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.

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
• 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
├── 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)

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).

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_<version>_<os>_<arch>.tar.gz.sbom.json — cataloged by syft from the contents of that specific archive.
• pi-go_<version>_sbom.spdx.json — the aggregate SBOM cataloged from the source tree, and the one the SBOM attestation binds to. It covers the Go module graph, which is the same across every platform in the build matrix, plus the pinned GitHub Actions the release itself was built with.

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

Requirements

• Go 1.25+
• 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.

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="..."   # only for Ollama Cloud (:cloud suffix)

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.

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 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 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)

Slash commands

Memory Palace

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

Layers:

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.

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.

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

Environment variable substitution: Pi automatically expands ${ENV_VAR} patterns in server URLs using .pi-go/.env

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.

License

See LICENSE for details.