agent-block

Single-purpose agent building block with built-in mesh communication.

What is agent-block?

A headless agent runtime. Each agent runs as a single process, executes its task, then exits. No rich interactive TUI, no sub-agent orchestration — orchestration belongs to the caller (shell, A2A, CI, etc.).

agent-block handles the infrastructure that individual agents shouldn't have to — mesh connectivity (A2A), MCP server management, LLM API access — so that Lua code focuses purely on domain logic.

Think of it like Envoy for agents: the process itself is simple, but the communication layer is fully capable.

Design Decisions

• Single run — One process, one task, one exit. Orchestration belongs to the caller (shell, A2A, CI, etc.), not inside the agent
• Headless — No terminal UI. Agents are composed via A2A/mesh protocols, not interactive prompts
• Runtime owns the protocol — Mesh, MCP, and HTTP are provided by the runtime. Lua code never deals with connection management or wire formats
• Lua for logic, Rust for plumbing — Domain logic in Lua. VM, networking, and protocol handling in Rust

Architecture

┌─────────────────────────────────────────────┐
│              agent-block (binary)            │
│                                             │
│  ┌─────────┐  ┌──────────┐  ┌───────────┐  │
│  │ mlua-isle│  │ mesh-sdk │  │ llm-client│  │
│  │ (Lua VM) │  │ (relay)  │  │ (API)     │  │
│  └────┬─────┘  └────┬─────┘  └─────┬─────┘  │
│       │             │              │         │
│  ─────┴─────────────┴──────────────┴─────── │
│              Lua Stdlib Bridge               │
│  mesh.send / mesh.on / llm.chat / fs.read   │
│  tool.register / tool.call / log.* / env.*  │
│  mcp.connect / mcp.call / mcp.list_tools    │
└─────────────────────────────────────────────┘
         ↕ WebSocket              ↕ stdio
┌─────────────────┐    ┌──────────────────┐
│   agent-mesh     │    │  MCP Servers     │
│   relay          │    │  (outline-mcp)   │
└─────────────────┘    └──────────────────┘

Usage

# Basic
agent-block --script scripts/hello.lua

# With project context
agent-block --script scripts/test_fcloop.lua --project .

# With mesh
ANTHROPIC_API_KEY=... agent-block --script my_agent.lua --relay ws://localhost:9090/ws

# Pass a prompt and system context from the CLI
agent-block --script my_agent.lua \
    --prompt "Summarise the README" \
    -c "You are a concise technical writer."

CLI flags --prompt and -c / --context inject the _PROMPT and _CONTEXT Lua globals into the script. Use them with agent.run:

-- my_agent.lua
local agent = require("agent")
local result = agent.run({
    prompt = _PROMPT,    -- nil when --prompt is omitted (agent.run will error — expected)
    system = _CONTEXT,   -- nil when -c is omitted (system prompt is optional)
})
print(result.content)

Both flags also accept environment variables as fallback:

Flag	Env var
--prompt	AGENT_BLOCK_PROMPT
-c / --context	AGENT_BLOCK_CONTEXT

MCP Echo Harness

A self-contained reference MCP server for smoke-testing the agent-block MCP client bridge. Exposes tools, resources, prompts, logging, and sampling over stdio or HTTP.

# stdio (default) — connect via mcp.connect("echo", "target/debug/examples/echo_mcp_server", {})
cargo run --example echo_mcp_server

# HTTP on an ephemeral port — prints ECHO_MCP_URL=http://127.0.0.1:<port>/mcp
cargo run --example echo_mcp_server -- --transport http --port 0

# Also emit 5 log notifications (1-second intervals) and attempt a sampling round-trip
cargo run --example echo_mcp_server -- --transport http --port 0 --emit-logs --request-sampling

Verify from Lua (requires the server to be running with --transport http):

local url = os.getenv("ECHO_MCP_URL")
mcp.connect_http("echo", url)
print(mcp.list_tools("echo"))         -- 2 tools: echo, slow_echo
print(mcp.list_resources("echo"))     -- 2 resources: text://hello, text://note
print(mcp.list_prompts("echo"))       -- 1 prompt: greet
-- call slow_echo to exercise progress notifications
mcp.on_progress("echo", function(tok, prog, total, msg)
    print("progress", prog, total, msg)
end)
print(mcp.call("echo", "slow_echo", { msg = "hi", steps = 3 }))

See examples/verify_echo_harness.lua for the full verification script.

MCP Resource Subscribe Smoke Server

A standalone binary example for shell-level smoke-testing the Resource Subscribe API (mcp.subscribe_resource / mcp.on_resource_update). Starts an HTTP MCP server with resources.subscribe capability enabled and fires at least one notify_resource_updated event after each subscribe call.

# Ephemeral port — prints SUBSCRIBE_TEST_SERVER_URL=http://127.0.0.1:<port>/mcp
cargo run --example subscribe_test_server

# Fixed port
cargo run --example subscribe_test_server -- --port 7878

# Periodic notify every 500 ms (instead of single fire on subscribe)
cargo run --example subscribe_test_server -- --port 0 --interval 500

Shell smoke (requires the server URL printed above):

export MCP_HTTP_URL="$(cargo run --example subscribe_test_server 2>/dev/null \
    | grep SUBSCRIBE_TEST_SERVER_URL | cut -d= -f2-)"
agent-block -s tests/fixtures/mcp_on_resource_update_callback.lua
# Expect: SUBSCRIBE_OK, RESOURCE_UPDATE_EV_OK, UPDATE_HITS=1, FIXTURE_DONE

See docs/runbooks/e2e-mcp-resource-subscribe.md for the full positive/negative verification procedure (Step 2 = shell positive, Step 3 = negative against a server without subscribe capability).

Lua API

llm.*

• llm.chat(messages, opts) — LLM call (Anthropic Messages API)

tool.*

• tool.register(name, schema, handler [, meta]) — Register a tool. Optional meta = { group = "..." } assigns the tool to a named group for use with agent.run({ tool_groups = {...} }).
• tool.call(name, input) — Call a registered tool
• tool.list() — List registered tool names
• tool.schema() — Anthropic tools-format schema array (includes group field when set)

mcp.*

Support status, capability matrix, and the tool-grouping design rationale live in docs/architecture/mcp-support.md.

• mcp.connect(name, command, args) — Spawn MCP server over stdio + initialize handshake
• mcp.connect_http(name, url, opts) — Connect to an MCP server over HTTP transport. opts.transport = "sse" | "http" (default "http" = Streamable HTTP; "sse" = SSE). opts.headers table is forwarded as request headers.
• mcp.call(name, tool_name, arguments) — Call an MCP tool
• mcp.list_tools(name) — List available tools
• mcp.list_resources(name) — List resources exposed by the server. Returns { ok=true, resources=[{uri, name, description, mimeType, ...}] }.
• mcp.list_resource_templates(name) — List resource URI templates exposed by the server. Returns { ok=true, resource_templates=[{uriTemplate, name, ...}] }.
• mcp.read_resource(name, uri) — Read a resource by URI. Returns { ok=true, contents=[{uri, mimeType, text|blob}] }.
• mcp.list_prompts(name) — List prompt templates exposed by the server. Returns { ok=true, prompts=[{name, description, arguments}] }.
• mcp.get_prompt(name, prompt_name, args) — Retrieve a rendered prompt template. Returns { ok=true, description, messages=[{role, content}] }.
• mcp.complete(name, ref, arg_name, arg_value) — Request completion suggestions (MCP Completion typeahead, Phase 3). ref is {type="ref/prompt", name=...} or {type="ref/resource", uri=...}. Returns { ok=true, values=[...], total=number?, has_more=bool? } or { ok=false, error=str }.
• mcp.on_progress(name, handler) — Register a per-server progress notification callback. handler(token, progress, total, message) is called for each notifications/progress event from the named server. Handler must be a pure Lua function.
• mcp.on_log(name, handler) — Register a per-server log notification callback. handler(level, logger, data) is called for each notifications/message event from the named server. When no handler is registered the notification is forwarded to the Rust tracing target "lua" at the corresponding level (debug/info/notice/warning/ error/critical/alert/emergency). Handler must be a pure Lua function.
• mcp.cancel(name, request_id) — Send a notifications/cancelled notification to the named server for the given request_id. Also fired automatically when mcp.call times out. Explicit use is only needed for manual cancellation flows.
• mcp.set_sampling_handler(server_name, handler) — Register a per-server Lua function to respond to sampling/createMessage requests from the MCP server. handler(params) receives the CreateMessageRequest table and must return a table matching CreateMessageResult ({ model, stop_reason, role, content }). When no handler is registered the server receives method_not_found.
• mcp.set_elicitation_handler(server_name, fn) — Register a per-server Lua function to respond to elicitation/create requests originating from the MCP server (server→client, Form variant only). fn(server_name, message, schema_json) must return a table with action = "accept"|"decline"|"cancel" and (for accept) a content table conforming to the schema. Url-variant elicitation requests are always declined without reaching the callback. Handler must be a pure Lua function.
• mcp.set_roots_handler(server_name, fn) — Register a per-server Lua function to respond to roots/list requests originating from the MCP server (server→client direction). fn(server_name) must return a Lua array of root tables, each with at least a uri field and an optional name field (e.g. { { uri="file:///home/user", name="home" } }). When no handler is registered the server receives method_not_found. Handler must be a pure Lua function; C functions and Rust-bound callbacks are not supported.
• mcp.notify_roots_list_changed(name) — Send a notifications/roots/list_changed notification to the named server (client→server, fire-and-forget). Use this whenever the client's set of filesystem roots changes so the server can re-request the updated list via roots/list. Failures are logged at warn level and silently discarded.
• mcp.server_info(name) — Return the server's InitializeResult as a Lua table. Returns { ok=true, server_info={serverInfo, capabilities, ...} } on success. Useful for inspecting which MCP capability groups (resources, prompts, tools, etc.) a server declares. Returns { ok=false, error="..." } if the server is not connected.
• mcp.ping(name) — Send a ping keepalive request to the named server and measure round-trip latency. Returns { ok=true, latency_ms=N } on success or { ok=false, error="..." } on failure (unknown server, timeout, or RPC error).
• mcp.subscribe_resource(server, uri) — Send a resources/subscribe RPC for the given resource URI. Returns { ok=true } on success or { ok=false, error="..." } on failure. Requires the server to declare the resources.subscribe capability.
• mcp.unsubscribe_resource(server, uri) — Send a resources/unsubscribe RPC to stop receiving change notifications for the given URI. Same return shape as subscribe_resource.
• mcp.on_resource_update(server, callback) — Register a per-server callback for notifications/resources/updated events. callback(ev) where ev = { type="resource_update", server, uri }. Handler must be a pure Lua function.
• mcp.on_resources_list_changed(server, callback) — Register a per-server callback for notifications/resources/list_changed events. callback(ev) where ev = { type="resources_list_changed", server }.
• mcp.on_tools_list_changed(server, callback) — Register a per-server callback for notifications/tools/list_changed events. callback(ev) where ev = { type="tools_list_changed", server }.
• mcp.on_prompts_list_changed(server, callback) — Register a per-server callback for notifications/prompts/list_changed events. callback(ev) where ev = { type="prompts_list_changed", server }.
• mcp.disconnect(name) — Disconnect server

mesh.*

• mesh.send(agent_id, payload) — Synchronous send (raises Lua error on failure)
• mesh.request(agent_id, payload) — Request-response
• mesh.agent_id() — Own AgentId

std.fs.* (mlua-batteries)

• std.fs.read(path), std.fs.write(path, content), std.fs.glob(pattern), std.fs.exists(path)
• std.fs.walk(dir), std.fs.copy(src, dst), std.fs.mkdir(path), std.fs.remove(path)
• std.fs.is_file(path), std.fs.is_dir(path), std.fs.read_binary(path), std.fs.write_binary(path, bytes)

sh.*

• sh.exec(cmd, opts) — Execute a shell command

std.json.* (mlua-batteries)

• std.json.encode(value), std.json.decode(str), std.json.encode_pretty(value)

std.env.* (mlua-batteries + agent-block extensions)

• std.env.get(key), std.env.set(key, value), std.env.get_or(key, default), std.env.home()
• std.env.agent_id(), std.env.project_root() — agent-block specific

std.path.* / std.time.* (mlua-batteries)

• std.path.join(...), std.path.basename(path), std.path.dirname(path)
• std.time.now(), std.time.sleep(secs), std.time.measure(fn)

std.kv.* (mlua-batteries, SQLite-backed)

• std.kv.get(ns, key) — retrieve a value by namespace + key; returns nil if absent
• std.kv.set(ns, key, value) — store a value (any Lua value, JSON-encoded internally)
• std.kv.delete(ns, key) — delete a key; returns true if it existed, false otherwise
• std.kv.list(ns, prefix?) — list keys in a namespace, optionally filtered by prefix
• std.kv.register_tools() — register kv_get, kv_set, kv_delete, kv_list as LLM-callable tools

Storage: AGENT_BLOCK_HOME/kv.sqlite (override via AGENT_BLOCK_KV_PATH; :memory: supported).

std.sql.* (mlua-batteries, SQLite-backed)

• std.sql.execute(sql, params?) — execute a DML statement; returns { affected = N }
• std.sql.query(sql, params?) — execute a query; returns an array of row tables
• std.sql.register_tools() — register sql_execute, sql_query as LLM-callable tools

Storage: AGENT_BLOCK_HOME/sql.sqlite (override via AGENT_BLOCK_SQL_PATH; :memory: supported).

std.ts.* (agent-block, SQLite-backed TSDB)

• std.ts.append(series, value, tags?, at?) — append a data point; value is a Lua number or table (JSON-encoded, losslessly decoded on read); tags is an optional {key=value} table; at is an optional Unix timestamp in milliseconds (default: now)
• std.ts.query(series, opts) — range query; opts fields:
  • from, to (integer ms) — time range (default: full range)
  • tags (table) — AND-filter; each key-value pair uses SQLite json_extract
  • agg (string) — "count" | "sum" | "avg" | "last" (optional)
  • bucket_ms (integer) — bucket width; requires agg; produces time-bucketed rows
  • limit, offset (integer) — pagination
• std.ts.last(series, tags?) — most-recent data point; same tag AND-filter as query
• std.ts.register_tools() — register ts_append, ts_query, ts_last as LLM-callable tools

Ordering guarantee: raw-path results (query without agg) are ordered by (ts ASC, rowid ASC); last and query with agg="last" resolve same-millisecond ties by (ts DESC, rowid DESC) so the last-appended row always wins. This is a deterministic SQLite rowid tie-breaker — no DDL or index change is required.

Storage: AGENT_BLOCK_HOME/ts.sqlite (override via AGENT_BLOCK_TS_PATH; :memory: supported).

agent (StdPkg — require("agent"))

Built-in ReAct loop module. Available without any path configuration after cargo install.

local agent = require("agent")

local result = agent.run({
    prompt  = "List files in the current directory and summarise them.",
    system  = "You are a helpful assistant.",           -- optional
    model   = "claude-haiku-4-5-20251001",             -- optional, env ANTHROPIC_MODEL as fallback
    max_tokens       = 4096,                            -- per-request token limit
    max_iterations   = 20,                              -- loop iteration cap
    max_tokens_budget = 50000,                          -- total token budget (nil = unlimited)
    timeout          = 120,                             -- HTTP timeout in seconds
    mcp_servers = {                                     -- optional MCP servers to connect
        { name = "outline", command = "outline-mcp", args = {} },
        -- HTTP/SSE form: use `url` instead of `command`
        { name = "remote", url = "https://example.com/mcp",
          transport_opts = { transport = "sse" } },     -- transport = "sse" | "http" (default "http")
    },
    sampling = function(params) ... end,                -- optional: called for sampling/createMessage
                                                        -- from every connected MCP server
    -- Anthropic server-side context editing (default ON). Pass `false` to opt out,
    -- or pass a full override table (replaces the default entirely).
    context_management        = true,                   -- default true; false disables beta header + body
    context_management_config = {                       -- default: trigger 80K, keep 3, clear_at_least 10K
        edits = {
            {
                type           = "clear_tool_uses_20250919",
                trigger        = { type = "input_tokens", value = 80000 },
                keep           = { type = "tool_uses",    value = 3 },
                clear_at_least = { type = "input_tokens", value = 10000 },
            },
        },
    },
    on_turn = function(info)                            -- optional per-turn callback
        print("turn", info.turn_number, "#tools", #info.tool_calls)
        -- info.context_management is present only on turns where the server fired
        -- an edit; nil-guard before indexing applied_edits.
        if info.context_management and info.context_management.applied_edits then
            for _, edit in ipairs(info.context_management.applied_edits) do
                print("  edit:", edit.type, "cleared", edit.cleared_tool_uses, "tool_uses")
            end
        end
    end,
    extra_tools = {},                                   -- optional extra Anthropic tool defs
})

if result.ok then
    print(result.content)
else
    print("error:", result.error)
end
-- result fields: ok, content, usage{input_tokens,output_tokens,total_tokens}, num_turns, error, messages

Provider Switching

By default agent.run uses the Anthropic Messages API. Pass provider = "openai" to route to any OpenAI-compatible endpoint (vLLM, llama.cpp, OpenRouter, RunPod, etc.):

-- Anthropic (default) — requires ANTHROPIC_API_KEY
local result = agent.run({ prompt = "Hello", model = "claude-haiku-4-5-20251001" })

-- OpenAI — requires OPENAI_API_KEY (or opts.api_key)
local result = agent.run({
    prompt  = "Hello",
    provider = "openai",
    model   = "gpt-4o-mini",
})

-- Local vLLM / llama.cpp / RunPod — custom base_url
local result = agent.run({
    prompt   = "Hello",
    provider = "openai",
    base_url = "http://localhost:8080/v1",
    model    = "Qwen/Qwen3-0.6B",
    api_key  = "token-abc123",           -- or api_key_env = "MY_KEY"
})

Environment variables used per provider:

provider	default key env	override via
anthropic	ANTHROPIC_API_KEY	opts.api_key / opts.api_key_env
openai	OPENAI_API_KEY	opts.api_key / opts.api_key_env

opts.base_url overrides the endpoint root. Default for openai is https://api.openai.com/v1.

cache_control, context_management, and context_management_config are Anthropic-only: they are operative when provider="anthropic" (or unset) and emit a warn-level log message then are ignored when provider="openai".

Key behaviours:

• MCP servers listed in mcp_servers are connected automatically and disconnected on exit (even on error).
• Each entry may use the stdio form { name, command, args } or the HTTP form { name, url, transport_opts }. Both forms can coexist in the same list.
• Pass sampling = fn in agent.run opts to register a single Lua function as the sampling/createMessage handler for every connected MCP server (mcp.set_sampling_handler is called per server automatically).
• Pass enable_resources = true in agent.run opts to automatically register {server}__mcp_list_resources and {server}__mcp_read_resource as LLM-callable tools for each connected server that declares the resources capability. Default false. If a server does not declare resources, the opt-in is silently skipped (logged at info).
• Pass enable_prompts = true in agent.run opts to automatically register {server}__mcp_list_prompts and {server}__mcp_get_prompt as LLM-callable tools for each connected server that declares the prompts capability. Default false. Capability check and silent skip apply the same way as enable_resources.
• Pass on_progress = fn(ev) in agent.run opts to receive progress notifications from all connected MCP servers. The callback is called with an envelope table { type="progress", server, token, progress, total, message }. No capability gate — all servers are registered. User callback errors are swallowed and logged at warn.
• Pass progress_to_log = true in agent.run opts to bridge progress notifications to log.info automatically. Ignored when on_progress is also set (callback takes priority). Default false.
• Pass on_log = fn(ev) in agent.run opts to receive log notifications from servers that declare the logging capability. The callback is called with an envelope table { type="log", server, level, logger, data }. Servers without logging capability are silently skipped (logged at info). User callback errors are swallowed and logged at warn.
• Pass log_to_stderr = true in agent.run opts to bridge server log notifications to log.debug|info|warn|error automatically. Ignored when on_log is also set (callback takes priority). Logging capability gate applies the same way as on_log. Default false.
• MCP tool names are namespaced as server_name__tool_name to avoid collisions.
• MCP tools are automatically assigned to a group for use with tool_groups. Group resolution follows this priority: (1) the tool's _meta.group field (string, non-empty) declared by the server takes precedence — rmcp serialises Tool.meta as _meta via #[serde(rename = "_meta")]; (2) fallback to the server name. Pass tool_groups = { "outline" } (for example) to agent.run to include only tools from that MCP server. This aligns with the MCP SEP-986 tool-name prefix grouping guidance and the mcp__<server>__* convention used by Claude Code. Tools without an explicit group (e.g. plain registered Lua tools) fall into the "default" group.
• Tool dispatch: MCP tools via mcp.call(), registered Lua tools via tool.call().
• Never throws — all errors returned as { ok=false, error="..." }.
• Context editing is on by default: once the conversation crosses ~80K input tokens, Anthropic evicts all but the most recent 3 tool-use / tool-result pairs server-side so the loop can keep running. Works on Sonnet 4 / Sonnet 4.5 / Haiku 4.5 / Opus 4 / 4.1 / 4.5. Pass context_management = false to disable, or context_management_config = { edits = { ... } } to replace the default entirely (the whole table is forwarded as body.context_management; no partial merge).
• on_turn(info) gains an additive info.context_management field that forwards the raw response.context_management from Anthropic ({ applied_edits = { { type, cleared_tool_uses, cleared_input_tokens }, ... } }). The field is absent on turns where the server did not fire any edit — nil-guard before indexing.
• The blocks/ directory is embedded in the binary; place a local blocks/agent/init.lua in the project root to override.
• LLM dump logging is safe-by-default and ENV-driven:
  • AGENT_BLOCK_LLM_DUMP=off|meta|full (default off)
  • when unset, RUST_LOG containing debug or trace enables meta
  • full is downgraded to meta when AGENT_BLOCK_ENV=prod|production unless AGENT_BLOCK_LLM_DUMP_ALLOW_PROD=true
  • request auth headers (x-api-key / authorization) are always redacted in dump logs
  • log lines use fixed-order key=value format with a unique marker (prefix=ab.obs component=llm); legacy prefix=ab.llm lines are also emitted for compatibility
  • meta includes call correlation and runtime signals (call, turn, iter, latency_ms, stop_reason, tool_uses, token usage, context edit count)
  • optional agent.run({ log_meta = { trace_id, agent_id, agent_name, run_id } }) appends external context to dump lines (same keys can also come from AGENT_BLOCK_TRACE_ID, AGENT_BLOCK_AGENT_ID, AGENT_BLOCK_AGENT_NAME, AGENT_BLOCK_RUN_ID)

compile_loop (Filesystem block — require("compile_loop"))

Tool factory for the autonomous compile-and-fix loop. The primary surface is compile_loop.make(conf), which returns a tool_def consumable directly by agent.run.

Place blocks/compile_loop/init.lua in the project root (resolved via the filesystem blocks/ path; no EMBEDDED_BLOCKS entry is required).

local compile_loop = require("compile_loop")
local agent        = require("agent")

-- Define a caller-supplied runner function
local function lua_runner(file_path)
    local p = io.popen("lua " .. file_path .. ' 2>&1; echo "__EXIT__=$?"', "r")
    if not p then return { ok = false, stdout = "", stderr = "popen failed", exit_code = -1 } end
    local out = p:read("*a") or ""
    p:close()
    local exit_code = tonumber(out:match("__EXIT__=(%d+)%s*$") or "1")
    out = out:gsub("__EXIT__=%d+%s*$", "")
    local pass = exit_code == 0 and out:find("ALL_PASS", 1, true) ~= nil
    return { ok = pass, stdout = out, stderr = "", exit_code = exit_code }
end

-- Build a tool_def and pass it to the parent agent
local td = compile_loop.make({
    runner    = lua_runner,       -- required: function(path) → {ok, stdout, stderr, exit_code}
    max_iters = 5,                -- optional, default 5
    lang      = "lua",            -- optional, default "lua"
    -- conf.llm is optional: when omitted the parent agent's provider/model/api_key
    -- are inherited at call time via _AGENT_LLM_CTX (Crux #2).
    llm = {
        provider = "anthropic",
        model    = "claude-haiku-4-5-20251001",
        -- api_key / api_key_env / base_url / max_tokens / temperature / timeout
    },
})

local result = agent.run({
    prompt      = "Write a Lua function that returns the nth Fibonacci number.",
    model       = "claude-haiku-4-5-20251001",
    extra_tools = { td },         -- tool_def passed directly; no caller-side adaptation
})

compile_loop.make(conf) returns { name, schema, handler }. As a side-effect tool.register(name, schema, handler) is called, so the registry and tool_def.handler share the same function identity. The tool name defaults to "compile_loop"; pass conf.name to override (useful when registering multiple instances).

Multi-file mode: pass target_files = {pathA, pathB, ...} together with edit_mode = "diff" to edit several files in a single loop. The runner signature changes to function(paths) (list). Multi-file lazy-load (the read_file tool dispatch loop, sliding window K=3, stderr trim) works on both the "anthropic" and "openai" provider paths. See blocks/compile_loop/README.md §"Multi-file mode" and the examples/test_anthropic_compile_loop_multi*.lua / examples/test_openai_compile_loop_multi_lazy_load.lua smoke scripts.

Read-and-distill for large files: in multi-file lazy-load mode, read_file now inspects file size before returning content. Files at or below READ_FILE_FULL_THRESHOLD (default 10 000 chars) are returned verbatim as before. Files that exceed the threshold are split into line-based chunks and summarised by the child LLM (provider-agnostic, same call path as the outer loop), and the tool returns a digest string plus a line-index ("L1-50: ...\nL51-180: ..."). The digest cache (mf_state.file_digest[path]) survives per-iteration resets; only file-mtime changes or file_digest_refresh = "always" trigger re-distillation.

read_file_range tool: after receiving a digest the LLM can call read_file_range(path, line_start, line_end) to retrieve the verbatim lines from that range. The handler reads directly from disk without passing through distillation, regardless of file size. Guards: target_files allowlist, 1-indexed inclusive range, max READ_FILE_RANGE_MAX_LINES lines (default 500) per call.

New optional conf fields for large-file distillation:

field	type	default	description
conf.target_func	string | nil	nil	Function name to prioritise in chunk ordering. Chunks containing this name are ranked second (after last_err-overlap chunks). Existing callers that omit this field are unaffected.
conf.distill_threshold	number | nil	10 000	Override READ_FILE_FULL_THRESHOLD per-instance.
conf.distill_chunk_lines	number | nil	200	Lines per distill chunk.
conf.distill_max_tokens	number | nil	4 000	Max chars for the packed digest returned to the LLM.

Tool input (supplied by the LLM at call time): spec (string, required), target_file (absolute path, required), lang (string, optional).

edit_mode (opt-in diff mode): pass edit_mode = "diff" to compile_loop.make to switch the child LLM to Aider-style SEARCH/REPLACE patch output instead of emitting the whole file on every iteration. This is the preferred mode for large existing files where minimal-edit is critical (e.g. fixing a single function in a 500-line file).

local td = compile_loop.make({
    runner    = lua_runner,
    edit_mode = "diff",        -- opt-in; default is "full"
    llm       = { provider = "anthropic", model = "claude-haiku-4-5-20251001" },
})

The child LLM must output one or more SEARCH/REPLACE blocks in this exact format:

<<<<<<< SEARCH
<existing text to replace, character-exact>
=======
<replacement text>
>>>>>>> REPLACE

compile_loop applies each block in order using a two-stage match (exact → whitespace- normalized). Blocks whose SEARCH text does not match the current file content are reported back to the child LLM with the full file content attached, triggering a retry.

When target_file is absent or empty at loop entry, edit_mode = "diff" automatically falls back to "full" with a warn-level log line (diff requires a base file to patch).

target_file dual role: when target_file already exists at loop entry, its content is embedded in the initial user message as === Current file content === so the child LLM can build on it rather than generating from scratch. In full mode the file is overwritten on every iteration; in diff mode only the matched regions are replaced. When the file is absent or empty, the message contains spec only — preserving the original synthesis behaviour (backward-compatible).

Target model class: the full-file output strategy is designed for Qwen3 / Haiku-grade mid-weight models. Emitting the whole file on each iteration avoids the apply-failure cost of diff/Edit-tool workflows and keeps the feedback loop simple and fast. For the latest Sonnet/Opus with native edit-tool support, a diff-based block is a future consideration (separate issue; out of scope here).

Tool output JSON (never contains code or history — Counter WF-A defence):

{ ok, iters, summary, failure_reason?, last_error?, artifact_path }

failure_reason values: "llm_call" | "open_target_file" | "stagnation" | "max_iters".

LLM inheritance (Crux #2): when conf.llm is omitted (or individual fields are absent), compile_loop resolves provider, base_url, api_key, api_key_env, and model from the parent agent.run call context at tool-dispatch time. No hardcoded provider default; no error for missing credentials at make() time.

Stagnation detection: when 3 consecutive iterations produce identical runner stderr the loop gives up immediately, independent of the remaining iteration budget. failure_reason = "stagnation".

Observability (ab.obs events): compile_loop emits structured ab.obs log events on each iteration, gated by AGENT_BLOCK_LLM_DUMP (same env var as the agent block). Set AGENT_BLOCK_LLM_DUMP=meta to activate. Each line uses the key=value format with prefix=ab.obs component=compile_loop.

event	when emitted	fields
iter_start	start of each iteration	iter, target_file
iter_result	after runner executes	iter, ok, exit_code, stderr_len
converged	before PASS return	iters
stagnation	before stagnation give-up	iters
max_iters_reached	before max_iters give-up	iters

Provider support: "anthropic" and "openai"-compatible endpoints (vLLM, llama.cpp, OpenRouter, RunPod, etc.) are both fully implemented in conf.llm.

conf.llm.provider	Default key env	Override via
"anthropic"	ANTHROPIC_API_KEY	conf.llm.api_key / api_key_env
"openai"	OPENAI_API_KEY	conf.llm.api_key / api_key_env

External runner examples

Example	Runner	Provider
examples/test_anthropic_compile_loop.lua	inline lua	Anthropic
examples/test_qwen_compile_loop.lua	inline lua	Qwen (OpenAI-compat)
examples/test_qwen_compile_loop_rust.lua	inline cargo	Qwen (OpenAI-compat)
examples/test_qwen_compile_loop_lust.lua	mlua-probe MCP	Qwen (OpenAI-compat)
examples/test_compile_loop_parent.lua	inline lua	Anthropic parent + Qwen child
examples/test_anthropic_compile_loop_pytest.lua	inline pytest	Anthropic
examples/test_anthropic_compile_loop_multi_lazy_load.lua	inline lua (multi-file)	Anthropic
examples/test_openai_compile_loop_multi_lazy_load.lua	inline lua (multi-file)	Qwen (OpenAI-compat)
tests/fixtures/compile_loop_distill_mock.lua	shared e2e fixture (distill, multi-file)	Anthropic / OpenAI-compat
tests/fixtures/compile_loop_distill_range_mock.lua	e2e fixture (read_file_range verbatim)	Anthropic

coding_agent (Filesystem block — require("coding_agent"), thin facade)

Backward-compatible facade over compile_loop. Prefer the compile_loop.make() API for new code. coding_agent is retained for existing callers.

Place blocks/coding_agent/init.lua in the project root.

coding_agent.run(opts) — run the loop directly from Lua (facade over compile_loop).

local coding = require("coding_agent")

local res = coding.run({
    provider    = "anthropic",                    -- "openai" | "anthropic"
    api_key     = "...",                          -- or api_key_env = "ANTHROPIC_API_KEY"
    model       = "claude-haiku-4-5-20251001",
    target_file = "/tmp/work/solution.lua",
    spec        = "Write a Lua function that returns the nth Fibonacci number.",
    lang        = "lua",                          -- code fence label (default "lua")
    max_iters   = 5,
    runner      = function(file_path)
        -- return { ok=bool, stdout, stderr, exit_code }
        local p = io.popen("lua " .. file_path .. " 2>&1; echo __EXIT__=$?", "r")
        local out = p:read("*a"); p:close()
        local ec = tonumber(out:match("__EXIT__=(%d+)") or "1")
        return { ok = ec == 0, stdout = out, stderr = "", exit_code = ec }
    end,
    on_iter = function(info) print("iter", info.iter, info.result.ok) end,
})

-- res fields:
--   ok             boolean
--   artifact_path  string      absolute path of the target file
--   iters          int
--   summary        string      "PASS in N iters" or "give-up: <reason>"
--   failure_reason string?     "llm_call"|"open_target_file"|"stagnation"|"max_iters"
--   last_error     string?     last runner stderr (trimmed to 800 chars) on failure
--
-- NOTE: "code" and "history" fields are no longer returned (removed in this release).

coding_agent.register_tool(opts) — register the compile_loop tool with the host tool registry so a parent LLM can invoke it via tool.call. Returns the registered tool name.

local coding = require("coding_agent")

-- Register once (typically at agent startup)
coding.register_tool({
    provider    = "openai",
    base_url    = "http://localhost:8080/v1",
    api_key     = "...",
    model       = "Qwen/Qwen2.5-Coder-7B",
    runner_kind = "lua",    -- "lua" | "cargo" | runner function
    max_iters   = 5,
    lang        = "lua",
})

-- The parent LLM can now call the "compile_loop" tool with:
--   { spec = "...", target_file = "/abs/path/to/file.lua", lang = "lua" }
-- The tool response JSON contains: ok, artifact_path, iters, summary,
--   failure_reason?, last_error?   (code and history are excluded).

Built-in runner_kind values (resolved in the coding_agent facade; compile_loop itself accepts only a runner function):

runner_kind	Behaviour
"lua"	Runs lua <file> and passes on exit 0 + ALL_PASS in stdout
"cargo"	Runs cargo test --offline in the file's directory; passes on "test result: ok"
function	Called as runner(file_path) — must return { ok, stdout, stderr, exit_code }

lshape (Vendored package — require("lshape"))

lshape is vendored under blocks/lshape/ so scripts can use schema validation and LuaCATS generation without external installation.

local lshape = require("lshape")
local T = lshape.t
local User = T.shape({ name = T.string, age = T.number })
local ok, why = lshape.check.check({ name = "Ada", age = 36 }, User)
assert(ok, why)

log.*

• log.info/warn/error/debug(msg)

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.