threats)
├── extension/      Hooks, skills, MCP server integration
├── plugin/         Plugin marketplaces — manifests, registry, install/update
├── lsp/            LSP JSON-RPC client, language registry, manager, hooks
├── palace/         Memory Palace — drawers, layers, KG, miners, embedder, search
├── provider/       LLM providers implementing genai model interface
├── rpc/            Unix socket JSON-RPC 2.0 server
├── session/        JSONL persistence, branching, compaction
├── subagent/       Process spawner, orchestrator, concurrency pool
├── tools/          Sandboxed tools (read, write, edit, bash, grep, find, git, lsp)
└── tui/            Bubble Tea v2 UI, slash commands, commit workflow

Request flow

User input → CLI → Agent → LLM provider → Tool calls → Sandbox → Response → TUI
                     ↕           ↕            ↕
              Session store   Palace       LSP servers
              (JSONL events)  (memory,   (format, diagnostics)
                              KG, search)

See ARCHITECTURE.md for detailed documentation.

Installation

Quick install (recommended)

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/dimetron/pi-go/main/scripts/install.sh | bash

This script detects your OS/arch, downloads the latest release binary, and installs it to /usr/local/bin (or ~/.local/bin if needed).

Windows

powershell -NoProfile -Command "iwr https://raw.githubusercontent.com/dimetron/pi-go/main/scripts/install.ps1 -UseBasicParsing | iex"

Or, from a checkout:

powershell -NoProfile -ExecutionPolicy Bypass -File scripts/install.ps1

Installs pi.exe to %LOCALAPPDATA%\Programs and adds that directory to your user PATH. Restart the terminal afterwards so the new PATH is picked up. Runs on Windows PowerShell 5.1 (built into Windows 10/11) and PowerShell 7+; windows/amd64 only. Set GITHUB_TOKEN if you hit the GitHub API rate limit while it resolves the latest release.

The installer checks the download against the release's checksums.txt and refuses to install on a mismatch. That catches a corrupted or swapped archive, but not a substituted release — checksums.txt travels the same path as the archive. Run pi verify afterwards for the provenance check that does answer that question — see Verifying a release.

Windows machines without bash.exe on PATH — a stock Windows install has none — run agent commands through powershell.exe instead, so write PowerShell syntax in prompts: ; or a newline rather than &&. Installing Git for Windows puts a bash on PATH and restores the bash behaviour.

NixOS / Nix

The repository includes a flake that builds pi-go reproducibly and exposes a NixOS module. To install it in a NixOS configuration, add the repository as an input:

# flake.nix
{
  inputs.pi-go.url = "github:dimetron/pi-go";
outputs = { self, nixpkgs, pi-go, ... }: {
    nixosConfigurations.my-host = nixpkgs.lib.nixosSystem {
      system = "x86_64-linux";
      modules = [
        ./configuration.nix
        pi-go.nixosModules.default
      ];
    };
  };
}

Enable it in configuration.nix:

{ programs.pi-go.enable = true; }

Then rebuild with sudo nixos-rebuild switch --flake .. For a one-off use, run nix run github:dimetron/pi-go or install it into a profile with nix profile install github:dimetron/pi-go.

go install

go install github.com/dimetron/pi-go/cmd/pi@latest

Make sure your GOPATH/bin is in your PATH. The binary will be installed as pi.

Build from source

git clone https://github.com/dimetron/pi-go.git
cd pi-go
go install ./cmd/pi

Pre-built binaries

Download the latest release for your platform from the Releases page.

Verifying a release

pi verify checks the running binary against the attestations published for it, with no other tooling required:

pi verify                                     # the running binary
pi verify ./pi                                # a specific file
pi verify pi-go_1.2.3_linux_amd64.tar.gz      # a downloaded archive, before extracting
pi verify --json                              # machine-readable
pi verify --sbom > sbom.spdx.json             # print the attested SBOM document

/usr/local/bin/pi
  sha256:abd70659b49183320320426af4abf34555b031e432aff27afbdbf1be39e1ecff
✓ build provenance
      repository  github.com/dimetron/pi-go
      workflow    .github/workflows/release.yml@refs/tags/v1.2.3
      commit      4086645aa1f2c3d4e5f60718293a4b5c6d7e8f90
      run         https://github.com/dimetron/pi-go/actions/runs/1234/attempts/1
      signer      https://github.com/dimetron/pi-go/.github/workflows/release.yml@refs/tags/v1.2.3
      signed      2026-08-21T12:00:00Z
✓ SBOM
      format      SPDX 2.3
      packages    192
      ecosystems  golang 180, github 12
      signer      https://github.com/dimetron/pi-go/.github/workflows/release.yml@refs/tags/v1.2.3
      signed      2026-08-21T12:00:00Z

The check starts from the file's SHA-256 and nothing else — the version compiled into the binary, the name it was installed under and the URL it came from are all attacker-controlled. That digest is looked up in GitHub's attestations API and the returned Sigstore bundles are verified against the public-good Sigstore trust root: certificate chain, Rekor transparency-log inclusion, signed certificate timestamp, and a certificate identity that must name **this repository's release workflow, running on a tag**. A signature from any other workflow, branch or repository is rejected.

A binary you built yourself has no attestation and reports as unverified. That is the expected answer, not a failure.

Verification needs network access. The Sigstore trust root is cached in ~/.pi-go/sigstore after the first run.

Verifying with the GitHub CLI

The same attestations are readable by gh, if you would rather not trust the binary to vouch for itself:

gh attestation verify ./pi --repo dimetron/pi-go
SBOM attestation. The predicate type carries the SPDX version syft emitted.
gh attestation verify ./pi --repo dimetron/pi-go \
  --predicate-type https://spdx.dev/Document/v2.3

What is attested, and what is published

Both the release archives and the raw binaries inside them are attestation subjects. scripts/install.sh extracts the binary and puts it on your PATH, so the archive digest is not the digest you end up running; attesting only the archive would leave the installed binary unverifiable. The binaries themselves are not published as release assets — the digest is all verification needs.

Each release publishes SBOMs as assets, in SPDX JSON:

• pi-go___.tar.gz.sbom.json — cataloged by syft from the

• pi-go__sbom.spdx.json — the aggregate SBOM cataloged from the

Regenerate the aggregate SBOM locally with make sbom (requires syft).

Requirements

• Go 1.27+
• At least one LLM provider API key or a running Ollama instance

API keys

Set the API key for your provider as an environment variable. The provider is inferred from the model name, so --model is usually the only routing you need.

| Provider | Model prefix | API key env var | Base URL env var | |---|---|---|---| | Anthropic | claude-* | ANTHROPIC_API_KEY (or ANTHROPIC_AUTH_TOKEN) | ANTHROPIC_BASE_URL | | OpenAI | gpt-* | OPENAI_API_KEY | OPENAI_BASE_URL | | Google Gemini | gemini-* | GEMINI_API_KEY (or GOOGLE_API_KEY) | GEMINI_BASE_URL | | Mistral | mistral-, magistral- | MISTRAL_API_KEY | MISTRAL_BASE_URL | | xAI (Grok) | grok-* | XAI_API_KEY | XAI_BASE_URL | | OpenRouter | openrouter/ | OPENROUTER_API_KEY | OPENROUTER_BASE_URL | | agentgateway | agentgateway/ | none (optional AGENTGATEWAY_API_KEY) | AGENTGATEWAY_BASE_URL (default http://localhost:4000) | | Azure OpenAI | azure/ | AZUREOPENAI_API_KEY | — | | OpenCode | opencode/ | OPENCODE_API_KEY | OPENCODE_BASE_URL | | Ollama (local) | ollama/ | none | OLLAMA_HOST (default http://localhost:11434) | | Ollama Cloud | :cloud | OLLAMA_API_KEY | https://api.ollama.com, or the local daemon when no key is set |

export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."
export GEMINI_API_KEY="..."
export MISTRAL_API_KEY="..."     # optional — only if you use Mistral models
export XAI_API_KEY="..."         # optional — only if you use Grok models
export OPENROUTER_API_KEY="..."  # optional — only if you use OpenRouter models
export OPENCODE_API_KEY="..."
export OLLAMA_API_KEY="..."   # optional — only to reach Ollama Cloud directly

A name with no recognized prefix is rejected rather than guessed at — reach for the ollama/ prefix or the :cloud suffix to name an Ollama model explicitly.

A :cloud tag names a model, not a destination. With OLLAMA_API_KEY set the request goes straight to api.ollama.com; without one it goes to the local daemon, which has served cloud models on your ollama signin identity since Ollama 0.12 — so pi --model deepseek-v3.1:671b-cloud works with no key at all. The ollama/ prefix always means the local daemon, tag notwithstanding, and an explicit OLLAMA_HOST overrides both.

Build

make build      # build the pi binary
make test       # run unit tests
make lint       # golangci-lint (vet, staticcheck, errcheck, …)
make e2e        # run E2E integration tests
make clean      # remove binary

Usage

# Default interactive mode
pi
Select a model by prefix
pi --model claude:sonnet
pi --model openai:gpt-4o
pi --model gemini:gemini-2.5-pro
pi --model mistral-large-latest
pi --model grok-4.6
pi --model azure/my-gpt5-deployment
pi --model openrouter/google/gemini-3.7-flash
pi --model ollama/gemma4:12b-mlx
pi --model opencode/kimi-k3
pi --model agentgateway/deepseek-v4-flash:0731-cloud
pi --model minimax-m3:cloud # automatically detect ollama if :cloud
Use model roles
pi --smol          # fast, cheap model
pi --slow          # most capable model
pi --plan          # planning-oriented model
Additional options
pi --continue      # continue last session
pi --session <id>  # resume specific session
pi --system "..." # custom system instructions
pi --url "..."    # custom API endpoint URL
Non-interactive modes
pi --mode print "explain this codebase"
pi --mode json "list all TODO comments"
pi --mode json --json-deltas full "..."      # one event per model chunk, not per sentence
pi --mode socket --socket /tmp/pi-go.sock  # JSON-RPC 2.0 over a Unix socket
pi --mode rpc                              # pi-compatible NDJSON over stdio (for pi-acp)

JSON mode

--mode json writes one JSON object per line to stdout. Streamed assistant text is grouped by sentence, so one text_delta carries a whole sentence rather than a single SSE chunk — a three-sentence reply is 5 lines instead of 76, and the delta fields still concatenate to exactly the reply. Concatenate delta to reconstruct the text; that is how every consumer uses it.

{"type":"message_start","agent":"pi","role":"model","session_id":"..."}
{"type":"thinking_delta","agent":"pi","delta":"..."}
{"type":"text_delta","agent":"pi","delta":"Go is a compiled language. "}
{"type":"tool_call","agent":"pi","tool_name":"bash","tool_input":{...}}
{"type":"tool_result","agent":"pi","tool_name":"bash","content":"{...}"}
{"type":"message_end"}

Slash commands

| Command | Description | |-----------------|----------------------------------------------------------| | /help | Show available commands | | /model | Switch model mid-conversation | | /session | List and switch sessions | | /branch | Create a conversation branch | | /commit | Generate and apply a git commit | | /compact | Compact session history | | /agents | Show running subagents | | /history | Show command history | | /plan | Start a Plan-Driven Development (PDD) session (auto-resumes if a spec exists) | | /run | Execute a spec with task agent | | /skill-create | Create a new skill | | /skill-list | List available skills | | /skill-load | Reload skills from disk | | /memory | Memory Palace commands (see below) | | /audit | Scan skills for hidden Unicode threats | | /clear | Clear conversation | | /exit | Exit the agent |

Plugin marketplaces

Install skill bundles from a plugin marketplace. pi-go reads the same .claude-plugin/marketplace.json manifest other coding agents use, so existing marketplaces work unchanged:

# Register the Superpowers marketplace
pi plugin marketplace add obra/superpowers-marketplace
Install a plugin from it
pi plugin install superpowers@superpowers-marketplace
See what is installed
pi plugin list

| Command | Description | |--------------------------------------------------|---------------------------------------------------------| | pi plugin marketplace add | Register a marketplace (GitHub owner/repo, git URL, or local directory) | | pi plugin marketplace list | List registered marketplaces | | pi plugin install | Install a plugin and make its skills available | | pi plugin list | List installed plugins, versions, and commits | | pi plugin update [plugin] | Update one plugin, or all of them | | pi plugin uninstall | Remove a plugin and its files |

Installed plugins live in ~/.pi-go/plugins/, and their skills are discovered automatically. Plugin skills have lower precedence than your own: a skill in ~/.pi-go/skills or .pi-go/skills with the same name always wins, so installing a plugin can never silently replace a skill you wrote or customized.

Sources may be a GitHub shorthand (owner/repo), a full git URL, or a local directory. Plugin names come from the marketplace manifest, which is untrusted input, so they are validated before being used as directory names.

Memory Palace

A 4-layer contextual memory system that gives the agent persistent awareness across sessions.

Layers:

| Layer | Name | Description | |-------|------|-------------| | L0 | Identity | Static identity file | | L1 | Essential Story | Top-15 drawers by importance, injected into system prompt | | L2 | On-Demand Recall | Context-filtered drawer chunks | | L3 | Search | Semantic (embedding) or keyword (FTS5) search |

CLI commands:

# Setup
pi memory model download         # download all-MiniLM-L6-v2 embedding model
pi memory model status           # check model path and status
pi memory init [dir]             # create palace.db + generate mempalace.yaml
Ingest
pi memory mine <dir>             # mine source files into drawers
pi memory mine --convos <dir>    # mine conversation files (JSONL/text)
Query
pi memory status                 # palace overview (drawers, wings, rooms, KG)
pi memory search <query>         # semantic or keyword search
pi memory wake-up                # print L0+L1 context for system prompt
pi memory recent [project]       # recent memory observations
Knowledge Graph
pi memory kg query <entity>      # query triples involving an entity
pi memory kg add <s> <p> <o>     # add a fact triple
pi memory kg timeline <entity>   # chronological timeline of facts

Configuration via mempalace.yaml in the project root:

wing: my-project
rooms:
  - name: auth
    patterns: ["internal/auth/**"]
    keywords: [jwt, token, session]
  - name: api
    patterns: ["internal/api/**"]
    keywords: [handler, endpoint, route]

When the Palace is enabled, the agent also gains tool access: palace-search, palace-add-drawer, palace-kg-query, palace-kg-add, palace-diary-write, palace-traverse, and more.

Security audit

# Scan all skill files for hidden Unicode characters
pi audit
Scan with verbose output (include info-level findings)
pi audit -v
Output as JSON for CI pipelines
pi audit --format json --output report.json
Auto-remove dangerous characters (creates .bak backups)
pi audit --strip
Preview what would be removed
pi audit --strip --dry-run
Scan a specific file
pi audit --file path/to/SKILL.md

Skills are automatically scanned on load — skills with critical findings (Unicode tags, BiDi overrides, variation selector attacks) are blocked from loading.

Configuration

Pi reads configuration from ~/.pi-go/config.json (global) and .pi-go/config.json (project-local):

• Model roles — Map role names to specific model strings
• Hooks — Shell commands triggered on tool events (e.g., post-write formatting)
• MCP servers — External tool servers via Model Context Protocol
• Themes — Terminal color schemes via theme config field
• Base URLs — Per-provider endpoints via the baseURLs field

Provider base URLs

Self-hosted or LAN endpoints can be declared in config instead of exported in every shell:

{
  "roles": {
    "default": { "model": "ollama/gemma-4-e4b:latest", "provider": "ollama" }
  },
  "baseURLs": {
    "ollama": "http://192.168.1.10:11434"
  }
}

Precedence is --url flag, then environment variable, then baseURLs config. The matching env vars are ANTHROPIC_BASE_URL, OPENAI_BASE_URL, GEMINI_BASE_URL, MISTRAL_BASE_URL, XAI_BASE_URL, OPENROUTER_BASE_URL, OPENCODE_BASE_URL, and OLLAMA_HOST. A per-shell or CI override still takes effect. An empty env var does not mask a configured value.

Ollama generation tuning

Ollama's per-request options are left at the server's own defaults, except for an output cap. Each knob below is opt-in: unset means the option is not sent at all, so Ollama's default stays in force. An unparseable value is ignored rather than fatal — a typo in an env var should not take down an otherwise healthy session.

| Env var | Ollama option | Ollama default | Purpose | |---|---|---|---| | PI_OLLAMA_NUM_PREDICT | num_predict | unlimited | Max tokens generated per turn. Pi defaults this to 16384; 0 or less removes the cap. | | PI_OLLAMA_REPEAT_PENALTY | repeat_penalty | 1.1 | How strongly repeated tokens are penalised. 1.0 disables. | | PI_OLLAMA_REPEAT_LAST_N | repeat_last_n | 64 | How many recent tokens the penalty looks back over. 0 disables, -1 uses the full context. | | PI_OLLAMA_PRESENCE_PENALTY | presence_penalty | 0.0 | Flat penalty for tokens already used. | | PI_OLLAMA_FREQUENCY_PENALTY | frequency_penalty | 0.0 | Penalty scaled by how often a token was used. |

These matter for models prone to repetition collapse, where a turn stops making progress and restates the same phrase until it hits a limit. num_predict only bounds how far such a turn runs; it does not stop it degenerating. The penalty window is the knob that targets the cause, and the default window is narrow: Ollama penalises repeats across the last 64 tokens only, while observed degenerate turns cycle on phrases of roughly 25–55 tokens, so a full cycle can fall outside the window the penalty can see.

# Widen the repetition window and penalise repeats harder.
export PI_OLLAMA_REPEAT_LAST_N=512
export PI_OLLAMA_REPEAT_PENALTY=1.2

Both apply to local Ollama and Ollama Cloud — they share one request path. Raising these trades diversity for repetition control, and a value that helps one model can degrade another, so tune per model rather than setting them globally.

Web search

The web_search tool lets the agent look up things that are not in the repository — a library's current release, a recent API change, an error message it has not seen before.

It uses one of two Ollama endpoints, tried in order:

1. A local daemon (OLLAMA_HOST, default http://localhost:11434), on

1. https://ollama.com/api/web_search, when OLLAMA_API_KEY is set. This is

With neither available, the tool returns that as an ordinary result naming what to set, rather than failing the turn.

The two endpoints are different paths, not the same path on two hosts, which is why pi-go calls them directly instead of through the Ollama Go SDK: the SDK's WebSearchExperimental posts to /api/experimental/web_search, which 404s on api.ollama.com.

Each result keeps its URL and is capped at 4000 bytes of content, so a large page — a GitHub repository page comes back as the whole rendered README — spends a bounded part of the context window, and the model can still fetch the full page with bash when a snippet is not enough.

# Local daemon (no key needed)
pi "what changed in the latest Ollama release?"
Headless / CI, using the cloud search API
export OLLAMA_API_KEY="..."
pi "what changed in the latest Ollama release?"

Custom OpenAI-compatible provider

For OpenAI-compatible APIs with model names that Pi cannot infer from a prefix, explicitly set the role provider to openai and point OPENAI_BASE_URL at the custom endpoint:

export OPENAI_API_KEY="your-api-key"
export OPENAI_BASE_URL="https://api.example.com/v1"

{
  "roles": {
    "default": {
      "model": "Qwen3.5-397B-A17B-FP8",
      "provider": "openai"
    }
  }
}

Then run Pi normally:

pi

You can also pass the endpoint per invocation:

OPENAI_API_KEY="your-api-key" pi --model Qwen3.5-397B-A17B-FP8 --url https://api.example.com/v1

When --url or OPENAI_BASE_URL is set, unknown model names are treated as custom OpenAI-compatible models. Setting provider: "openai" in config avoids relying on model-prefix detection.

MCP server integration

Pi supports the Model Context Protocol. Use it to extend the agent with external tools. Configure servers in ~/.pi-go/config.json:

{
  "mcp": {
    "servers": [
      {
        "name": "tavily-search",
        "url": "https://mcp.tavily.com/mcp/?tavilyApiKey=${TAVILY_API_KEY}"
      },
      {
        "name": "filesystem",
        "command": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-filesystem",
          "/tmp"
        ]
      }
    ]
  }
}

Or in standalone ~/.pi-go/mcp.json (Claude Desktop compatible format):

{
  "mcpServers": {
    "tavily-search": {
      "url": "https://mcp.tavily.com/mcp/?tavilyApiKey=${TAVILY_API_KEY}"
    },
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/tmp"
      ]
    }
  }
}

Supported transports:

• HTTP/Streamable — url field for cloud-based MCP servers
• Stdio — command + args for local subprocess servers

Editor integration

Pi can run as an Agent Client Protocol (ACP) server. Use it from any IDE that supports ACP.

Zed

Add pi to Zed's agent_servers in your settings:

{
  "agent_servers": {
    "pi": {
      "type": "custom",
      "command": "pi",
      "args": ["acp-server", "--model", "glm-5.2:cloud"],
      "env": {}
    }
  }
}

Then invoke via Zed's agent panel (⌘⇧A / Ctrl+Shift+A) and select "pi". The agent runs in the current Zed project directory with full access to pi's tools and memory.

JetBrains IDEs

JetBrains IDEs (IntelliJ IDEA, GoLand, PyCharm, WebStorm, …) discover ACP agents from ~/.jetbrains/acp.json. Add pi under agent_servers:

{
  "agent_servers": {
    "Pi-Go": {
      "command": "pi",
      "args": ["acp-server", "--model", "agentgateway/ollama/glm-5.3-flash:cloud"]
    }
  }
}

Restart the IDE so it picks up the file, then open the AI Assistant / agent panel and select "Pi-Go". The agent runs in the current project directory. pi acp-server accepts --model plus --url, --header key=value (repeatable) and --insecure; with no --model it falls back to glm-5.2:cloud.

VS Code

The vscode/ directory contains a VS Code extension that drives pi-go over ACP, surfacing it as a native agent in VS Code's Chat/Agent Sessions UI. See vscode/README.md for installation and usage.

Sessions survive the server

Every ACP session's transcript is written to the same store the terminal uses (~/.pi-go/sessions//, or $PI_SESSIONS_DIR), keyed by the ACP session id. The server implements the protocol's session lifecycle on top of it:

| Method | What pi does | |---|---| | session/load | Replays the stored transcript to the client, then continues it | | session/resume | Continues the transcript without replaying it | | session/list | Lists stored sessions, newest first, optionally filtered by cwd |

So an editor can restart pi — or the machine — and pick a thread up where it left off, and pi --session reopens the same conversation from the terminal.

kagent

Run pi-go as a custom agent inside kagent on Agent Substrate via the A2A adapter image. See docs/kagent-harness.md for the deployment guide and specs/kagent/ for the Dockerfile, manifests, and step-by-step deploy notes.

License

See LICENSE for details.